Best larger-system route
Studio 96
The smallest system in the range that reaches this model's preferred working allowance.
Explore Studio 96Google DeepMind model
Gemma 4 12B Hardware Requirements & Server Compatibility
Gemma 4 12B is a current Google DeepMind release for multimodal assistants that need image, video or audio understanding on one workstation gpu. The pinned repository contains approximately 23.92GB of model weights. Our 32GB on one GPU figure is a calculated memory screen, while 64GB on one GPU is the safer starting allowance for deployment testing.
- Model version
google/gemma-4-12b-it- Family and variant
- Gemma 4 · 12B instruction tuned
- Source version
707f0a3b8a3c- Source updated
- 20 July 2026
Google DeepMind Gemma 4 12B
google/gemma-4-12b-it - Minimum GPU memory
- 32GB on one GPU
- Recommended hardware
- 64GB on one GPU
- Licence
- Apache License 2.0
- Useful for
- Language & reasoning, Vision & OCR, Speech & audio
Buyer verdict
Where Gemma 4 12B is a sensible fit
Shortlist Gemma 4 12B when multimodal assistants that need image, video or audio understanding on one workstation gpu is the priority and the exact licence and runtime suit the organisation. Choose hardware from the recommended allowance, then measure quality and performance on representative work before purchase.
These figures apply to the named model version. Quantisation, fine-tuning, context length, image resolution, batch size and serving software can materially change the hardware needed.
Hardware requirements
- Minimum
- 32GB on one GPU
- Recommended
- 64GB on one GPU
This allowance is derived from the pinned artifact and leaves only limited runtime headroom.
This working allowance creates room for runtime allocations and representative workload testing; it is not a performance benchmark.
Technical specification
Gemma 4 12B model and hardware facts
Specifications shown for source version
707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7, updated
20 July 2026.
- Architecture
- Dense multimodal model with hybrid attention
- Parameters
- 11.95B
- Context
- 262,144 tokens Architecture ceiling; serving capacity requires testing
- Modalities
- Text + image + video + audio → text
- Repository weights
- 23.92GB Pinned first-party repository file total
- Native format
- BF16 safetensors
Product compatibility
Gemma 4 12B compatibility across all 11 GPU systems
- Systems that fit without model splitting
- 11
- Systems needing multi-GPU validation
- 0
A system is listed as fitting when its GPU memory meets the requirement shown above. Speed, usable context, batch size and concurrent users still need testing with the final model and software configuration.
sme workstation
Team 32
- Per GPU
- 32GB
- Total GPU memory
- 32GB
- GPU count
- 1
Meets the lower memory screen, but not the recommended working allowance.
Available GPU memory clears the lower 32GB on one GPU screen but not the 64GB on one GPU working allowance. It may load, but choose a larger system when context, batch, concurrency or response time matter.
sme workstation
Company 64
- Per GPU
- 32GB
- Total GPU memory
- 64GB
- GPU count
- 2
Meets the lower memory screen, but not the recommended working allowance.
Available GPU memory clears the lower 32GB on one GPU screen but not the 64GB on one GPU working allowance. It may load, but choose a larger system when context, batch, concurrency or response time matter.
sme workstation
Studio 96
- Per GPU
- 96GB
- Total GPU memory
- 96GB
- GPU count
- 1
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
sme workstation
Studio 192
- Per GPU
- 96GB
- Total GPU memory
- 192GB
- GPU count
- 2
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Value Rack 128
- Per GPU
- 32GB
- Total GPU memory
- 128GB
- GPU count
- 4
Meets the lower memory screen, but not the recommended working allowance.
Available GPU memory clears the lower 32GB on one GPU screen but not the 64GB on one GPU working allowance. It may load, but choose a larger system when context, batch, concurrency or response time matter.
pcie rack
Value Rack 256
- Per GPU
- 32GB
- Total GPU memory
- 256GB
- GPU count
- 8
Meets the lower memory screen, but not the recommended working allowance.
