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
- [Bug]: client-supplied kv_transfer_params.remote_prefill_cached_tokens can forge prompt_tokens_details.cached_tokens
- [Bug]: MFU/MBU silently drops attention and FFN for GPTQ, AWQ, gpt-oss, DeepSeek-V4 FP8 and online-quantized models
- [Docs] Document engine-aware routing with vLLM Router for DP deployments
- [Model] Add Lumma (LummaForCausalLM) + XPU attention head-size fallback
- [Bug]: llama3_json streaming drops assistant content that starts with '{' but is not a tool call (non-streaming returns it)
- [Bug]: MFU/MBU bills linear-attention layers of hybrid models as full attention; reported bandwidth exceeds the GPU's peak
- [RFC]: Hardening /v1/messages (Anthropic API) for Claude Code across model families
- [Bug]: Guidance feature gate mistakes property names and literal data for `patternProperties`
- [RFC]: Native span pooling for contextual chunk embeddings
- [Qwen4Exp] Gather PLE rows on the host from the checkpoint files (`host_file_gather`)
- Docs
- Python not yet supported