Testing Qwen3.6-27 Q8: Nearly 100K Context on 32GB VRAM
BitGreen1270 · reddit · 2026-07-05
The author shares benchmark parameters for running the Qwen3.6-27B Q8 quantized model逼近 100K context on an Ubuntu headless server (5090+64GB RAM) using llama-server. Subjectively, Q8 performs better than Q6/Q5 at around 60K context. VRAM is further optimized using configurations like KV Q80 and -b/-ub. The author notes this isn't rigorous research, just experience from a personal vibe coding project, and the VRAM is near its limit.
More from Infra
- Anthropic S-1: training infra edge cut per-task cost 40% and doubled revenue growth — BenBajarin · 2026-10-26
- Rewriting scaling law study: optimal synthetic data share rises from 10% to 32% with compute — heghbalz · 2026-10-26
- US states halt new data centers as 72% of Americans oppose local builds — YvesMulkers · 2026-10-26
- $5M Seed Round Can't Afford One DeepSeek Training Run: GPU-less AI Startup's Three Paths — FinanceYF5 · 2026-10-26
- HBM demand shifts: low-stack inference plans shelved, Rubin to go 12-high only — BenBajarin · 2026-10-26
- CircuitHub unveils The Grid, a factory-scale robot making PCBs in the US twice as fast as Shenzhen — garrytan · 2026-10-26