diff --git a/latest/.buildinfo b/latest/.buildinfo index 7eaa80657f..4d83aad3b8 100644 --- a/latest/.buildinfo +++ b/latest/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: cb3cbe8a473ef8fd1cf27e6890eb63f4 +config: ee79abf721be5d1b28815a3912832a13 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/latest/_cpp_gen/executor.html b/latest/_cpp_gen/executor.html index 5dbf38d7a5..07cfa3ce9e 100644 --- a/latest/_cpp_gen/executor.html +++ b/latest/_cpp_gen/executor.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -348,6 +348,7 @@
  • Generate text with guided decoding
  • Control generated text using logits processor
  • Generate text with multiple LoRA adapters
    • +
  • Speculative Decoding
  • Run LLM-API with pytorch backend on Slurm
  • Run trtllm-bench with pytorch backend on Slurm
  • Run trtllm-serve with pytorch backend on Slurm
    • @@ -408,7 +409,7 @@
  • KV Cache Management: Pools, Blocks, and Events
  • KV cache reuse
  • Speculative Sampling
    • -
  • Disaggregated-Service (experimental)
    • +
  • Disaggregated-Service (Experimental)

    • Performance

    Performance

    Performance

      @@ -708,7 +709,10 @@ Running the shared and routed experts in 2 streams combined with other multi-str

      These optimizations target the overall execution flow, scheduling, and resource management within the inference system. They are shared between DeepSeek R1 models and other models supported in the TensorRT-LLM, here we are sharing some ablation study for the performance benefits on DeepSeek R1 on B200.

      • CUDA Graph

        -

        This had a significant 22% E2E performance impact for throughput scenarios. CUDA Graphs allow capturing a sequence of CUDA operations and launching them as a single unit, drastically reducing kernel launch overheads. This is particularly beneficial for models with many small kernels, and particularly on the PyTorch flow, because the python host code normally executes slower than C++. Since the CUDA Graph freezes the kernel launch parameters, which is normally associated with the tensor shapes, it can only be safely used with static shape, meaning that different CUDA graphs need to be captured for different batch sizes. Each graph will have some cost of memory usage, and capturing time, thus we cannot capture every possible CUDA graph for all possible batches. For the non-captured batch sizes, PyTorch eager mode code will be executed. There is a feature called CUDA Graph padding in TensorRT-LLM, which is a good trade-off between the number of CUDA Graphs and the CUDA Graph hit ratio; it tries to pad a batch to the nearest one with a captured CUDA Graph. Normally you should enable the CUDA Graph padding feature to increase the CUDA Graph hit rate, but the padding itself has some overhead due to wasted tokens computation. Users can opt-out the CUDA Graph padding feature to see the perf benefits, by setting the cuda_graph_padding_enabled to false, see API here Pytorch backend config

        +

        This had a significant 22% E2E performance impact for throughput scenarios.

        +

        CUDA Graphs allow capturing a sequence of CUDA operations and launching them as a single unit, drastically reducing kernel launch overheads. This is particularly beneficial for models with many small kernels, and particularly on the PyTorch flow, because the python host code normally executes slower than C++. Since the CUDA Graph freezes the kernel launch parameters, which is normally associated with the tensor shapes, it can only be safely used with static shape, meaning that different CUDA graphs need to be captured for different batch sizes. Each graph will have some cost of memory usage, and capturing time, thus we cannot capture every possible CUDA graph for all possible batches. For the non-captured batch sizes, PyTorch eager mode code will be executed.

        +

        There is a feature called CUDA Graph padding in TensorRT-LLM, which is a good trade-off between the number of CUDA Graphs and the CUDA Graph hit ratio; it tries to pad a batch to the nearest one with a captured CUDA Graph. Normally you should enable the CUDA Graph padding feature to increase the CUDA Graph hit rate, but the padding itself has some overhead due to wasted tokens computation.

        +

        Users can opt-out the CUDA Graph padding feature to see the perf benefits, by setting the cuda_graph_config:\n  padding_enabled: False, see API here Pytorch backend config

      • Overlap Scheduler:

        Showed a 4% E2E performance impact and should generally always be used. This scheduler manages the execution of different operations (like computation and communication) to overlap them effectively on the GPU and network. The intuition is to hide latency by performing computation while waiting for data transfers or vice versa, improving overall hardware utilization. The overlap schedule is already defaulted on in TensorRT-LLM by commit. In case there are corner cases where it does not work, users can still opt-out this feature by set disable_overlap_scheduler to true.

        @@ -876,9 +880,9 @@ Running the shared and routed experts in 2 streams combined with other multi-str
        -

        Last updated on July 06, 2025.

        +

        Last updated on July 14, 2025.

        -

        This page is generated by TensorRT-LLM commit 66f299a.

