Gemma 3 12B is a large language model developed by Google, featuring 12 billion parameters and an impressive context length of 32,768 tokens. This model excels in generating high-quality text across a wide range of tasks, including but not limited to, creative writing, summarization, and question-answering. Its extensive context window allows it to maintain coherence over longer passages, making it particularly suitable for tasks that require deep understanding and long-term memory.
In its size class, Gemma 3 12B holds its own, offering a balance between performance and resource efficiency. While it may not outperform the largest models in terms of raw capabilities, it provides a compelling trade-off between computational demands and output quality. The model supports quantization options like Q4_K_M and Q8_0, which help reduce the VRAM requirements to a range of 7.3 to 12.2 GB, making it feasible for users with mid-range GPUs. Ideal for researchers, developers, and enthusiasts who need a powerful yet manageable LLM, Gemma 3 12B is a solid choice for those looking to deploy advanced text generation capabilities on local hardware without the need for top-tier GPUs.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 6.799 GB | 7.3 GB | 7.8 GB | 85% |
| Q8_0 | 8 | 11.651 GB | 12.15 GB | 12.65 GB | 98% |
Context window & KV cache
Adds 1.25 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 32K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Gemma 3 12B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull gemma3:12b - 2
Chat
ollama run gemma3:12b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"gemma3:12b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Gemma 3 12B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Gemma 3 12Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
8.7 GB
7.3 GB weights + 0.9 GB KV
Aggregate tok/s
21
across 1 user
Per-user tok/s
21
12 B dense
✅ Fits in 24 GB VRAM with 15.3 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Gemma 3 12B?
Gemma 3 12B requires 7.3 GB VRAM minimum with Q4_K_M quantization. For full precision you need 12.15 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Gemma 3 12B?
To run Gemma 3 12B, you need a GPU with at least 7.3 GB of VRAM, but 12.2 GB is recommended for better performance, especially with higher quantization levels.
Is Gemma 3 12B good for coding?
Gemma 3 12B is well-suited for coding tasks due to its large context length of 32,768 tokens and high-quality training data, making it effective for code generation and completion.
Gemma 3 12B vs Llama 3.1 8B?
Gemma 3 12B has more parameters (12B vs 8B) and a longer context length (32,768 vs 2,048 tokens), which generally results in better performance for complex tasks, but requires more VRAM and computational resources.
Can I run Gemma 3 12B on a Mac?
Yes, Gemma 3 12B can run on Macs, especially those with M1 or M2 chips, which provide sufficient VRAM and computational power to handle the model efficiently.
How much VRAM does Gemma 3 12B need?
Gemma 3 12B requires between 7.3 GB and 12.2 GB of VRAM, depending on the quantization level used. Higher quantization levels reduce VRAM usage but may slightly impact performance.
Is Gemma 3 12B censored?
Gemma 3 12B is not inherently censored, but its responses are guided by the training data and any filters applied during inference. Users can implement additional content moderation as needed.
Is Gemma 3 12B commercial-use allowed?
Yes, Gemma 3 12B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 12B context length?
Gemma 3 12B has a context length of 32,768 tokens, which is significantly longer than many other models, allowing it to handle longer and more complex inputs.
Does Gemma 3 12B support function calling?
Gemma 3 12B supports function calling, enabling it to interact with external systems and APIs, enhancing its capabilities for various applications.
Gemma 3 12B quantization options?
Gemma 3 12B supports multiple quantization options, including INT8 and INT4, which reduce VRAM usage and improve inference speed while maintaining acceptable accuracy.
Can Gemma 3 12B run on CPU?
While Gemma 3 12B can technically run on a CPU, it is highly inefficient and slow. Using a GPU with sufficient VRAM is strongly recommended for practical performance.
Gemma 3 12B fine-tuning?
Gemma 3 12B can be fine-tuned on custom datasets to improve performance on specific tasks. Fine-tuning typically requires a powerful GPU and a significant amount of data.
Gemma 3 12B system requirements?
To run Gemma 3 12B, you need a system with at least 7.3 GB of VRAM, 32 GB of RAM, and a multi-core CPU. For optimal performance, a GPU with 12.2 GB of VRAM and an SSD are recommended.
Gemma 3 12B performance benchmark?
Gemma 3 12B can process around 50-100 tokens per second on a high-end GPU like the RTX 3090, depending on the quantization level and batch size.
Gemma 3 12B for RAG?
Gemma 3 12B is suitable for Retrieval-Augmented Generation (RAG) tasks due to its large context length and ability to handle complex queries, making it effective for integrating external knowledge sources.
Gemma 3 12B for agents?
Gemma 3 12B can be used to create intelligent agents due to its strong natural language understanding and generation capabilities, making it suitable for chatbots, virtual assistants, and other conversational applications.
Gemma 3 12B for coding vs general?
Gemma 3 12B performs well in both coding and general tasks, but its large context length and specialized training data make it particularly strong for coding-related tasks such as code generation and documentation.
Gemma 3 12B vs ChatGPT?
Gemma 3 12B has a larger context length (32,768 vs 2,048 tokens) and is specifically optimized for local deployment, while ChatGPT is a cloud-based service with a different set of capabilities and use cases.
Gemma 3 12B download size?
The download size of Gemma 3 12B varies depending on the quantization level. The full model is approximately 24 GB, but quantized versions can be as small as 6 GB.
Best quant for Gemma 3 12B?
The best quantization for Gemma 3 12B depends on your hardware. INT8 provides a good balance between performance and VRAM usage, while INT4 is more efficient but may have a slight drop in accuracy.