vLLM(@vllm_project):This week's vLLM Office Hours: @AMD on trends in AI agent applications. Every contribution ships upstream in vLLM main. The primitives agentic inference needs are all in vLLM today: 🧠 Prefix caching — automatic KV reuse across agent turns, lower TTFT 🦅 EAGLE / P-EAGLE spec decode — draft proposals verified in a single pass 🛠️ Tool calling — parallel calls + guided decoding for schema-compliant outputs 🌙 Mooncake KV connector — distributed KV offload for long agentic traces 💾 CPU KV offload — throughput gains once KV cache outgrows GPU memory 🧭 vLLM Semantic Router — route requests across small vs large models (joint work with @AIatAMD) full session 👇

2026.05.16 14:19

This week's vLLM Office Hours: @AMD on trends in AI agent applications. Every contribution ships upstream in vLLM main. The primitives agentic inference needs are all in vLLM today: 🧠 Prefix caching — automatic KV reuse across agent turns, lower TTFT 🦅 EAGLE / P-EAGLE spec decode — draft proposals verified in a single pass 🛠️ Tool calling — parallel calls + guided decoding for schema-compliant outputs 🌙 Mooncake KV connector — distributed KV offload for long agentic traces 💾 CPU KV offload — throughput gains once KV cache outgrows GPU memory 🧭 vLLM Semantic Router — route requests across small vs large models (joint work with @AIatAMD) full session 👇

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