        +

        This page is generated by TensorRT-LLM commit cfcb97a.

        diff --git a/latest/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html b/latest/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html index 7a53403269..5f596cf09a 100644 --- a/latest/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html +++ b/latest/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html @@ -58,7 +58,7 @@ @@ -68,7 +68,7 @@ - + @@ -346,6 +346,7 @@
      • Generate text with guided decoding
      • Control generated text using logits processor
      • Generate text with multiple LoRA adapters
        • +
      • Speculative Decoding
      • Run LLM-API with pytorch backend on Slurm
      • Run trtllm-bench with pytorch backend on Slurm
      • Run trtllm-serve with pytorch backend on Slurm
        • @@ -406,7 +407,7 @@
      • KV Cache Management: Pools, Blocks, and Events
      • KV cache reuse
      • Speculative Sampling
        • -
      • Disaggregated-Service (experimental)
        • +
      • Disaggregated-Service (Experimental)

      Performance

      Performance

      +

    • Qwen 3

      + +

    • Reproducing Steps

    @@ -741,6 +747,19 @@ $\frac{\text{Total Output Tokens/sec}}{\left(\frac{\text{NumCtxGPUs} \times \tex

    For Pareto curves with MTP = 1, 2, 3, it can be observed that disaggregated results show a 1.7x improvement over aggregated results at 50 tokens/sec/user (20 ms latency). Enabling MTP provides a larger speedup at higher concurrencies.

    +
    +

    Qwen 3#

    +
    +

    ISL 8192 - OSL 1024 (Machine Translation Dataset)#

    +
    +
    + Qwen 3 Pareto curves +
    +
    +

    Figure 15. Qwen 3 Pareto curves.

    +

    We also conducted performance evaluations of Qwen 3 on GB200 GPUs. The data indicate that the speedups achieved by disaggregation over aggregation range from 1.7x to 6.11x.

    +
    +

    Reproducing Steps#

    We provide a set of scripts to reproduce the performance data presented in this paper. Please refer to the usage instructions described in this document.

    @@ -814,6 +833,10 @@ $\frac{\text{Total Output Tokens/sec}}{\left(\frac{\text{NumCtxGPUs} \times \tex
  • ISL 4096 - OSL 1024 (Machine Translation Dataset)
    • +
  • Qwen 3 +
  • Reproducing Steps
    • @@ -913,9 +936,9 @@ $\frac{\text{Total Output Tokens/sec}}{\left(\frac{\text{NumCtxGPUs} \times \tex
    -

    Last updated on July 06, 2025.

    +

    Last updated on July 14, 2025.

    -

    This page is generated by TensorRT-LLM commit 66f299a.

    +

    This page is generated by TensorRT-LLM commit cfcb97a.

    diff --git a/latest/blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html b/latest/blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html new file mode 100644 index 0000000000..65ca216c7d --- /dev/null +++ b/latest/blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html @@ -0,0 +1,797 @@ + + + + + + + + + + + + How to launch Llama4 Maverick + Eagle3 TensorRT-LLM server — TensorRT-LLM + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + +
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    + +
    +

    How to launch Llama4 Maverick + Eagle3 TensorRT-LLM server#

    +

    Artificial Analysis has benchmarked the Llama4 Maverick with Eagle3 enabled TensorRT-LLM server running at over 1000 tokens per second per user on 8xB200 GPUs. This implementation leverages NVIDIA’s TensorRT-LLM combined with speculative decoding using the Eagle3 model to further boost performance.

    +

    In the guide below, we will walk you through how to launch your own high-performance Llama4 Maverick with Eagle3 enabled TensorRT-LLM server, from build to deployment. (Note that your specific performance numbers may vary—speculative decoding speedups depend upon the dataset!)

    +
    +

    Prerequisites#

    +
      +

    • 8x NVIDIA B200 GPUs in a single node (we have a forthcoming guide for getting great performance on H100)

      • +

    • CUDA Toolkit 12.8 or later

      • +

    • Docker with NVIDIA Container Toolkit installed

      • +

    • Fast SSD storage for model weights

      • +

    • Access to Llama4 Maverick and Eagle3 model checkpoints

      • +

    • A love of speed

      • +
    +
    +
    +

    Download Artifacts#

    + +

    In Step 4: Start the TensorRT-LLM server, /path/to/maverick and /path/to/eagle refer to the download paths of the above respective models.

    +
    +
    +

    Launching the server#

    +
    +

    Step 1: Clone the repository#

    +
    git clone https://github.com/NVIDIA/TensorRT-LLM.git
+cd TensorRT-LLM
+git submodule update --init --recursive
+git lfs pull
+
    +
    +

    The last command, git lfs pull, ensures all large files stored with Git LFS are properly downloaded. If git lfs is not installed, please install following Install Git LFS

    +
    +
    +

    Step 2: Prepare the TensorRT-LLM release Docker image#

    +
    +

    Option 1. Use weekly release NGC docker image#

    +

    TensorRT-LLM provides weekly release docker image

    +
    +
    +

    Option 2. Build TensorRT-LLM Docker image (Alternative way)#

    +

    If you want to compile a specific TensorRT-LLM commit, you can build the docker image by checking out the specific branch or commit and running a make command. This may take 15-30 minutes depending on your system.

    +
    make -C docker release_build
+
    +
    +
    +
    +
    +

    Step 3: (Optional) Tag and push the Docker image to your registry#

    +

    If you want to use this image on multiple machines or in a cluster:

    +
    docker tag tensorrt_llm/release:latest docker.io/<username>/tensorrt_llm:main
+docker push docker.io/<username>/tensorrt_llm:main
+
    +
    +

    Replace <username> with your Docker Hub username or your private registry path.

