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
- [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`)
- [Bugfix] Llama3 JSON tool parser: flush buffered '{...}' as content when it isn't a tool call
- [Performance][Bug]: Tiered Offloading
- vllm serve segfaults deterministically right after weight loading (single L4 GPU, V1 engine) — only avoided by VLLM_TRACE_FUNCTION=1
- Docs
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