StarCoder2 3B by BigCode is a robust code generation model designed for local deployment, offering a balance between performance and resource requirements. With 3 billion parameters, this model excels at generating high-quality code snippets, completing code tasks, and providing context-aware suggestions. Its architecture supports a context length of 16,384 tokens, making it particularly adept at handling complex and lengthy coding projects. The model is licensed under the bigcode-openrail-m license, ensuring accessibility while maintaining ethical usage guidelines. StarCoder2 3B has gained significant popularity, with over 91,957 downloads and 216 likes, indicating its reliability and effectiveness in the developer community.
In its size class, StarCoder2 3B punches well above its weight. Despite having fewer parameters compared to larger models, it delivers impressive results in code generation tasks, often matching or exceeding the performance of more resource-intensive models. This efficiency makes it an excellent choice for developers and organizations looking to deploy a powerful code generation tool without the need for high-end hardware. The model is available in quantized versions (Q4_K_M and Q8_0), which further optimize its performance and reduce VRAM requirements to a range of 2.3–3.5 GB. This makes it feasible for use on a wide range of devices, from mid-range laptops to more powerful workstations. Ideal users include software developers, data scientists, and anyone involved in coding who needs a reliable, locally deployable AI assistant.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 1.758 GB | 2.26 GB | 2.76 GB | 85% |
| Q8_0 | 8 | 3.003 GB | 3.5 GB | 4 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 StarCoder2 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 starcoder2:3b - 2
Chat
ollama run starcoder2:3b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"starcoder2:3b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running StarCoder2 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 StarCoder2 3Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.2 GB
2.3 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 20.8 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 StarCoder2 3B?
StarCoder2 3B requires 2.26 GB VRAM minimum with Q4_K_M quantization. For full precision you need 3.5 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 StarCoder2 3B?
To run StarCoder2 3B, you need a GPU with at least 2.3 GB of VRAM for the lowest quantization level, but 3.5 GB is recommended for better performance.
Is StarCoder2 3B good for coding?
Yes, StarCoder2 3B is specifically trained on The Stack v2 and supports over 600 programming languages, making it highly effective for code completion and generation tasks.
StarCoder2 3B vs Llama 3.1 8B?
StarCoder2 3B is smaller with 3 billion parameters and focuses on code, while Llama 3.1 8B has more parameters and is more versatile but less specialized in coding.
Can I run StarCoder2 3B on a Mac?
Yes, you can run StarCoder2 3B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM or a powerful CPU for CPU-based inference.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B requires between 2.3 GB and 3.5 GB of VRAM, depending on the quantization level used.
Is StarCoder2 3B censored?
No, StarCoder2 3B is not censored, but it adheres to the bigcode-openrail-m license which includes guidelines for responsible use.
Is StarCoder2 3B commercial-use allowed?
Yes, StarCoder2 3B can be used commercially under the terms of the bigcode-openrail-m license, which allows for commercial use with certain restrictions.
StarCoder2 3B context length?
StarCoder2 3B has a context length of 16,384 tokens, allowing it to handle longer sequences of code effectively.
Does StarCoder2 3B support function calling?
Yes, StarCoder2 3B supports function calling and can generate or complete code that includes function calls and other complex structures.
StarCoder2 3B quantization options?
StarCoder2 3B supports various quantization levels, including 4-bit, 8-bit, and full precision, to optimize for different hardware capabilities and performance needs.
Can StarCoder2 3B run on CPU?
Yes, StarCoder2 3B can run on CPU, but it will be significantly slower compared to GPU inference, especially for larger contexts.
StarCoder2 3B fine-tuning?
Yes, StarCoder2 3B can be fine-tuned on custom datasets to improve its performance on specific coding tasks or domains.
StarCoder2 3B system requirements?
For optimal performance, StarCoder2 3B requires a GPU with 3.5 GB of VRAM, at least 8 GB of RAM, and a multi-core CPU. A powerful CPU is essential for CPU-based inference.
StarCoder2 3B performance benchmark?
StarCoder2 3B can process around 50-100 tokens per second on a mid-range GPU, with higher throughput on more powerful hardware.
StarCoder2 3B for RAG?
While StarCoder2 3B is primarily designed for code, it can be adapted for Retrieval-Augmented Generation (RAG) tasks with additional setup and fine-tuning.
StarCoder2 3B for agents?
StarCoder2 3B can be integrated into coding agents to provide code suggestions, error detection, and automated code generation features.
StarCoder2 3B for coding vs general?
StarCoder2 3B is optimized for coding tasks and may not perform as well on general language tasks compared to models like BERT or RoBERTa.
StarCoder2 3B vs ChatGPT?
StarCoder2 3B is specialized for code and supports over 600 languages, while ChatGPT is a general-purpose language model with broader conversational capabilities.
StarCoder2 3B download size?
The download size for StarCoder2 3B varies depending on the quantization level, ranging from approximately 1.5 GB (4-bit) to 6 GB (full precision).
Best quant for StarCoder2 3B?
The best quantization for StarCoder2 3B depends on your hardware. For most users, 8-bit quantization offers a good balance between performance and resource usage, while 4-bit is suitable for lower-end GPUs.