    +
    +
    +

    Step 4: Start the TensorRT-LLM server#

    +

    This command launches the server with Llama4 Maverick as the main model and Eagle3 as the draft model for speculative decoding. Make sure you have downloaded both model checkpoints before running this command.

    +

    Important: Replace /path/to/maverick and /path/to/eagle with the actual paths to your Maverick and Eagle3 model checkpoints on your host machine, downloaded in the Download Artifacts stage

    +
    docker run -d --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
+    -p 8000:8000 --gpus=all -e "TRTLLM_ENABLE_PDL=1" \
+    -v /path/to/maverick:/config/models/maverick -v /path/to/eagle:/config/models/eagle \
+    docker.io/<username>/tensorrt_llm:main sh \
+        -c "echo -e 'enable_attention_dp: false\nenable_min_latency: true\nenable_autotuner: false\ncuda_graph_config:\n  max_batch_size: 8\nspeculative_config:\n  decoding_type: Eagle\n  max_draft_len: 3\n  speculative_model_dir: /config/models/eagle\nkv_cache_config:\n  enable_block_reuse: false' > c.yaml && \
+        TRT_LLM_DISABLE_LOAD_WEIGHTS_IN_PARALLEL=True \
+        trtllm-serve /config/models/maverick \
+            --host 0.0.0.0 --port 8000 \
+            --backend pytorch --tp_size 8 --ep_size 1 \
+            --trust_remote_code --extra_llm_api_options c.yaml \
+            --kv_cache_free_gpu_memory_fraction 0.75"
+
    +
    +

    This command:

    +
      +

    • Runs the container in detached mode (-d)

      • +

    • Sets up shared memory and stack limits for optimal performance

      • +

    • Maps port 8000 from the container to your host

      • +

    • Enables all GPUs with tensor parallelism across all 8 GPUs

      • +

    • Creates a configuration file for speculative decoding with Eagle3

      • +

    • Configures memory settings for optimal throughput

      • +
    +

    After running this command, the server will initialize, which may take several minutes as it loads and optimizes the models.

    +

    You can query the health/readiness of the server using

    +
    curl -s -o /dev/null -w "%{http_code}" "http://localhost:8000/health"
+
    +
    +

    When the 200 code is returned the server is ready for queries. Note that the very first query may take longer due to initialization and compilation.

    +
    +
    +

    Step 5: Test the server with a sample request#

    +

    Once the server is running, you can test it with a simple curl request:

    +
    curl localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+        "model": "Llama4-eagle",
+        "messages": [{"role": "user", "content": "Why is NVIDIA a great company?"}],
+        "max_tokens": 1024
+    }' -w "\n"
+
+# {"id":"chatcmpl-e752184d1181494c940579c007ab2c5f","object":"chat.completion","created":1748018634,"model":"Llama4-eagle","choices":[{"index":0,"message":{"role":"assistant","content":"NVIDIA is considered a great company for several reasons:\n\n1. **Innovative Technology**: NVIDIA is a leader in the development of graphics processing units (GPUs) and high-performance computing hardware. Their GPUs are used in a wide range of applications, from gaming and professional visualization to artificial intelligence (AI), deep learning, and autonomous vehicles.\n2. ...","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":17,"total_tokens":552,"completion_tokens":535}}
+
    +
    +

    The server exposes a standard OpenAI-compatible API endpoint that accepts JSON requests. You can adjust parameters like max_tokens, temperature, and others according to your needs.

    +
    +
    +

    Step 6: (Optional) Monitor server logs#

    +

    To view the logs of the running container:

    +
    docker ps # get the container id
+docker logs -f <container_id>
+
    +
    +

    This is useful for troubleshooting or monitoring performance statistics reported by the server.

    +
    +
    +

    Step 7: (Optional) Stop the server#

    +

    When you’re done with the server:

    +
    docker ps # get the container id
+docker kill <container_id>
+
    +
    +
    +
    +
    +

    Troubleshooting Tips#

    +
      +

    • If you encounter CUDA out-of-memory errors, try reducing max_batch_size or max_seq_len

      • +

    • Ensure your model checkpoints are compatible with the expected format

      • +

    • For performance issues, check GPU utilization with nvidia-smi while the server is running

      • +

    • If the container fails to start, verify that the NVIDIA Container Toolkit is properly installed

      • +

    • For connection issues, make sure port 8000 is not being used by another application

      • +
    +
    +
    +

    Performance Tuning#

    +

    The configuration provided is optimized for 8xB200 GPUs, but you can adjust several parameters for your specific workload:

    +
      +

    • max_batch_size: Controls how many requests can be batched together

      • +

    • max_draft_len: The number of tokens Eagle can speculate ahead

      • +

    • kv_cache_free_gpu_memory_fraction: Controls memory allocation for the KV cache

      • +
    +
    +
    + + +
    + + + + + +
    + +
    +
    +
    + +
    + + + + + + + + + + +
    +
    + +
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    +
    +
    + + + + + + + + \ No newline at end of file diff --git a/latest/commands/trtllm-build.html b/latest/commands/trtllm-build.html index 685c9dfd58..ab6974cdc8 100644 --- a/latest/commands/trtllm-build.html +++ b/latest/commands/trtllm-build.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -348,6 +348,7 @@
  • Generate text with guided decoding
  • Control generated text using logits processor
  • Generate text with multiple LoRA adapters
    • +
  • Speculative Decoding
  • Run LLM-API with pytorch backend on Slurm
  • Run trtllm-bench with pytorch backend on Slurm
  • Run trtllm-serve with pytorch backend on Slurm
    • @@ -408,7 +409,7 @@
  • KV Cache Management: Pools, Blocks, and Events
  • KV cache reuse
  • Speculative Sampling
    • -
  • Disaggregated-Service (experimental)
    • +
  • Disaggregated-Service (Experimental)

    • Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    @@ -675,9 +677,9 @@
    -

    Last updated on July 06, 2025.

