TensorRT-LLM Release 0.17.0

Hi,

We are very pleased to announce the 0.17.0 version of TensorRT-LLM. This update includes:

Key Features and Enhancements

• Blackwell support
  • NOTE: pip installation is not supported for TRT-LLM 0.17 on Blackwell platforms only. Instead, it is recommended that the user build from source using NVIDIA NGC 25.01 PyTorch container.
  • Added support for B200.
  • Added support for GeForce RTX 50 series using Windows Subsystem for Linux (WSL) for limited models.
  • Added NVFP4 Gemm support for Llama and Mixtral models.
  • Added NVFP4 support for the LLM API and trtllm-bench command.
  • GB200 NVL is not fully supported.
  • Added benchmark script to measure perf benefits of KV cache host offload with expected runtime improvements from GH200.
• PyTorch workflow
  • The PyTorch workflow is an experimental feature in tensorrt_llm._torch. The following is a list of supported infrastructure, models, and features that can be used with the PyTorch workflow.
  • Added support for H100/H200/B200.
  • Added support for Llama models, Mixtral, QWen, Vila.
  • Added support for FP16/BF16/FP8/NVFP4 Gemm and fused Mixture-Of-Experts (MOE), FP16/BF16/FP8 KVCache.
  • Added custom context and decoding attention kernels support via PyTorch custom op.
  • Added support for chunked context (default off).
  • Added CudaGraph support for decoding only.
  • Added overlap scheduler support to overlap prepare inputs and model forward by decoding 1 extra token.
• Added FP8 context FMHA support for the W4A8 quantization workflow.
• Added ModelOpt quantized checkpoint support for the LLM API.
• Added FP8 support for the Llama-3.2 VLM model. Refer to the “MLLaMA” section in examples/multimodal/README.md.
• Added PDL support for userbuffer based AllReduce-Norm fusion kernel.
• Added runtime support for seamless lookahead decoding.
• Added token-aligned arbitrary output tensors support for the C++ executor API.

API Changes

• [BREAKING CHANGE] KV cache reuse is enabled automatically when paged_context_fmha is enabled.
• Added --concurrency support for the throughput subcommand of trtllm-bench.

Fixed Issues

• Fixed incorrect LoRA output dimension. Thanks for the contribution from @akhoroshev in #2484.
• Added NVIDIA H200 GPU into the cluster_key for auto parallelism feature. (#2552)
• Fixed a typo in the __post_init__ function of LLmArgs Class. Thanks for the contribution from @topenkoff in #2691.
• Fixed workspace size issue in the GPT attention plugin. Thanks for the contribution from @AIDC-AI.
• Fixed Deepseek-V2 model accuracy.

Infrastructure Changes

• The base Docker image for TensorRT-LLM is updated to nvcr.io/nvidia/pytorch:25.01-py3.
• The base Docker image for TensorRT-LLM Backend is updated to nvcr.io/nvidia/tritonserver:25.01-py3.
• The dependent TensorRT version is updated to 10.8.0.
• The dependent CUDA version is updated to 12.8.0.
• The dependent ModelOpt version is updated to 0.23 for Linux platform, while 0.17 is still used on Windows platform.

Known Issues

• Need --extra-index-url https://pypi.nvidia.com when running pip install tensorrt-llm due to new third-party dependencies.
• The PYPI SBSA wheel is incompatible with PyTorch 2.5.1 due to a break in the PyTorch ABI/API, as detailed in the related GitHub issue.