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
- feat(completions): token-native binary transport via stream_format=msgpack|protobuf
- [KV Offload] Expose SimpleCPU offload metrics
- [Doc] Add JarvisLabs deployment guide
- [Kernel][MLA] Triton-fused TurboQuant decode backend
- Route TorchAO and LLM-Compressor Quantized Inference through zentorch on AMD Zen CPUs
- [CI][Elastic EP] Fix Elastic EP Scaling Test Failure
- [Bug] DFlash speculative decoding fundamentally incompatible with all KV cache quantization (fp8, turboquant) due to non-causal attention requirement
- [Feature]: Close all non gpustack related tickets and route them to provider`s github
- [Feature]: Polymorphic buffer management for V1 worker (CPU/GPU staged tensors, lower hot-path overhead)
- [Bug]: Decode Context Parallelism (`--decode-context-parallel-size`) output drift and gibberish in latest nightly
- Docs
- Python not yet supported