    +

    Last updated on July 14, 2025.

    -

    This page is generated by TensorRT-LLM commit 66f299a.

    +

    This page is generated by TensorRT-LLM commit cfcb97a.

    diff --git a/latest/examples/llm_guided_decoding.html b/latest/examples/llm_guided_decoding.html index 0596d36160..0128ae1575 100644 --- a/latest/examples/llm_guided_decoding.html +++ b/latest/examples/llm_guided_decoding.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -344,6 +344,7 @@
  • Generate text with guided decoding
  • Control generated text using logits processor
  • Generate text with multiple LoRA adapters
    • +
  • Speculative Decoding
  • Run LLM-API with pytorch backend on Slurm
  • Run trtllm-bench with pytorch backend on Slurm
  • Run trtllm-serve with pytorch backend on Slurm
    • @@ -404,7 +405,7 @@
  • KV Cache Management: Pools, Blocks, and Events
  • KV cache reuse
  • Speculative Sampling
    • -
  • Disaggregated-Service (experimental)
    • +
  • Disaggregated-Service (Experimental)

    • Performance

    Performance

      @@ -508,28 +509,26 @@ 10 # Sample prompts. 11 prompts = [ 12 "Hello, my name is", -13 "The president of the United States is", -14 "The capital of France is", -15 "The future of AI is", -16 ] -17 -18 # Create a sampling params. -19 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -20 -21 for output in llm.generate(prompts, sampling_params): -22 print( -23 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" -24 ) -25 -26 # Got output like -27 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' -28 # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the' -29 # Prompt: 'The capital of France is', Generated text: 'Paris.' -30 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' -31 -32 -33if __name__ == '__main__': -34 main() +13 "The capital of France is", +14 "The future of AI is", +15 ] +16 +17 # Create a sampling params. +18 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +19 +20 for output in llm.generate(prompts, sampling_params): +21 print( +22 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" +23 ) +24 +25 # Got output like +26 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' +27 # Prompt: 'The capital of France is', Generated text: 'Paris.' +28 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' +29 +30 +31if __name__ == '__main__': +32 main() @@ -662,9 +661,9 @@
      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/examples/llm_inference_async.html b/latest/examples/llm_inference_async.html index 5d06d3f009..f6f1b697d0 100644 --- a/latest/examples/llm_inference_async.html +++ b/latest/examples/llm_inference_async.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -344,6 +344,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -404,7 +405,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

    Performance

      @@ -508,36 +509,34 @@ 10 # Sample prompts. 11 prompts = [ 12 "Hello, my name is", -13 "The president of the United States is", -14 "The capital of France is", -15 "The future of AI is", -16 ] -17 -18 # Create a sampling params. -19 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -20 -21 # Async based on Python coroutines -22 async def task(prompt: str): -23 output = await llm.generate_async(prompt, sampling_params) -24 print( -25 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" -26 ) -27 -28 async def main(): -29 tasks = [task(prompt) for prompt in prompts] -30 await asyncio.gather(*tasks) -31 -32 asyncio.run(main()) -33 -34 # Got output like follows: -35 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' -36 # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the' -37 # Prompt: 'The capital of France is', Generated text: 'Paris.' -38 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' -39 -40 -41if __name__ == '__main__': -42 main() +13 "The capital of France is", +14 "The future of AI is", +15 ] +16 +17 # Create a sampling params. +18 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +19 +20 # Async based on Python coroutines +21 async def task(prompt: str): +22 output = await llm.generate_async(prompt, sampling_params) +23 print( +24 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" +25 ) +26 +27 async def main(): +28 tasks = [task(prompt) for prompt in prompts] +29 await asyncio.gather(*tasks) +30 +31 asyncio.run(main()) +32 +33 # Got output like follows: +34 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' +35 # Prompt: 'The capital of France is', Generated text: 'Paris.' +36 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' +37 +38 +39if __name__ == '__main__': +40 main() @@ -670,9 +669,9 @@
      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/examples/llm_inference_async_streaming.html b/latest/examples/llm_inference_async_streaming.html index 33ff6aebb8..2554ef2664 100644 --- a/latest/examples/llm_inference_async_streaming.html +++ b/latest/examples/llm_inference_async_streaming.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -344,6 +344,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -404,7 +405,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

