CodeGemma 7B is a robust code generation model developed by Google, designed to assist developers in generating high-quality code snippets and completing complex coding tasks. With 8.5 billion parameters, this model offers a balance between performance and resource requirements, making it a versatile choice for both professional and hobbyist developers. It excels in generating contextually relevant and syntactically correct code across multiple programming languages, thanks to its impressive context length of 8192 tokens. This allows the model to maintain a broader understanding of the codebase, which is particularly useful for larger projects or when working with intricate code structures.
In its size class, CodeGemma 7B punches well above its weight, offering performance that rivals larger models while being more efficient in terms of memory usage and computational requirements. The available quantizations, Q4_K_M and Q8_0, further enhance its efficiency, making it suitable for deployment on a wide range of hardware, including systems with 5.5 to 8.9 GB of VRAM. This makes it an excellent choice for developers who need powerful code generation capabilities but may be limited by their hardware resources. Whether you're a seasoned developer looking to speed up your workflow or a beginner seeking guidance, CodeGemma 7B is a solid choice that delivers reliable and efficient code generation.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.964 GB | 5.46 GB | 5.96 GB | 85% |
| Q8_0 | 8 | 8.454 GB | 8.95 GB | 9.45 GB | 98% |
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 CodeGemma 7B
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 codegemma:7b - 2
Chat
ollama run codegemma:7b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"codegemma:7b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running CodeGemma 7B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host CodeGemma 7Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
6.7 GB
5.5 GB weights + 0.7 GB KV
Aggregate tok/s
29
across 1 user
Per-user tok/s
29
8.5 B dense
✅ Fits in 24 GB VRAM with 17.3 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 CodeGemma 7B?
CodeGemma 7B requires 5.46 GB VRAM minimum with Q4_K_M quantization. For full precision you need 8.95 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 CodeGemma 7B?
To run CodeGemma 7B, you need a GPU with at least 5.5 GB of VRAM for the lowest quantization level, up to 8.9 GB for higher precision levels.
Is CodeGemma 7B good for coding?
Yes, CodeGemma 7B is specifically designed for code generation and understanding, making it highly effective for coding tasks.
CodeGemma 7B vs Llama 3.1 8B?
CodeGemma 7B is optimized for code-related tasks, while Llama 3.1 8B is more general-purpose. CodeGemma 7B has a larger context length of 8192 tokens compared to Llama 3.1 8B's 2048 tokens.
Can I run CodeGemma 7B on a Mac?
Yes, you can run CodeGemma 7B on a Mac with a compatible GPU and sufficient VRAM. Ensure your Mac meets the minimum VRAM requirements and has the necessary drivers installed.
How much VRAM does CodeGemma 7B need?
CodeGemma 7B requires between 5.5 GB and 8.9 GB of VRAM, depending on the quantization level used.
Is CodeGemma 7B censored?
No, CodeGemma 7B is not censored. However, it adheres to ethical guidelines and may have content filters to prevent harmful outputs.
Is CodeGemma 7B commercial-use allowed?
Yes, CodeGemma 7B is licensed under the Gemma license, which allows commercial use as long as you comply with the terms of the license.
CodeGemma 7B context length?
CodeGemma 7B has a context length of 8192 tokens, allowing it to handle longer sequences of code or text.
Does CodeGemma 7B support function calling?
Yes, CodeGemma 7B supports function calling, enabling it to generate and understand complex code structures.
CodeGemma 7B quantization options?
CodeGemma 7B supports various quantization options, including 4-bit, 8-bit, and 16-bit, which affect the model's size and performance.
Can CodeGemma 7B run on CPU?
While CodeGemma 7B can run on a CPU, it will be significantly slower compared to running on a GPU due to its large size and computational requirements.
CodeGemma 7B fine-tuning?
Yes, CodeGemma 7B can be fine-tuned on custom datasets to improve its performance on specific tasks or domains.
CodeGemma 7B system requirements?
CodeGemma 7B requires a GPU with 5.5 GB to 8.9 GB of VRAM, at least 16 GB of RAM, and a multi-core CPU. It also needs a modern operating system and compatible drivers.
CodeGemma 7B performance benchmark?
Performance benchmarks for CodeGemma 7B vary, but it typically processes around 100-200 tokens per second on a high-end GPU, depending on the quantization level and batch size.
CodeGemma 7B for RAG?
Yes, CodeGemma 7B can be used for Retrieval-Augmented Generation (RAG) to enhance its code generation capabilities by incorporating external knowledge sources.
CodeGemma 7B for agents?
CodeGemma 7B can be integrated into AI agents to provide code generation and understanding capabilities, enhancing the agent's functionality in coding environments.
CodeGemma 7B for coding vs general?
CodeGemma 7B is specialized for coding tasks, offering better performance and accuracy in generating and understanding code compared to general-purpose models.
CodeGemma 7B vs ChatGPT?
CodeGemma 7B is specifically tuned for code-related tasks, while ChatGPT is a general-purpose language model. CodeGemma 7B excels in generating and understanding code, whereas ChatGPT is better for a wide range of natural language tasks.
CodeGemma 7B download size?
The download size of CodeGemma 7B varies depending on the quantization level, ranging from approximately 4.25 GB (4-bit quant) to 17 GB (16-bit quant).
Best quant for CodeGemma 7B?
The best quantization level for CodeGemma 7B depends on your hardware and performance needs. 8-bit quantization offers a good balance between size and performance, while 4-bit is more efficient but may sacrifice some accuracy.