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8317.2
TFLOPS
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11
46
Mitko Vasilev
mitkox
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iotcoi
mitkox
AI & ML interests
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
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posted
an
update
2 days ago
Got to 1199.8 tokens/sec with Devstral Small -2 on my desktop GPU workstation. vLLM nightly. Works out of the box with Mistral Vibe. Next is time to test the big one.
posted
an
update
19 days ago
I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. Itβs a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config. Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2. Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,
posted
an
update
about 1 month ago
I just threw Qwen3-0.6B in BF16 into an on device AI drag race on AMD Strix Halo with vLLM: 564 tokens/sec on short 100-token sprints 96 tokens/sec on 8K-token marathons TL;DR You don't just run AI on AMD. You negotiate with it. The hardware absolutely delivers. Spoiler alert; there is exactly ONE configuration where vLLM + ROCm + Triton + PyTorch + Drivers + Ubuntu Kernel to work at the same time. Finding it required the patience of a saint Consumer AMD for AI inference is the ultimate "budget warrior" play, insane performance-per-euro, but you need hardcore technical skills that would make a senior sysadmin nod in quiet respect.
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Runtime error
M A P OpenCodeInterpreter DS 6.7B
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