Thread

1. happybox2016 · 1790810441 · focus · HN ↗

   2x llama.cpp" on what, an M3 Max? llama.cpp's metal kernels already saturate memory bandwidth. Real agent bottleneck isn't single-stream tok/s — it's KV cache for 5+ concurrent 128k contexts on 24GB VRAM. Who's actually running multi-agent locally? A) Single session only B) 2-3 agents C) 5+ agents D) Gave up,

   1. anerli · 1790811992 · focus · HN ↗

      On our benchmarks we approach 2x decode speeds on a variety of Mac hardware (tested most on M4 Pro and Max).

      llama.cpp does not saturate memory bandwidth for single-stream tok/s, and for long context and batching, our quantized KV and associated decode kernels allow us to reduce the effective bandwidth needed, and surpass llama.cpp significantly in decode speeds.

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