In the news
vLLM Delivers Day-0 Support for Kimi K3.1 Pro
vLLM · Inferact and the vLLM Team · Published · 3 min read
In 30 seconds
- What happened
- vLLM added day-zero support for Moonshot AI's Kimi K3.1 Pro, a 104B-parameter multimodal model with sparse attention across NVIDIA, AMD, Google, and Alibaba hardware.
- Why it matters
- Engineers deploying large language models at scale who need efficient long-context inference and want to serve agentic workloads with minimal latency.
- Watch out
- Sparse attention requires careful tuning of context parallelism settings; KV cache layout is complex with mixed quantization formats and token-sharded prefill adds exchange buffer overhead.
- llm
- attention
- latency
- kv cache
- token
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