Available GPU memory clears the lower 32GB on one GPU screen but not the 64GB on one GPU working allowance. It may load, but choose a larger system when context, batch, concurrency or response time matter.
pcie rack
Enterprise 384
- Per GPU
- 96GB
- Total GPU memory
- 384GB
- GPU count
- 4
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Enterprise 768
- Per GPU
- 96GB
- Total GPU memory
- 768GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
H200 1.1TB
- Per GPU
- 141GB
- Total GPU memory
- 1,128GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
frontier partner
Frontier Native 2.3TB
- Per GPU
- 288GB
- Total GPU memory
- 2,304GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
frontier partner
Frontier Rack 20TB
- Per GPU
- 288GB
- Total GPU memory
- 20,736GB
- GPU count
- 72
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
Deployment reality
Strengths, limits and runtime route
What it is good at
- Text, image, video and audio input in the 12B release.
- 256K context.
- A single-GPU workstation deployment route.
- Instruction-tuned official checkpoint.
Where to be cautious
- Audio and video preprocessing can materially increase system requirements.
- Runtime support for the very recent Gemma 4 architecture must be pinned and tested.
- Repository weight size does not include every runtime allocation, KV cache, media encoder, batch or concurrent session.
- No GPU Servers benchmark result is claimed until this exact revision has been run under disclosed test conditions.
Serving software
- Transformers
- Gemma runtime integrations
Before installation
- Use source version 707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7 rather than an unversioned latest branch.
- Start with Transformers only after checking support for the exact architecture and numerical format.
- Use 64GB on one GPU as the procurement starting point; the lower figure is a minimum-memory screen, not a service-level promise.
- Record precision, context, batch, concurrency, container digest, driver and measured latency in the acceptance report.
System requirements
- GPU layout
- The minimum is screened against one GPU. Additional GPUs provide replicas or throughput unless the chosen runtime supports model parallelism.
- Context and cache
- 262,144 tokens. Context length, KV cache, batch and concurrent sessions must be tested together; the architecture limit is not a throughput guarantee.
- System RAM
- Plan system RAM above the 23.92GB repository-weight footprint where model staging or CPU offload is required.
- Storage
- Reserve at least 60GB for the pinned weights, runtime cache and one rollback copy; production datasets and logs are additional.
- Serving software
- First-party material names Transformers, Gemma runtime integrations. Pin the serving version and container digest because support for recent architectures can change quickly.
- Representative workload
- Acceptance-test multimodal assistants that need image, video or audio understanding on one workstation gpu at the intended quality, context, batch, concurrency and response-time target.
Performance: speed, usable context and concurrency depend on the selected system, software and workload. No benchmark is quoted on this page.
Commercial and legal boundary
Apache License 2.0
Commercial use: permitted
The Gemma 4 model card identifies Apache 2.0; retain notices and apply Google's linked terms.
Always retain the applicable notices and recheck the live terms for the intended organisation, territory, use and distribution route. Obtain legal advice where required; the official licence governs use.
Read the official licenceTechnical questions
Gemma 4 12B deployment FAQ
What GPU memory does Gemma 4 12B need?
Use 32GB on one GPU as the lower memory screen and 64GB on one GPU as the safer starting allowance. The final requirement changes with runtime, context, batch, concurrency and precision.
Which GPU Servers product should I start with for Gemma 4 12B?
Use the compatibility table to find systems that meet the recommended allowance. A system that only meets the minimum can load the model in principle but may not meet the required context or response time.
Can Gemma 4 12B be used commercially?
The Gemma 4 model card identifies Apache 2.0; retain notices and apply Google's linked terms. The linked official licence is authoritative; legal advice may be appropriate for the intended use and distribution route.
What a complete Gemma 4 12B deployment needs
GPU memory is only one part of the system. Storage, data access, serving software, monitoring and administrator handover also affect a reliable deployment.
