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Search.setIndex({"alltitles": {"(H200 Only) Using OpenAI Triton Kernels for MoE": [[21, "h200-only-using-openai-triton-kernels-for-moe"]], "(H200/H100 Only) Using OpenAI Triton Kernels for MoE": [[21, "h200-h100-only-using-openai-triton-kernels-for-moe"]], "1. Add the field to TorchLlmArgs": [[38, "add-the-field-to-torchllmargs"]], "1. Balance Ratio": [[8, "balance-ratio"]], "1. Committed APIs": [[38, "committed-apis"]], "1. Download TensorRT LLM": [[2, "download-tensorrt-llm"]], "1. Expert Replication and Load Balancing": [[103, "expert-replication-and-load-balancing"]], "1. Implement the method in _TorchLLM": [[38, "implement-the-method-in-torchllm"]], "1. Initial Approach for Weight Updating - cudaMemcpyAsync": [[20, "initial-approach-for-weight-updating-cudamemcpyasync"]], "1. Knob Naming": [[38, "knob-naming"]], "1. Scheduler (Leader) Interface (KvCacheConnectorScheduler)": [[96, "scheduler-leader-interface-kvcacheconnectorscheduler"]], "1. Using a Model from the Hugging Face Hub": [[158, "using-a-model-from-the-hugging-face-hub"]], "1. Weights size": [[151, "weights-size"]], "2. Activation size": [[151, "activation-size"]], "2. Avoiding Deadlock - Multithreaded CPU Copy with Managed Memory": [[20, "avoiding-deadlock-multithreaded-cpu-copy-with-managed-memory"]], "2. Custom EP Communication Kernels": [[103, "custom-ep-communication-kernels"]], "2. Download the DeepSeek R1 models": [[2, "download-the-deepseek-r1-models"]], "2. Hierarchical Configuration": [[38, "hierarchical-configuration"]], "2. Non-committed APIs": [[38, "non-committed-apis"]], "2. Speed-of-Light Throughput (SOL TPS)": [[8, "speed-of-light-throughput-sol-tps"]], "2. Update the API schema": [[38, "update-the-api-schema"], [38, "id1"]], "2. Using a Local Hugging Face Model": [[158, "using-a-local-hugging-face-model"]], "2. Worker Interface (KvCacheConnectorWorker)": [[96, "worker-interface-kvcacheconnectorworker"]], "3. Build and run TensorRT LLM container": [[2, "build-and-run-tensorrt-llm-container"]], "3. Expert Parallelism Load Balancer (EPLB)": [[103, "expert-parallelism-load-balancer-eplb"]], "3. I/O tensors": [[151, "i-o-tensors"]], "3. NUMA Memory to Prevent Page Migration": [[20, "numa-memory-to-prevent-page-migration"]], "3. Prefer LlmArgs Over Environment Variables": [[38, "prefer-llmargs-over-environment-variables"]], "3. Run validation tests": [[38, "run-validation-tests"]], "3.1 Runtime and decoder buffers except KV cache tensor": [[151, "runtime-and-decoder-buffers-except-kv-cache-tensor"]], "3.2 KV cache tensor": [[151, "kv-cache-tensor"]], "4. Addressing the TLB Thrashing Issue": [[20, "addressing-the-tlb-thrashing-issue"]], "4. Compile and Install TensorRT LLM": [[2, "compile-and-install-tensorrt-llm"]], "5. Optional: Tune GPU clocks": [[2, "optional-tune-gpu-clocks"]], "6. Dataset preparation": [[2, "dataset-preparation"]], "@record_signature to Decorate Functionals Requiring FLayerInfo": [[119, "record-signature-to-decorate-functionals-requiring-flayerinfo"]], "ADP Balance Strategy": [[8, null]], "ADP Balance Strategy: Coordinated Waiting Mechanism": [[8, "adp-balance-strategy-coordinated-waiting-mechanism"]], "ADP Balance with Context Wait Implementation": [[8, "adp-balance-with-context-wait-implementation"]], "ADP Balance with Full Strategy Implementation": [[8, "adp-balance-with-full-strategy-implementation"]], "ALiBi": [[83, "alibi"], [117, "alibi"]], "API": [[115, "api"]], "API Change Principles": [[38, "api-change-principles"]], "API Changes": [[126, "api-changes"], [164, "api-changes"], [164, "id4"], [164, "id9"], [164, "id13"], [164, "id24"], [164, "id29"], [164, "id34"], [164, "id39"], [164, "id46"], [164, "id51"], [164, "id57"], [164, "id63"], [164, "id69"]], "API Reference": [[96, "api-reference"], [109, null], [159, null]], "API Schema Management": [[38, "api-schema-management"]], "API Types and Stability Guarantees": [[38, "api-types-and-stability-guarantees"]], "ATen IR Optimization": [[108, "aten-ir-optimization"]], "AWQ Quantization Scaling Factors": [[128, "awq-quantization-scaling-factors"]], "About": [[24, "about"], [27, "about"]], "About Speculative Sampling": [[125, "about-speculative-sampling"]], "About TensorRT LLM": [[162, "about-tensorrt-llm"]], "About extra_llm_api_options": [[26, "about-extra-llm-api-options"]], "Access & Licensing": [[32, "access-licensing"], [33, "access-licensing"]], "Accuracy": [[7, "accuracy"]], "Accuracy