StarCoder2 7B by BigCode is a robust code generation model designed to assist developers with writing high-quality code across various programming languages. With 7 billion parameters, it excels in generating coherent and contextually relevant code snippets, making it particularly useful for tasks such as code completion, bug fixing, and even generating entire functions or classes based on natural language prompts. The model's impressive context length of 16,384 tokens allows it to maintain a deep understanding of the codebase, which is crucial for more complex projects.
Compared to other models in its size class, StarCoder2 7B punches well above its weight. It offers a good balance between performance and efficiency, requiring only 4.7 to 7.6 GB of VRAM, which makes it accessible for deployment on a wide range of hardware, including consumer-grade GPUs. This efficiency, combined with its strong performance in code generation, makes it a compelling choice for developers looking to enhance their productivity without the need for high-end hardware. Ideal users include software engineers, data scientists, and hobbyists who want to streamline their coding process and improve code quality. Realistic hardware for running this model includes modern laptops and desktops with at least 8 GB of RAM and a GPU with the specified VRAM range.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.155 GB | 4.66 GB | 5.16 GB | 85% |
| Q8_0 | 8 | 7.105 GB | 7.61 GB | 8.11 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: 16K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run StarCoder2 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 starcoder2:7b - 2
Chat
ollama run starcoder2:7b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"starcoder2:7b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running StarCoder2 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 StarCoder2 7Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
5.8 GB
4.7 GB weights + 0.7 GB KV
Aggregate tok/s
36
across 1 user
Per-user tok/s
36
7 B dense
✅ Fits in 24 GB VRAM with 18.2 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 7B?
StarCoder2 7B requires 4.66 GB VRAM minimum with Q4_K_M quantization. For full precision you need 7.61 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 7B?
To run StarCoder2 7B, you need a GPU with at least 4.7 GB of VRAM for the lowest quantization level, and up to 7.6 GB for higher precision levels.
Is StarCoder2 7B good for coding?
Yes, StarCoder2 7B is specifically designed for coding tasks and offers better completions compared to smaller models, making it a strong choice for developers.
StarCoder2 7B vs Llama 3.1 8B?
StarCoder2 7B is optimized for coding tasks, while Llama 3.1 8B is more general-purpose. StarCoder2 7B has a larger context length of 16384 tokens, which is beneficial for complex coding tasks.
Can I run StarCoder2 7B on a Mac?
Yes, you can run StarCoder2 7B on a Mac, but you will need a compatible GPU with sufficient VRAM and the necessary drivers installed.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B requires between 4.7 GB and 7.6 GB of VRAM, depending on the quantization level used.
Is StarCoder2 7B censored?
No, StarCoder2 7B is not censored, but it adheres to the bigcode-openrail-m license, which includes guidelines for responsible use.
Is StarCoder2 7B commercial-use allowed?
Yes, StarCoder2 7B can be used commercially, but you must comply with the terms of the bigcode-openrail-m license, which includes restrictions on certain uses.
StarCoder2 7B context length?
StarCoder2 7B has a context length of 16384 tokens, which is significantly longer than many other models and allows for more complex code generation and understanding.
Does StarCoder2 7B support function calling?
Yes, StarCoder2 7B supports function calling, which is essential for generating and executing code snippets effectively.
StarCoder2 7B quantization options?
StarCoder2 7B supports various quantization options, including 4-bit, 8-bit, and full precision, allowing you to balance between performance and resource usage.
Can StarCoder2 7B run on CPU?
Yes, StarCoder2 7B can run on CPU, but it will be significantly slower compared to running on a GPU due to the model's size and complexity.
StarCoder2 7B fine-tuning?
Yes, StarCoder2 7B can be fine-tuned on your own data to improve its performance on specific tasks or domains.
StarCoder2 7B system requirements?
To run StarCoder2 7B, you need a system with at least 4.7 GB of VRAM, 16 GB of RAM, and a modern CPU. A high-performance GPU is recommended for optimal performance.
StarCoder2 7B performance benchmark?
StarCoder2 7B typically processes around 50-100 tokens per second on a high-end GPU, with performance varying based on the specific hardware and quantization level used.
StarCoder2 7B for RAG?
Yes, StarCoder2 7B can be used for Retrieval-Augmented Generation (RAG) to enhance code generation by incorporating external information.
StarCoder2 7B for agents?
Yes, StarCoder2 7B can be integrated into agents for tasks such as code generation, debugging, and automated testing.
StarCoder2 7B for coding vs general?
StarCoder2 7B is optimized for coding tasks, offering better performance and accuracy in generating and completing code compared to general-purpose models.
StarCoder2 7B vs ChatGPT?
StarCoder2 7B is specialized for coding tasks, while ChatGPT is a more general-purpose language model. StarCoder2 7B excels in code generation and completion, whereas ChatGPT is better suited for a wide range of natural language tasks.
StarCoder2 7B download size?
The download size for StarCoder2 7B varies depending on the quantization level, ranging from approximately 3.5 GB for 4-bit quantization to 14 GB for full precision.
Best quant for StarCoder2 7B?
The best quantization level for StarCoder2 7B depends on your hardware and performance needs. 8-bit quantization offers a good balance between speed and accuracy, while 4-bit is more resource-efficient but may have slightly lower performance.