Run qwen3.8-27b iq3_s quant in 16G VRAM with 128K context, 50 t/s

I managed to get it run and would like to share my setup. hope it may help somebody.

My setup:

CPU: 12600K

Memory: 64G DDR4

Graphic card: nVidia 4070ti super 16G VRAM

OS: Ubuntu 24.04.5

Model engine: buun-llama-cpp

model: Huihui-Qwen3.8-27B-abliterated-UD-IQ3_S.gguf (based on Unsloth IQ3_S)

My start script:

#!/bin/bash

# Define variables for easy tweaking
MODEL_PATH="$HOME/llm_models/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-UD-IQ3_S.gguf"
THREADS=$(nproc) # Automatically uses maximum physical cores
GPU_LAYERS=999
CONTEXT=128000
PORT=1234

# Execute llama-server with parameters
~/buun-llama-cpp/build/bin/llama-server \
  -m "$MODEL_PATH" \
  --alias "qwen3.8-27b" \
  --jinja --reasoning-format deepseek \
  --chat-template-kwargs "{\"reasoning_effort\": \"medium\"}" \
  --reasoning-preserve \
  --no-mmproj \
  --ubatch-size 1024 --batch-size 2048 \
  --spec-type mtp --spec-draft-n-max 2  \
  --cache-type-k-draft turbo2_tcq --cache-type-v-draft turbo2_tcq \
  -np 1 --kv-unified -ctk vbr -ctv vbr --fit off \
  --load-mode none -fa on --temp 1 --top-p 0.95 --top-k 20 --min-p 0.0 \
  -t "$THREADS" \
  -ngl "$GPU_LAYERS" \
  -c "$CONTEXT" \
  --port "$PORT" \
  --host 0.0.0.0

With this setup, I got prefill start from 1300 t/s, down to around 800 t/s when context go beyond 100K.

Decode speed around 50 t/s, very usable for Hermes agent.

the vbr kv quant will give you decent prefill speed and quality for short context for it start from F16, and will not give you OOM for it will compress the less important context to lower quant when its getting longer.

if you boot the OS to CLI mode, it will save you some more 300MB~600MB VRAM for your context, you may even able to fit in a IQ4_XS main model.

I tried but it was not very stable.

Waiting for the buun fork to improve.