Gemma 3 MoE 9B is Google take on the open MoE recipe. 9 B total / 2.5 B active makes it the natural step-up from Gemma 3 4B for users with 12 GB cards. Same Gemma license terms apply, so commercial use is permitted with attribution but not unrestricted.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 5.5 GB | 7 GB | 10 GB | 85% |
Context window & KV cache
Adds 1.00 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 8K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Gemma 3 MoE 9B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
bartowski/gemma-3-moe-9b-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Gemma 3 MoE 9B 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 MoE 9Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
8.3 GB
7.0 GB weights + 0.8 GB KV
Aggregate tok/s
100
across 1 user
Per-user tok/s
100
MoE active params
✅ Fits in 24 GB VRAM with 15.8 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 MoE 9B?
Gemma 3 MoE 9B requires 7 GB VRAM minimum with Q4_K_M quantization. For full precision you need 7 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 MoE 9B?
To run Gemma 3 MoE 9B, you need a GPU with at least 12 GB of VRAM. The model requires 7.0 GB of VRAM, but a 12 GB card is recommended for optimal performance.
Is Gemma 3 MoE 9B good for coding?
Gemma 3 MoE 9B is well-suited for coding tasks due to its strong contextual understanding and ability to generate coherent code snippets. However, specialized models like Codex may offer more tailored performance for coding-specific tasks.
Gemma 3 MoE 9B vs Llama 3.1 8B?
Gemma 3 MoE 9B has 9 billion parameters and a context length of 8192 tokens, while Llama 3.1 8B has 8 billion parameters and a context length of 2048 tokens. Gemma 3 MoE 9B generally offers better performance in tasks requiring longer context and more parameters.
Can I run Gemma 3 MoE 9B on a Mac?
Yes, you can run Gemma 3 MoE 9B on a Mac with an M1 or M2 chip, but you will need to ensure you have the necessary dependencies and libraries installed. A GPU with at least 12 GB of VRAM is still recommended for optimal performance.
How much VRAM does Gemma 3 MoE 9B need?
Gemma 3 MoE 9B requires 7.0 GB of VRAM, but a GPU with at least 12 GB of VRAM is recommended to handle the model efficiently.
Is Gemma 3 MoE 9B censored?
Gemma 3 MoE 9B is not inherently censored, but it adheres to ethical guidelines and may filter out harmful or inappropriate content during inference.
Is Gemma 3 MoE 9B commercial-use allowed?
Gemma 3 MoE 9B is licensed under the 'gemma' license, which allows for commercial use. However, you should review the specific terms of the license for any restrictions or requirements.
Gemma 3 MoE 9B context length?
Gemma 3 MoE 9B has a context length of 8192 tokens, allowing it to process and generate text with a longer context compared to many other models.
Does Gemma 3 MoE 9B support function calling?
Gemma 3 MoE 9B supports function calling, enabling it to interact with external systems and APIs, enhancing its capabilities for complex tasks.
Gemma 3 MoE 9B quantization options?
Gemma 3 MoE 9B supports various quantization options, including 8-bit and 4-bit quantization, which can reduce the model's memory footprint and improve inference speed without significant loss in performance.
Can Gemma 3 MoE 9B run on CPU?
While Gemma 3 MoE 9B can technically run on a CPU, it is highly inefficient and slow. A GPU with at least 12 GB of VRAM is strongly recommended for practical use.
Gemma 3 MoE 9B fine-tuning?
Gemma 3 MoE 9B can be fine-tuned on specific datasets to improve performance on particular tasks. Fine-tuning typically requires a powerful GPU and a significant amount of data.
Gemma 3 MoE 9B system requirements?
To run Gemma 3 MoE 9B, you need a system with at least 12 GB of GPU VRAM, 32 GB of RAM, and a modern CPU. Additionally, ensure you have the necessary software dependencies installed.
Gemma 3 MoE 9B performance benchmark?
Gemma 3 MoE 9B can process around 100-150 tokens per second on a high-end GPU like the RTX 3090. Performance can vary based on the specific hardware and quantization used.
Gemma 3 MoE 9B for RAG?
Gemma 3 MoE 9B can be used for Retrieval-Augmented Generation (RAG) tasks, leveraging its strong contextual understanding and ability to generate coherent text based on retrieved information.
Gemma 3 MoE 9B for agents?
Gemma 3 MoE 9B is suitable for creating conversational agents due to its large context length and ability to maintain coherent dialogue over extended interactions.
Gemma 3 MoE 9B for coding vs general?
Gemma 3 MoE 9B performs well in both coding and general text generation tasks. However, for specialized coding tasks, models like Codex might offer more tailored performance.
Gemma 3 MoE 9B vs ChatGPT?
Gemma 3 MoE 9B has a larger context length (8192 tokens) and is designed for local deployment, while ChatGPT is a cloud-based service with a smaller context length (2048 tokens). Gemma 3 MoE 9B is better suited for tasks requiring longer context and local execution.
Gemma 3 MoE 9B download size?
The download size for Gemma 3 MoE 9B is approximately 18 GB for the full model, but this can vary depending on the quantization level used.
Best quant for Gemma 3 MoE 9B?
The best quantization for Gemma 3 MoE 9B depends on your specific needs. 8-bit quantization offers a good balance between performance and memory efficiency, while 4-bit quantization further reduces memory usage with a slight trade-off in performance.