studies for Relaxed Acceptance": [[14, "accuracy-studies-for-relaxed-acceptance"]], "Achieving speedup with MTP speculative decoding": [[14, "achieving-speedup-with-mtp-speculative-decoding"]], "Acknowledgement": [[8, "acknowledgement"], [16, "acknowledgement"], [17, "acknowledgement"]], "Acknowledgements": [[10, "acknowledgements"], [11, "acknowledgements"], [12, "acknowledgements"], [20, "acknowledgements"]], "Acknowledgment": [[13, "acknowledgment"], [14, "acknowledgment"], [15, "acknowledgment"]], "Activation": [[146, "module-tensorrt_llm.layers.activation"]], "Adding a Model": [[127, null]], "Adding a New Argument": [[38, "adding-a-new-argument"]], "Adding a New Method": [[38, "adding-a-new-method"]], "Adding a New Model": [[160, null]], "Adding a New Model in PyTorch Backend": [[165, null]], "Additional Outputs": [[82, null]], "Advanced Configuration": [[84, "advanced-configuration"], [168, "advanced-configuration"]], "Advanced Usage": [[89, "advanced-usage"], [99, "advanced-usage"], [174, "advanced-usage"], [177, "advanced-usage"]], "Advanced topics": [[110, "advanced-topics"]], "Algorithm": [[123, "algorithm"]], "Algorithm & Complexity": [[19, "algorithm-complexity"]], "An Example: Implement Dynasor-CoT on Scaffolding": [[11, "an-example-implement-dynasor-cot-on-scaffolding"]], "Announcements": [[164, "announcements"], [164, "id67"]], "Architecture": [[11, "architecture"], [38, "architecture"], [96, "architecture"], [105, "architecture"]], "Architecture Overview": [[42, null], [103, "architecture-overview"]], "Architecture Ovewiew": [[166, null]], "Asyncio-Based Generation": [[50, "asyncio-based-generation"]], "Attempts at Online EPLB Implementation": [[20, "attempts-at-online-eplb-implementation"]], "Attention": [[146, "module-tensorrt_llm.layers.attention"], [167, null]], "Attention Backends": [[83, "attention-backends"], [167, "attention-backends"]], "Attention Kernel": [[13, "attention-kernel"]], "Attention Module": [[103, "attention-module"]], "Attention Weights": [[128, "attention-weights"]], "Attention Window Size": [[97, "attention-window-size"]], "Attention backends": [[90, "attention-backends"], [175, "attention-backends"]], "Attention for MTP": [[14, "attention-for-mtp"]], "Auto Multi-stream": [[108, "auto-multi-stream"]], "AutoDeploy": [[174, null]], "AutoDeploy (Prototype)": [[89, null]], "Autoregressive MTP Layers": [[13, "autoregressive-mtp-layers"]], "Auto\u2011Enablement with Heuristic": [[19, "autoenablement-with-heuristic"]], "Avoiding unnecessary --disable-fail-fast usage": [[39, "avoiding-unnecessary-disable-fail-fast-usage"]], "B200 max-throughput for R1 with FP16 KV cache": [[2, "b200-max-throughput-for-r1-with-fp16-kv-cache"]], "B200 max-throughput for R1-0528 with FP8 KV cache": [[2, "b200-max-throughput-for-r1-0528-with-fp8-kv-cache"]], "B200 min-latency": [[2, "b200-min-latency"]], "Background": [[13, "background"], [14, "background"], [99, "background"], [100, "background"], [177, "background"]], "Background & Motivation": [[19, "background-motivation"]], "Background Knowledge": [[108, "background-knowledge"]], "Background and Challenges": [[10, "background-and-challenges"]], "Background and Motivation": [[11, "background-and-motivation"]], "BaseCheckpointLoader": [[91, "basecheckpointloader"], [176, "basecheckpointloader"]], "BaseConfigLoader": [[91, 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"beam-search"], [117, "beam-search"]], "Before Benchmarking": [[44, "before-benchmarking"], [136, "before-benchmarking"]], "Before You Begin: TensorRT-LLM LLM-API": [[137, "before-you-begin-tensorrt-llm-llm-api"]], "Benchmark": [[2, "benchmark"], [2, "id1"], [7, "benchmark"], [31, "benchmark"]], "Benchmarking Default Performance": [[137, null]], "Benchmarking Performance": [[29, "benchmarking-performance"], [30, "benchmarking-performance"], [32, "benchmarking-performance"], [33, "benchmarking-performance"], [34, "benchmarking-performance"], [35, "benchmarking-performance"]], "Benchmarking a non-Medusa Low Latency Engine": [[136, "benchmarking-a-non-medusa-low-latency-engine"]], "Benchmarking with LoRA Adapters in PyTorch workflow": [[44, "benchmarking-with-lora-adapters-in-pytorch-workflow"], [136, "benchmarking-with-lora-adapters-in-pytorch-workflow"]], "Benchmarking with trtllm-bench": [[84, null], [137, "benchmarking-with-trtllm-bench"], [168, null]], "Best Practices": [[38, 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