    Performance

      @@ -509,55 +510,54 @@ 11 # Sample prompts. 12 prompts = [ 13 "Hello, my name is", -14 "The president of the United States is", -15 "The capital of France is", -16 "The future of AI is", -17 ] -18 -19 # Create a sampling params. -20 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -21 -22 # Async based on Python coroutines -23 async def task(id: int, prompt: str): -24 -25 # streaming=True is used to enable streaming generation. -26 async for output in llm.generate_async(prompt, -27 sampling_params, -28 streaming=True): -29 print(f"Generation for prompt-{id}: {output.outputs[0].text!r}") -30 -31 async def main(): -32 tasks = [task(id, prompt) for id, prompt in enumerate(prompts)] -33 await asyncio.gather(*tasks) -34 -35 asyncio.run(main()) -36 -37 # Got output like follows: -38 # Generation for prompt-0: '\n' -39 # Generation for prompt-3: 'an' -40 # Generation for prompt-2: 'Paris' -41 # Generation for prompt-1: 'likely' -42 # Generation for prompt-0: '\n\n' -43 # Generation for prompt-3: 'an exc' -44 # Generation for prompt-2: 'Paris.' -45 # Generation for prompt-1: 'likely to' -46 # Generation for prompt-0: '\n\nJ' -47 # Generation for prompt-3: 'an exciting' -48 # Generation for prompt-2: 'Paris.' -49 # Generation for prompt-1: 'likely to nomin' -50 # Generation for prompt-0: '\n\nJane' -51 # Generation for prompt-3: 'an exciting time' -52 # Generation for prompt-1: 'likely to nominate' -53 # Generation for prompt-0: '\n\nJane Smith' -54 # Generation for prompt-3: 'an exciting time for' -55 # Generation for prompt-1: 'likely to nominate a' -56 # Generation for prompt-0: '\n\nJane Smith.' -57 # Generation for prompt-3: 'an exciting time for us' -58 # Generation for prompt-1: 'likely to nominate a new' +14 "The capital of France is", +15 "The future of AI is", +16 ] +17 +18 # Create a sampling params. +19 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +20 +21 # Async based on Python coroutines +22 async def task(id: int, prompt: str): +23 +24 # streaming=True is used to enable streaming generation. +25 async for output in llm.generate_async(prompt, +26 sampling_params, +27 streaming=True): +28 print(f"Generation for prompt-{id}: {output.outputs[0].text!r}") +29 +30 async def main(): +31 tasks = [task(id, prompt) for id, prompt in enumerate(prompts)] +32 await asyncio.gather(*tasks) +33 +34 asyncio.run(main()) +35 +36 # Got output like follows: +37 # Generation for prompt-0: '\n' +38 # Generation for prompt-3: 'an' +39 # Generation for prompt-2: 'Paris' +40 # Generation for prompt-1: 'likely' +41 # Generation for prompt-0: '\n\n' +42 # Generation for prompt-3: 'an exc' +43 # Generation for prompt-2: 'Paris.' +44 # Generation for prompt-1: 'likely to' +45 # Generation for prompt-0: '\n\nJ' +46 # Generation for prompt-3: 'an exciting' +47 # Generation for prompt-2: 'Paris.' +48 # Generation for prompt-1: 'likely to nomin' +49 # Generation for prompt-0: '\n\nJane' +50 # Generation for prompt-3: 'an exciting time' +51 # Generation for prompt-1: 'likely to nominate' +52 # Generation for prompt-0: '\n\nJane Smith' +53 # Generation for prompt-3: 'an exciting time for' +54 # Generation for prompt-1: 'likely to nominate a' +55 # Generation for prompt-0: '\n\nJane Smith.' +56 # Generation for prompt-3: 'an exciting time for us' +57 # Generation for prompt-1: 'likely to nominate a new' +58 59 -60 -61if __name__ == '__main__': -62 main() +60if __name__ == '__main__': +61 main() @@ -690,9 +690,9 @@
      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/examples/llm_inference_distributed.html b/latest/examples/llm_inference_distributed.html index 471ac80d2e..f5c8b7c7a9 100644 --- a/latest/examples/llm_inference_distributed.html +++ b/latest/examples/llm_inference_distributed.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -344,6 +344,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -404,7 +405,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

    Performance

      @@ -516,29 +517,27 @@ 18 # Sample prompts. 19 prompts = [ 20 "Hello, my name is", -21 "The president of the United States is", -22 "The capital of France is", -23 "The future of AI is", -24 ] -25 -26 # Create a sampling params. -27 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -28 -29 for output in llm.generate(prompts, sampling_params): -30 print( -31 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" -32 ) -33 -34 # Got output like -35 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' -36 # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the' -37 # Prompt: 'The capital of France is', Generated text: 'Paris.' -38 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' -39 -40 -41# The entry point of the program need to be protected for spawning processes. -42if __name__ == '__main__': -43 main() +21 "The capital of France is", +22 "The future of AI is", +23 ] +24 +25 # Create a sampling params. +26 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +27 +28 for output in llm.generate(prompts, sampling_params): +29 print( +30 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" +31 ) +32 +33 # Got output like +34 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' +35 # Prompt: 'The capital of France is', Generated text: 'Paris.' +36 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' +37 +38 +39# The entry point of the program need to be protected for spawning processes. +40if __name__ == '__main__': +41 main() @@ -671,9 +670,9 @@
      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/examples/llm_logits_processor.html b/latest/examples/llm_logits_processor.html index 2fb2e355b0..99f9f396f7 100644 --- a/latest/examples/llm_logits_processor.html +++ b/latest/examples/llm_logits_processor.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -344,6 +344,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -404,7 +405,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

