llamaperf

Qwen3.8 27B

on NVIDIA RTX 5090 · llama.cpp · 160,000 ctx

Tone: mixedNVIDIA hardware
Sep 7, 2026
Throughput
120.0 t/s gen
Quant
Q5_K_L (GGUF)
KV cache
Q8
VRAM reported
32 GB

Use cases

coding

Summary

User reports DFlash2 speculative decoding with Qwen3.8 27B on an RTX 5090, with bursts of ~200 tk/s on code, ~80-90 tk/s on thinking, and an average of ~120 tk/s for code generation. Setup uses a Q5_K_L base model and a Q4_K_M draft model. The user compares this to MTP at ~140 tk/s on code and ~100 tk/s otherwise, notes the setup is memory hungry and required reducing context from 220k to 160k, and reports better results previously with DFlash on Qwen3.6 27B.