llamaperf

Qwen3.8 27B

on NVIDIA RTX 3090 · llama.cpp · 205,000 ctx

Tone: positiveNVIDIA hardware
Sep 7, 2026
Throughput
59.0 t/s gen
Quant
Q4_K_M (GGUF)
KV cache
Q8
VRAM reported
24 GB

Use cases

codingagentic

Summary

User reports 59 t/s in a one-shot full software run and up to 70 t/s in testing on an existing codebase. Setup uses an asymmetric Q8 KV cache quant and speculative decoding with an MTP draft at 205k context, with --ctx-size 205000, --parallel 1, --kv-unified, --flash-attn on, -ctk q8_0, -ctv q4_0, --temp 0.6, --top-p 0.95, --top-k 20, --min-p 0.0, --spec-type draft-mtp, --spec-draft-n-max 4, --spec-draft-p-min 0.7, --batch-size 512, --ubatch-size 512, --reasoning-effort xhigh, --reasoning-format auto, --fit off, --cache-prompt, -ngl 99 and --no-mmproj. The user notes this is faster than mtp2, which had 120k context and 41 t/s.