    Performance

      @@ -842,8 +843,6 @@
  • auto_parallel_world_size (tensorrt_llm.llmapi.TrtLlmArgs attribute) -
    • -
  • autotuner_enabled (tensorrt_llm.llmapi.TorchLlmArgs attribute)
  • avg_pool2d() (in module tensorrt_llm.functional)
    • @@ -878,17 +877,19 @@
  • beam_width_array (tensorrt_llm.llmapi.SamplingParams attribute) +
    • +
  • BEGIN_THINKING_PHASE_TOKEN (tensorrt_llm.llmapi.MTPDecodingConfig attribute)
  • bert_attention() (in module tensorrt_llm.functional)
  • BertAttention (class in tensorrt_llm.layers.attention)
  • BertForQuestionAnswering (class in tensorrt_llm.models) -
    • -
  • BertForSequenceClassification (class in tensorrt_llm.models)
  • DeepseekForCausalLM (class in tensorrt_llm.models) @@ -1280,6 +1283,8 @@
  • draft_tokens (tensorrt_llm.llmapi.DisaggregatedParams attribute)
  • DRAFT_TOKENS_EXTERNAL (tensorrt_llm.models.SpeculativeDecodingMode attribute) +
    • +
  • drafter (tensorrt_llm.llmapi.UserProvidedDecodingConfig attribute)
  • DraftTargetDecodingConfig (class in tensorrt_llm.llmapi)
    • @@ -1348,6 +1353,8 @@
  • embedding_bias (tensorrt_llm.llmapi.SamplingParams attribute)
  • embedding_parallel_mode (tensorrt_llm.llmapi.TrtLlmArgs attribute) +
    • +
  • enable_autotuner (tensorrt_llm.llmapi.TorchLlmArgs attribute)
  • enable_batch_size_tuning (tensorrt_llm.llmapi.DynamicBatchConfig attribute)
    • @@ -1377,6 +1384,8 @@ +
  • END_THINKING_PHASE_TOKEN (tensorrt_llm.llmapi.MTPDecodingConfig attribute) +
  • engine (tensorrt_llm.runtime.Session property)
  • engine_inspector (tensorrt_llm.runtime.GenerationSession property) @@ -1681,6 +1692,8 @@
  • (tensorrt_llm.llmapi.NGramDecodingConfig class method)
  • (tensorrt_llm.llmapi.QuantConfig class method) +
    • +
  • (tensorrt_llm.llmapi.UserProvidedDecodingConfig class method)
  • (tensorrt_llm.models.PretrainedConfig class method)
    • @@ -1910,6 +1923,8 @@
  • get_config_group() (tensorrt_llm.models.PretrainedConfig method)
  • get_context_phase_params() (tensorrt_llm.llmapi.DisaggregatedParams method) +
    • +
  • get_draft_model_prompt() (tensorrt_llm.llmapi.EagleDecodingConfig method)
  • get_first_past_key_value() (tensorrt_llm.layers.attention.KeyValueCacheParams method)
    • @@ -2516,8 +2531,6 @@
  • Mish (class in tensorrt_llm.layers.activation)
  • MIXED_PRECISION (tensorrt_llm.llmapi.QuantAlgo attribute) -
    • -
  • mixed_sampler (tensorrt_llm.llmapi.TorchLlmArgs attribute)
  • MLLaMAForCausalLM (class in tensorrt_llm.models)
    • @@ -2570,6 +2583,8 @@
  • (tensorrt_llm.llmapi.TorchLlmArgs attribute)
  • (tensorrt_llm.llmapi.TrtLlmArgs attribute) +
    • +
  • (tensorrt_llm.llmapi.UserProvidedDecodingConfig attribute)
  • model_name (tensorrt_llm.runtime.ModelConfig attribute) @@ -2723,10 +2738,10 @@
  • num_beams (tensorrt_llm.runtime.SamplingConfig attribute)
    • - - +
  • num_nextn_predict_layers (tensorrt_llm.llmapi.MTPDecodingConfig attribute) +
    • +
  • num_nextn_predict_layers_from_model_config (tensorrt_llm.llmapi.MTPDecodingConfig attribute)
  • num_return_sequences (tensorrt_llm.runtime.SamplingConfig attribute)
    • @@ -2963,8 +2980,6 @@
  • (tensorrt_llm.llmapi.SamplingParams attribute)
    • -
  • prompt_lookup_num_tokens (tensorrt_llm.llmapi.NGramDecodingConfig attribute) -
  • prompt_token_ids (tensorrt_llm.llmapi.RequestOutput attribute)
  • PromptTuningEmbedding (class in tensorrt_llm.layers.embedding) @@ -2981,12 +2996,6 @@
  • python_e2e (tensorrt_llm.runtime.MultimodalModelRunner property)
    • -
  • pytorch_weights_path (tensorrt_llm.llmapi.DraftTargetDecodingConfig attribute) - -
    • @@ -3317,8 +3326,6 @@
  • skip_cross_attn_blocks (tensorrt_llm.runtime.ModelConfig attribute)
    • - - + @@ -8968,6 +8995,8 @@
  • update_from_dict() (tensorrt_llm.llmapi.BuildConfig method) +
    • +
  • update_from_model_config() (tensorrt_llm.llmapi.MTPDecodingConfig method)
  • update_kv_cache_type() (tensorrt_llm.llmapi.BuildConfig method)
    • @@ -8985,10 +9014,10 @@
  • use_gpt_attention_plugin (tensorrt_llm.runtime.GenerationSession property)
    • - - +
    • use_lora() (tensorrt_llm.models.DecoderModel method) @@ -9037,6 +9070,8 @@

      V

      + -

      Performance

      -
    • Disaggregated-Service (experimental)

      Performance

      Performance

      Performance

      Performance

      Performance

        @@ -497,31 +498,12 @@

        LLM API Introduction#

        The LLM API is a high-level Python API designed to streamline LLM inference workflows.

