StarCoder 2 15B

Positioning

StarCoder 2 15B is the flagship model from BigCode, a collaboration between Hugging Face and ServiceNow. It is a dense 15-billion-parameter transformer specialized for code generation, released under the permissive BigCode OpenRAIL-M license. With a 16,384-token context window and strong fill-in-middle (FIM) capability, it is designed for code completion and infilling tasks. Its dense architecture means all 15B parameters are active during inference, making it a straightforward choice for local deployment on consumer hardware.

Strengths

• Permissive license for commercial use: The BigCode OpenRAIL-M license allows commercial deployment, making it suitable for enterprise coding assistants.
• Dense architecture with full parameter utilization: Unlike mixture-of-experts models, all 15B parameters are active, providing consistent performance across all inputs without routing overhead.
• Optimized for code infilling: Strong fill-in-middle support makes it ideal for code completion in IDEs and developer tools.
• Consumer-grade deployment: With Q4_K_M quant at ~8.4 GB on disk, it fits comfortably on a single 12-24 GB GPU, enabling local use without specialized hardware.

Limitations

• 16K context window: While adequate for many code files, it may be insufficient for very large codebases or long-range dependencies compared to models with 32K+ context.
• No multimodal capabilities: This model is text-only and cannot process images or other modalities.
• We don't yet have community-reported benchmarks for this model: Operators considering it should treat published vendor metrics as best-case and verify performance on their own workloads.
• Dense 15B parameters require more compute per token than smaller models: While quantized versions reduce memory, inference speed is inherently slower than smaller dense models (e.g., 7B) on the same hardware.

What it takes to run this locally

At FP16, the model requires ~30 GB on disk, exceeding most consumer GPUs. Quantization is essential for local use:

• Q8_0: ~16 GB (fits on 24 GB GPUs with ~8 GB overhead for KV cache)
• Q6_K: ~12.4 GB (fits on 16 GB GPUs with ~3.6 GB overhead)
• Q5_K_M: ~10.7 GB (fits on 16 GB GPUs with ~5.3 GB overhead)
• Q4_K_M: ~8.4 GB (fits on 12 GB GPUs with ~3.6 GB overhead)
• Q3_K_M: ~7.3 GB (fits on 12 GB GPUs with ~4.7 GB overhead)
• Q2_K: ~4.9 GB (fits on 8 GB GPUs with ~3.1 GB overhead)

Add ~30-50% for KV cache and framework overhead at typical context lengths. Deployment class: consumer (single 12-24 GB GPU).

Should you run this locally?

Yes if: You need a permissively-licensed code model for commercial use, have a consumer GPU with at least 12 GB VRAM, and can accept a 16K context window. The dense architecture ensures predictable behavior without routing complexity.

No if: You require longer context (e.g., >16K tokens), need multimodal capabilities, or prefer a smaller model for faster inference on limited hardware. Also, if community benchmarks are critical for your decision, wait for independent evaluations.

Catalog cross-links

• StarCoder 2 7B
• Code Llama 13B
• DeepSeek Coder 33B