llamaperf

Qwen3.8 27B

on 2× NVIDIA RTX 3090 · Geezuz

Tone: positiveNVIDIA hardware
Oct 21, 2026
Throughput
190.0 t/s gen
Quant
Q4_K_M (GGUF)
KV cache
8-bit

Use cases

coding

Summary

User reports Qwen3.8-27B at 190 tok/s on two RTX 3090s power-limited to 280 W over plain PCIe, at 0 context. Setup is the Geezuz engine, a dual-GPU fork of L-Forster's open-jet megakernel, with Q4_K_M GGUF weights and 8-bit KV cache; the model drafts with its own MTP head, verifying 5 drafted tokens per pass. At 8 concurrent requests the total is 622 tok/s; Q8_0 gives 143 tok/s single and 610 tok/s at 8. The user says llama.cpp's fastest two-GPU setup did about 100 tok/s on the same prompts, and that Geezuz averaged 164 tok/s and 214 on code at the same KL divergence. At 10K context it reports 159 tok/s single-request and 550 at 8 requests; vLLM still leads at 8 parallel requests by about 15%.