CodeGemma 2B is a robust code generation model developed by Google, designed to assist developers with writing and generating high-quality code snippets. With 2 billion parameters, this model offers a significant capacity for understanding complex programming tasks and generating contextually relevant code. The model's architecture, based on the gemma framework, supports a context length of 8192 tokens, allowing it to handle extensive codebases and maintain coherence over longer sequences. This makes it particularly useful for tasks like completing functions, generating documentation, and even suggesting optimizations.
In its size class, CodeGemma 2B stands out for its efficiency and performance. Despite having fewer parameters than some larger models, it manages to deliver impressive results, often outperforming its peers in terms of code quality and relevance. The model is available in quantized versions (Q4_K_M and Q8_0), which significantly reduce the VRAM requirements, making it feasible to run on systems with as little as 2.0–3.0 GB of VRAM. This accessibility means that developers with mid-range hardware can still leverage its capabilities without needing high-end GPUs.
CodeGemma 2B is ideal for software developers, especially those working on projects that require frequent code generation or optimization. It is also suitable for educational purposes, helping students and beginners understand and practice coding more effectively. Realistically, the model can be deployed on a wide range of hardware, from laptops with integrated graphics to more powerful desktops, making it a versatile tool for both professional and personal use.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 1.518 GB | 2.02 GB | 2.52 GB | 85% |
| Q8_0 | 8 | 2.486 GB | 2.99 GB | 3.49 GB | 98% |
Context window & KV cache
Adds 0.17 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 2B
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:2b - 2
Chat
ollama run codegemma:2b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"codegemma:2b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running CodeGemma 2B 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 2Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
2.9 GB
2.0 GB weights + 0.4 GB KV
Aggregate tok/s
125
across 1 user
Per-user tok/s
125
2 B dense
✅ Fits in 24 GB VRAM with 21.1 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 2B?
CodeGemma 2B requires 2.02 GB VRAM minimum with Q4_K_M quantization. For full precision you need 2.99 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 2B?
To run CodeGemma 2B, you need a GPU with at least 2.0 GB to 3.0 GB of VRAM, depending on the quantization level. For optimal performance, a GPU with 4 GB or more VRAM is recommended.
Is CodeGemma 2B good for coding?
Yes, CodeGemma 2B is specifically designed for code completion and provides fast, on-device suggestions, making it highly effective for coding tasks.
CodeGemma 2B vs Llama 3.1 8B?
CodeGemma 2B has 2 billion parameters and is optimized for lightweight, fast code completion, while Llama 3.1 8B is larger with 8 billion parameters, offering more comprehensive language understanding but requiring more resources.
Can I run CodeGemma 2B on a Mac?
Yes, CodeGemma 2B can run on a Mac as long as your system meets the minimum VRAM requirements of 2.0 GB to 3.0 GB, depending on the quantization level.
How much VRAM does CodeGemma 2B need?
CodeGemma 2B requires between 2.0 GB and 3.0 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require less VRAM.
Is CodeGemma 2B censored?
No, CodeGemma 2B is not censored. It is designed to provide uncensored, fast code suggestions, but it adheres to ethical guidelines and best practices.
Is CodeGemma 2B commercial-use allowed?
Yes, CodeGemma 2B is licensed under the Gemma license, which allows for commercial use, provided you comply with the terms of the license.
CodeGemma 2B context length?
CodeGemma 2B has a context length of 8192 tokens, allowing it to understand and generate longer sequences of code.
Does CodeGemma 2B support function calling?
Yes, CodeGemma 2B supports function calling, enabling it to generate and complete code that includes function calls and other complex structures.
CodeGemma 2B quantization options?
CodeGemma 2B supports various quantization options, including 4-bit, 8-bit, and 16-bit quantization, which can reduce the model size and VRAM requirements while maintaining performance.
Can CodeGemma 2B run on CPU?
Yes, CodeGemma 2B can run on a CPU, but it will be significantly slower compared to running on a GPU. A powerful multi-core CPU is recommended for better performance.
CodeGemma 2B fine-tuning?
CodeGemma 2B can be fine-tuned on custom datasets to improve its performance on specific coding tasks or domains. Fine-tuning requires a dataset and training infrastructure.
CodeGemma 2B system requirements?
To run CodeGemma 2B, you need a system with at least 8 GB of RAM, a GPU with 2.0 GB to 3.0 GB of VRAM, and a multi-core CPU. More resources will yield better performance.
CodeGemma 2B performance benchmark?
CodeGemma 2B can process around 50-100 tokens per second on a mid-range GPU, making it suitable for real-time code suggestions. Performance can vary based on hardware and quantization level.
CodeGemma 2B for RAG?
CodeGemma 2B can be used for Retrieval-Augmented Generation (RAG) in coding contexts, where it retrieves relevant code snippets and generates code based on them.
CodeGemma 2B for agents?
CodeGemma 2B can be integrated into coding agents to provide real-time code suggestions and completions, enhancing the productivity of developers.
CodeGemma 2B for coding vs general?
CodeGemma 2B is optimized for coding tasks and provides specialized code completion, whereas general-purpose models like GPT-3 are designed for a broader range of language tasks.
CodeGemma 2B vs ChatGPT?
CodeGemma 2B is specifically designed for code completion and is smaller with 2 billion parameters, while ChatGPT is a general-purpose model with more parameters and broader language capabilities.
CodeGemma 2B download size?
The download size of CodeGemma 2B varies depending on the quantization level. The 4-bit quantized version is approximately 1 GB, while the 16-bit version is around 4 GB.
Best quant for CodeGemma 2B?
The best quantization for CodeGemma 2B depends on your hardware. For most systems, 8-bit quantization offers a good balance between performance and resource usage, while 4-bit is ideal for lower-end hardware.