Gemma 4 31B IT QAT W4A16 CT
Google's official instruction-tuned Gemma 4 31B checkpoint with W4A16 quantization-aware compressed weights for text and image understanding. The published repository is downloadable; RAM and VRAM figures on this page remain runtime planning estimates.
Official Gemma 4 QAT checkpoint
What this model actually is
This is Google's instruction-tuned 31B Gemma 4 checkpoint using a W4A16 compressed-tensor layout. It is a multimodal text-and-image model, not an image generator. The official Hugging Face repository is public and contains the downloadable safetensors checkpoint.
Decision shortcut: Choose it when you want a strong multimodal model in the roughly 24–32GB planning class and your runtime supports the published Gemma 4 QAT architecture. Prefer a smaller Gemma or Qwen model when memory headroom, mature GGUF packaging, or speed matters more than model size.
The named Hugging Face repository is the official downloadable checkpoint, not an Ollama tag.
Use it for chat, RAG, and visual understanding. It does not generate images.
These are planning estimates derived from artifact size plus runtime headroom, not a LocalAIRun hardware test.
Long context increases KV-cache memory, so the minimum-memory estimate should not be used for maximum-context runs.
Good reasons to choose it
- Multimodal chat and document workflows on 32GB-class systems
- Users who need an official QAT checkpoint rather than an unofficial quantization
- Vision and RAG evaluation where image understanding is part of the workload
Verify before deployment
- Confirm that the exact runtime version supports Gemma 4 compressed tensors or the selected GGUF conversion.
- Leave additional memory for the operating system, image encoder, context cache, and application layer.
- Treat the Q4 and Q8 rows below as planning artifacts until a runtime-specific report is attached.
Task Fit
Not marked for typed decisions in the current library.
Not marked for code agent in the current library.
Not marked for code in the current library.
General writing, Q&A, and assistant use.
Document QA benefits from long context and instruction following.
Image or visual understanding, not necessarily image generation.
Not marked for image generation in the current library.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Evidence level
How this is assessed →Memory and hardware-fit values are planning estimates unless a tested report is linked.
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| Q4_0 | gguf | balanced | 24GB | 32GB | llama.cpp, lm-studio | Plan with this |
| Q8_0 | gguf | high | 40GB | 64GB | llama.cpp, lm-studio | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Enough unified/system memory with a balanced 5-year cost.
Highest local performance signal among compatible hardware.
Benchmarks
No benchmark data is available for this model yet.
Sources and compatibility
Execution evidence
Run Gemma 4 31B IT QAT W4A16 CT with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.