I squeezed it into 24 GiB VRAM (since I have RX7900XTX):

-- Q5_K_M Unsloth quantization on Linux llama.cpp

-- context 81k, flash attention on, 8-bit K/V caches

-- pp 625 t/s, tg 30 t/s

I have the same GPU and get very good results, even better than Gemma 4 26B A4B, using the following setup (Fedora 43 Silverblue, podman compose):

  services:
    llama:
      image: ghcr.io/ggml-org/llama.cpp:server-vulkan
      container_name: llama-qwen3.6-27b-dense
      ports:
        - 4201:8080
      volumes:
        - ./Qwen3.6-27B-Q4_K_M.gguf:/models/model.gguf:ro,z
        - ./mmproj-BF16.gguf:/models/mmproj.gguf:ro,z
      devices:
        - /dev/dri
      group_add:
        - video
      command: >
        -m /models/model.gguf
        --mmproj /models/mmproj.gguf
        --alias "Qwen3.6 27b Dense"
        -ngl 99
        -c 98304
        -b 2048
        --host 0.0.0.0
        --port 8080
        --parallel 2
        --kv-unified
        --ubatch-size 2048
        --flash-attn on
        -cb
        --jinja
        --no-webui
        -ctk q8_0
        -ctv q8_0
        --image-min-tokens 1024
        --temp 0.6
        --top-k 20
        --top-p 0.95
        --repeat-penalty 1
        --presence-penalty 1.5
        --reasoning auto
      restart: unless-stopped

Depends entirely on quantization. Q6_K with max context length (262144) is ~40GB of VRAM.

Q8 with the same context wouldn't fit in 48GB of VRAM, it did with 128k of context.