        -

        It supports a broad range of use cases, from single-GPU setups to multi-GPU and multi-node deployments, with built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA Dynamo and the Triton Inference Server.

        +

        It supports a broad range of use cases, from single-GPU setups to multi-GPU and multi-node deployments, with built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA Dynamo.

        While the LLM API simplifies inference workflows with a high-level interface, it is also designed with flexibility in mind. Under the hood, it uses a PyTorch-native and modular backend, making it easy to customize, extend, or experiment with the runtime.

        -
        -

        Supported Models#

        -
          -

        • DeepSeek variants

          • -

        • Llama (including variants Mistral, Mixtral, InternLM)

          • -

        • GPT (including variants Starcoder-1/2, Santacoder)

          • -

        • Gemma-1/2/3

          • -

        • Phi-1/2/3/4

          • -

        • ChatGLM (including variants glm-10b, chatglm, chatglm2, chatglm3, glm4)

          • -

        • QWen-1/1.5/2/3

          • -

        • Falcon

          • -

        • Baichuan-1/2

          • -

        • GPT-J

          • -

        • Mamba-1/2

          • -
        -
        -

        Note: For the most up-to-date list of supported models, you may refer to the TensorRT-LLM model definitions.

        -
        -

        Quick Start Example#

        A simple inference example with TinyLlama using the LLM API:

        -

        More examples can be found here.

        +

        For more advanced usage including distributed inference, multimodal, and speculative decoding, please refer to this README.

        Model Input#

        @@ -626,7 +608,6 @@
      +
      • +
    • UserProvidedDecodingConfig
    • TorchCompileConfig
    • LlmArgs
    • TorchLlmArgs
    • disable_overlap_scheduler
      • +
    • enable_autotuner
    • enable_iter_perf_stats
    • enable_iter_req_stats
    • enable_layerwise_nvtx_marker
    • enable_min_latency
      • +
    • enable_mixed_sampler
    • enable_trtllm_sampler
    • extra_resource_managers
    • force_dynamic_quantization
      • @@ -5227,7 +5357,6 @@ Whether to use a common pool for all requests, or the pool is private for each r
    • field_name
      • -
    • mixed_sampler
    • model_config
    • model_post_init()
    • moe_backend
      • @@ -5391,9 +5520,9 @@ Whether to use a common pool for all requests, or the pool is private for each r
      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/objects.inv b/latest/objects.inv index 12b96253cd..1468c2f94f 100644 Binary files a/latest/objects.inv and b/latest/objects.inv differ diff --git a/latest/overview.html b/latest/overview.html index 2caba71352..90fa00856d 100644 --- a/latest/overview.html +++ b/latest/overview.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -348,6 +348,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -408,7 +409,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

      • Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

        @@ -1215,10 +1216,10 @@ spec_decoding_generation_lengths: [bs] spec_decoding_position_offsets: [bs, max_gen_tokens] spec_decoding_packed_mask: [bs, max_draft_len, packed_length] ** eagle_temperature: [bs] -rand_data_validation: [bs, max_draft_tokens]

        +rand_data_validation: [bs, max_draft_len]

        ** The mask is tricky since the boolean mask will need to be
        -
        packed in runtime. So, the last dim will be:

        packed_length = ceil((max_draft_tokens+1)/32)

        +
        packed in runtime. So, the last dim will be:

        packed_length = ceil((max_draft_len+1)/32)

        @@ -3097,6 +3098,11 @@ ranges of the dimensions of when using TRT dynamic shapes.

        NONE = 1#
        +
        +
        +USER_PROVIDED = 128#
        +
        +
        static from_arguments(args: Namespace)[source]#
        @@ -3526,6 +3532,7 @@ ranges of the dimensions of when using TRT dynamic shapes.

      • MEDUSA
      • NGRAM
      • NONE
        • +
      • USER_PROVIDED
      • from_arguments()
      @@ -3629,9 +3636,9 @@ ranges of the dimensions of when using TRT dynamic shapes.

      -

      Last updated on July 06, 2025.

      +

      Last updated on July 14, 2025.

      -

      This page is generated by TensorRT-LLM commit 66f299a.

