Gemma 4 12B

EXL3 4 bpw · tabbyapi · 64K context · RTX 5070 Ti Laptop · 12 GB

Google's Gemma 4 12B from May 2026: a compact multimodal model with thinking, good with images and documents.

59 tok/sdecode

1,452 tok/sprefill

64Kcontext window

Tested on a siblingSep 27, 2026

Run it

Follow the steps below to download and run this recipe. Omarchy Local AI lists the recipes it can launch on your hardware. The server then answers on http://localhost:8000/v1.

1 Download the weights

hf download turboderp/gemma-4-12B-it-exl3 \
  --revision e8d17ffbe696accdba72a47dac858d7e880d709e \
  --local-dir ~/models/gemma-4-12B-it-exl3-e8d17ffb

2 Write the server config

cat > gemma-4-12b.tabbyapi.64k.yml <<'EOF'
network: {host: 0.0.0.0, port: 5000, disable_auth: true, disable_fetch_requests: true, send_tracebacks: false, api_servers: [OAI], sse_ping_interval: 15}
logging: {log_prompt: false, log_generation_params: false, log_requests: false, log_chat_completion_requests: false}
model:
  model_dir: /workspace/models
  model_name: gemma-4-12B-it-exl3-e8d17ffb
  backend: exllamav3
  inline_model_loading: false
  max_seq_len: 65536
  cache_size: 66560
  cache_mode: Q4
  tensor_parallel: false
  gpu_split_auto: true
  autosplit_reserve: [256]
  chunk_size: 2048
  output_chunking: true
  max_batch_size: 1
  vision: true
  reasoning: true
  start_in_reasoning: auto
  tool_format: null
  template_vars_default: {enable_thinking: true}
draft_model: {}
memory: {sysmem_recurrent_cache: 4096, sysmem_kv_cache: 0, cuda_malloc_async: true}
EOF

3 Start the server

docker run --rm \
  --gpus all \
  -p 8000:5000 \
  --shm-size 8g \
  -v ~/models/gemma-4-12B-it-exl3-e8d17ffb:/workspace/models/gemma-4-12B-it-exl3-e8d17ffb:ro \
  -v $PWD/gemma-4-12b.tabbyapi.64k.yml:/app/config.yml:ro \
  --entrypoint /opt/venv/bin/python3 \
  ghcr.io/0xsero/tabbyapi-exl3@sha256:0f83e6198dc3be2561652d8df2525a7d1a69733e12fc8e25cbc5bc319a8a3ad1 \
  main.py \
  --config /app/config.yml

What it passed

✓ loadLoads and serves within an hour

✓ chatAnswers a plain question and stops on its own

✓ reasoningThinks separately and gets 17 × 23 right

✓ toolsCalls a tool with the right arguments and uses the result

✓ contextRecalls a code buried in a prompt that fills 85% of the window

✓ speedDecodes at 15 tok/s or more

No RTX 5070 Ti Laptop is rentable; this ran on the RTX 5070, the same chip family.

Details

Weights: turboderp/gemma-4-12B-it-exl3 @ e8d17ffbe6 ›
Image: ghcr.io/0xsero/tabbyapi-exl3@sha256:0f83e6198dc3be2561652d8df2525a7d1a69733e12fc8e25cbc5bc319a8a3ad1
Engine profile: tabbyapi-exl3 ›
Recipe file: registry/recipes/nvidia/rtx-5070-ti-laptop-12gb/gemma-4-12b.tabbyapi.64k.json ›
Model released: May 23, 2026
Tested: vast, RTX 5070 (sibling: RTX 5070)