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
- [CI][Intel] Add GSM8K DeepSeek-V2-Lite-Instruct-FP8 XPU eval job
- [Feature] Support KV-cache events & KV-transfer for hybrid-attention models
- [Feature]: Real-time request-queue signal (NUM_REQUESTS_RUNNING/WAITING) for external routers
- [ROCm][Perf] gfx942: use FlyDSL fp8 MQA logits kernel (ROCm/aiter#3913)
- [RFC]: Speculative recompute fallback for slow remote KV cache loads
- FlashInfer + spec-decode silently downgrades to PIECEWISE cudagraphs (−16% measured); warning understates the cost
- [Performance]: Dynamic speculative decoding (num_speculative_tokens_per_batch_size) causes catastrophic aggregate-throughput collapse under concurrency at the batch-size threshold (MTP, V1/PIECEWISE)
- [Bug]: MTP draft model ignores checkpoint per-layer quantization config (block_name_to_quantize mapped inconsistently vs target)
- [Bug]: In-tree fallback LMCache adapter does not propagate retrieve failures to the engine
- [Bugfix][MoE] Filter packed expert weights during EP loading
- Docs
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