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Search.setIndex({"docnames": ["_cpp_gen/executor", "_cpp_gen/runtime", "advanced/batch-manager", "advanced/expert-parallelism", "advanced/gpt-attention", "advanced/gpt-runtime", "advanced/graph-rewriting", "advanced/inference-request", "advanced/lora", "advanced/weight-streaming", "architecture/add-model", "architecture/checkpoint", "architecture/core-concepts", "architecture/model-weights-loader", "architecture/overview", "architecture/workflow", "blogs/Falcon180B-H200", "blogs/H100vsA100", "blogs/H200launch", "blogs/XQA-kernel", "blogs/quantization-in-TRT-LLM", "commands/trtllm-build", "executor", "index", "installation/build-from-source-linux", "installation/build-from-source-windows", "installation/linux", "installation/windows", "key-features", "kv_cache_reuse", "llm-api-examples/customization", "llm-api-examples/index", "llm-api-examples/llm_api_examples", "llm-api-examples/llm_auto_parallel", "llm-api-examples/llm_generate", "llm-api-examples/llm_generate_async", "llm-api-examples/llm_generate_async_streaming", "llm-api-examples/llm_generate_distributed", "llm-api-examples/llm_quantization", "llm-api/index", "overview", "performance/perf-analysis", "performance/perf-best-practices", "performance/perf-overview", "python-api/tensorrt_llm.functional", "python-api/tensorrt_llm.layers", "python-api/tensorrt_llm.models", "python-api/tensorrt_llm.plugin", "python-api/tensorrt_llm.quantization", "python-api/tensorrt_llm.runtime", "quick-start-guide", "reference/memory", "reference/precision", "reference/support-matrix", "reference/troubleshooting", "release-notes", "speculative_decoding"], "filenames": ["_cpp_gen/executor.rst", "_cpp_gen/runtime.rst", "advanced/batch-manager.md", "advanced/expert-parallelism.md", "advanced/gpt-attention.md", "advanced/gpt-runtime.md", "advanced/graph-rewriting.md", "advanced/inference-request.md", "advanced/lora.md", "advanced/weight-streaming.md", "architecture/add-model.md", "architecture/checkpoint.md", "architecture/core-concepts.md", "architecture/model-weights-loader.md", "architecture/overview.md", "architecture/workflow.md", "blogs/Falcon180B-H200.md", "blogs/H100vsA100.md", "blogs/H200launch.md", "blogs/XQA-kernel.md", "blogs/quantization-in-TRT-LLM.md", "commands/trtllm-build.rst", "executor.md", "index.rst", "installation/build-from-source-linux.md", "installation/build-from-source-windows.md", "installation/linux.md", "installation/windows.md", "key-features.md", "kv_cache_reuse.md", "llm-api-examples/customization.md", "llm-api-examples/index.md", "llm-api-examples/llm_api_examples.rst", "llm-api-examples/llm_auto_parallel.rst", "llm-api-examples/llm_generate.rst", "llm-api-examples/llm_generate_async.rst", "llm-api-examples/llm_generate_async_streaming.rst", "llm-api-examples/llm_generate_distributed.rst", "llm-api-examples/llm_quantization.rst", "llm-api/index.rst", "overview.md", "performance/perf-analysis.md", "performance/perf-best-practices.md", "performance/perf-overview.md", "python-api/tensorrt_llm.functional.rst", "python-api/tensorrt_llm.layers.rst", "python-api/tensorrt_llm.models.rst", "python-api/tensorrt_llm.plugin.rst", "python-api/tensorrt_llm.quantization.rst", "python-api/tensorrt_llm.runtime.rst", "quick-start-guide.md", "reference/memory.md", "reference/precision.md", "reference/support-matrix.md", "reference/troubleshooting.md", "release-notes.md", "speculative_decoding.md"], "titles": ["Executor", "Runtime", "The Batch Manager in TensorRT-LLM", "Expert Parallelism in TensorRT-LLM", "Multi-Head, Multi-Query, and Group-Query Attention", "C++ GPT Runtime", "Graph Rewriting Module", "Inference Request", "Run gpt-2b + LoRA using GptManager / cpp runtime", "Running With Weight Streaming to Reduce GPU Memory Consumption", "Adding a Model", "TensorRT-LLM Checkpoint", "Model Definition", "TensorRT-LLM Model Weights Loader", "TensorRT-LLM Architecture", "TensorRT-LLM Build Workflow", "Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100", "H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token", "H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM", "New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget", "Speed up inference with SOTA quantization techniques in TRT-LLM", "trtllm-build", "Executor API", "Welcome to TensorRT-LLM\u2019s Documentation!", "Building from Source Code on Linux", "Building from Source Code on Windows", "Installing on Linux", "Installing on Windows", "Key Features", "KV cache reuse", "Common Customizations", "LLM Examples Introduction", "Examples", "LLM Auto Parallel", "LLM Generate", "LLM Generate Async", "LLM Generate Async Streaming", "LLM Generate Distributed", "LLM Quantization", "API Reference", "Overview", "Performance Analysis", "Best Practices for Tuning the Performance of TensorRT-LLM", "Overview", "Functionals", "Layers", "Models", "Plugin", 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