Stable Code 3B by Stability AI is a 3 billion parameter model designed for code generation tasks, leveraging the stablelm architecture. This model stands out with its impressive context length of 16,384 tokens, which allows it to handle complex and lengthy programming tasks, making it particularly useful for generating, completing, and debugging code. It is well-suited for developers and software engineers who need a powerful tool to assist with coding projects, especially those involving large codebases or intricate logic.
In its size class, Stable Code 3B holds its own, offering a balance between performance and efficiency. While it may not have the highest parameter count compared to some larger models, it punches above its weight in terms of code quality and context handling. The model is available in quantized versions (Q4_K_M, Q8_0), which significantly reduce the VRAM requirements to 2.1–3.3 GB, making it accessible on a wide range of hardware, including mid-range GPUs. This makes it an excellent choice for users who want high-quality code generation without the need for top-tier hardware. Developers with moderate GPU capabilities can confidently deploy this model for local use, ensuring a smooth and efficient coding experience.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 1.591 GB | 2.09 GB | 2.59 GB | 85% |
| Q8_0 | 8 | 2.769 GB | 3.27 GB | 3.77 GB | 98% |
Context window & KV cache
Adds 0.66 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 16K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Stable Code 3B
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 stable-code:3b - 2
Chat
ollama run stable-code:3b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"stable-code:3b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Stable Code 3B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Stable Code 3Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.0 GB
2.1 GB weights + 0.4 GB KV
Aggregate tok/s
83
across 1 user
Per-user tok/s
83
3 B dense
✅ Fits in 24 GB VRAM with 21.0 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 Stable Code 3B?
Stable Code 3B requires 2.09 GB VRAM minimum with Q4_K_M quantization. For full precision you need 3.27 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 Stable Code 3B?
To run Stable Code 3B, you need a GPU with at least 2.1 GB of VRAM, but 3.3 GB is recommended for better performance, especially with higher quantization levels.
Is Stable Code 3B good for coding?
Yes, Stable Code 3B is designed specifically for coding tasks and offers good completion quality, making it suitable for generating and completing code snippets.
Stable Code 3B vs Llama 3.1 8B?
Stable Code 3B has 3 billion parameters, making it smaller than Llama 3.1 8B, which has 8 billion parameters. Stable Code 3B is more lightweight and requires less VRAM, but may have slightly lower performance in complex tasks.
Can I run Stable Code 3B on a Mac?
Yes, you can run Stable Code 3B on a Mac, provided your Mac has a compatible GPU with at least 2.1 GB of VRAM. Ensure you have the necessary drivers and software installed.
How much VRAM does Stable Code 3B need?
Stable Code 3B requires between 2.1 GB and 3.3 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM for optimal performance.
Is Stable Code 3B censored?
Stable Code 3B is not explicitly censored, but it adheres to ethical guidelines and may filter out inappropriate or harmful content during inference.
Is Stable Code 3B commercial-use allowed?
The license for Stable Code 3B allows for commercial use, but you should review the specific terms of the license to ensure compliance with any conditions or restrictions.
Stable Code 3B context length?
Stable Code 3B has a context length of 16,384 tokens, which is quite large and allows for handling extensive code contexts and longer sequences.
Does Stable Code 3B support function calling?
Yes, Stable Code 3B supports function calling, enabling it to generate and execute code that includes function calls and other programming constructs.
Stable Code 3B quantization options?
Stable Code 3B supports various quantization options, including 8-bit, 4-bit, and mixed precision, which can help reduce memory usage and improve performance on lower-end hardware.
Can Stable Code 3B run on CPU?
Yes, Stable Code 3B can run on CPU, but it will be significantly slower compared to running on a GPU. Consider using quantization to optimize performance on CPU.
Stable Code 3B fine-tuning?
Stable Code 3B can be fine-tuned on custom datasets to improve its performance on specific coding tasks or domains. Fine-tuning typically requires a powerful GPU and sufficient training data.
Stable Code 3B system requirements?
To run Stable Code 3B, you need a system with at least 8 GB of RAM, a GPU with 2.1 GB to 3.3 GB of VRAM, and a modern CPU. Ensure you have the latest drivers and CUDA toolkit installed.
Stable Code 3B performance benchmark?
Performance benchmarks for Stable Code 3B show it can process around 50-100 tokens per second on a mid-range GPU, with higher throughput on more powerful hardware.
Stable Code 3B for RAG?
Stable Code 3B can be used for Retrieval-Augmented Generation (RAG) tasks, where it retrieves relevant code snippets and integrates them into the generated output.
Stable Code 3B for agents?
Stable Code 3B can be integrated into coding agents or bots to provide code suggestions, complete functions, and assist with debugging and documentation.
Stable Code 3B for coding vs general?
Stable Code 3B is optimized for coding tasks and may perform better in generating and completing code compared to general-purpose models, which are designed for a wider range of tasks.
Stable Code 3B vs ChatGPT?
Stable Code 3B is specifically designed for coding tasks and has a larger context length, while ChatGPT is a general-purpose language model. Stable Code 3B may offer better performance for coding-specific tasks.
Stable Code 3B download size?
The download size for Stable Code 3B is approximately 6 GB for the full model, but this can vary depending on the quantization level used.
Best quant for Stable Code 3B?
The best quantization for Stable Code 3B depends on your hardware. For most users, 8-bit quantization provides a good balance between performance and memory usage, while 4-bit quantization can further reduce memory requirements.