      +

      This page is generated by TensorRT-LLM commit cfcb97a.

      diff --git a/latest/python-api/tensorrt_llm.plugin.html b/latest/python-api/tensorrt_llm.plugin.html index ba906fb160..22e4aff957 100644 --- a/latest/python-api/tensorrt_llm.plugin.html +++ b/latest/python-api/tensorrt_llm.plugin.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -348,6 +348,7 @@
    • Generate text with guided decoding
    • Control generated text using logits processor
    • Generate text with multiple LoRA adapters
      • +
    • Speculative Decoding
    • Run LLM-API with pytorch backend on Slurm
    • Run trtllm-bench with pytorch backend on Slurm
    • Run trtllm-serve with pytorch backend on Slurm
      • @@ -408,7 +409,7 @@
    • KV Cache Management: Pools, Blocks, and Events
    • KV cache reuse
    • Speculative Sampling
      • -
    • Disaggregated-Service (experimental)
      • +
    • Disaggregated-Service (Experimental)

      • Performance

      Performance

      Performance

      Performance

        @@ -519,12 +520,12 @@ 6 7 prompts = [ 8 "Hello, my name is", - 9 "The president of the United States is", -10 "The capital of France is", -11 "The future of AI is", -12 ] -13 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -14 + 9 "The capital of France is", +10 "The future of AI is", +11 ] +12 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) +13 +14 # Alternatively, use "nvidia/Llama-3.1-8B-Instruct-FP8" to enable FP8 inference. 15 llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0") 16 17 outputs = llm.generate(prompts, sampling_params) @@ -642,7 +643,7 @@ The model definition is a minimal example that shows some of the optimizations a

        Container image tags

        In the example shell commands, x.y.z corresponds to the TensorRT-LLM container version to use. If omitted, IMAGE_TAG will default to tensorrt_llm.__version__ -(e.g., this documentation was generated from the 1.0.0rc2 source tree). +(e.g., this documentation was generated from the 1.0.0rc3 source tree). If this does not work, e.g., because a container for the version you are currently working with has not been released yet, you can try using a container published for a previous @@ -852,9 +853,9 @@ container published for a previous

        -

        Last updated on July 06, 2025.

        +

        Last updated on July 14, 2025.

        -

        This page is generated by TensorRT-LLM commit 66f299a.

        +

        This page is generated by TensorRT-LLM commit cfcb97a.

        diff --git a/latest/reference/ci-overview.html b/latest/reference/ci-overview.html index 826111ce83..8ec327815c 100644 --- a/latest/reference/ci-overview.html +++ b/latest/reference/ci-overview.html @@ -58,7 +58,7 @@ @@ -70,7 +70,7 @@ - + @@ -348,6 +348,7 @@
      • Generate text with guided decoding
      • Control generated text using logits processor
      • Generate text with multiple LoRA adapters
        • +
      • Speculative Decoding
      • Run LLM-API with pytorch backend on Slurm
      • Run trtllm-bench with pytorch backend on Slurm
      • Run trtllm-serve with pytorch backend on Slurm
        • @@ -408,7 +409,7 @@
      • KV Cache Management: Pools, Blocks, and Events
      • KV cache reuse
      • Speculative Sampling
        • -
      • Disaggregated-Service (experimental)
        • +
      • Disaggregated-Service (Experimental)

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

        @@ -610,9 +611,9 @@
        -

        Last updated on July 06, 2025.

        +

        Last updated on July 14, 2025.

        -

        This page is generated by TensorRT-LLM commit 66f299a.

        +

        This page is generated by TensorRT-LLM commit cfcb97a.

        diff --git a/latest/searchindex.js b/latest/searchindex.js index e6e32e0b9d..42bc248c6d 100644 --- a/latest/searchindex.js +++ b/latest/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Download TensorRT-LLM": [[21, "download-tensorrt-llm"]], "1. Using a Model from the Hugging Face Hub": [[65, "using-a-model-from-the-hugging-face-hub"]], "1. Weights size": [[87, "weights-size"]], "2. Activation size": [[87, "activation-size"]], "2. Download the DeepSeek R1 models": [[21, "download-the-deepseek-r1-models"]], "2. Using a Local Hugging Face Model": [[65, "using-a-local-hugging-face-model"]], "3. Build and run TensorRT-LLM container": [[21, "build-and-run-tensorrt-llm-container"]], "3. I/O tensors": [[87, "i-o-tensors"]], "3.1 Runtime and decoder buffers except KV cache tensor": [[87, "runtime-and-decoder-buffers-except-kv-cache-tensor"]], "3.2 KV cache tensor": [[87, "kv-cache-tensor"]], "4. Compile and Install TensorRT-LLM": [[21, "compile-and-install-tensorrt-llm"]], "5. Optional: Tune GPU clocks": [[21, "optional-tune-gpu-clocks"]], "6. 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      • Generate text with guided decoding
      • Control generated text using logits processor
      • Generate text with multiple LoRA adapters
        • +
      • Speculative Decoding
      • Run LLM-API with pytorch backend on Slurm
      • Run trtllm-bench with pytorch backend on Slurm
      • Run trtllm-serve with pytorch backend on Slurm
        • @@ -408,7 +409,7 @@
      • KV Cache Management: Pools, Blocks, and Events
      • KV cache reuse
      • Speculative Sampling
        • -
      • Disaggregated-Service (experimental)
        • +
      • Disaggregated-Service (Experimental)

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

      Performance

        @@ -716,9 +717,9 @@ In the create_pytor
        -

        Last updated on July 06, 2025.

        +

        Last updated on July 14, 2025.

        -

        This page is generated by TensorRT-LLM commit 66f299a.

        +

        This page is generated by TensorRT-LLM commit cfcb97a.