vllm
https://github.com/vllm-project/vllm
Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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- Issues
- [Quantization] add humming moe backend to all fused moe oracles
- [Build] Switch CUDA 12.9 wheel builds to PyTorch manylinux_2_28 base
- [ROCm][CI] Remove benchmarks test group and shard long test groups
- Layerwise reload crashes with CUDA illegal memory access on compressed-tensors channel-wise FP8 MoE
- [Bug]: thinking_token_budget silently ignored when no penalties are set (inverted condition in gpu_input_batch.py)
- [Bugfix] Fix inverted condition causing thinking_token_budget to be silently ignored
- [Bug]: Pipeline Parallelism scheduler does not split sequences into pipeline micro-batches
- Keep first/last n token in high precision for nvfp4 kv cache
- [RFC]: Long-context-optimized Pipeline Parallelism, CPP + Async P2P + Dynamic Chunking
- [Bug]: DeepEP MoE all-to-all backend integration is unusable on Blackwell (SM103 / GB300)
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