Yi Coder 9B
01.AI's coding specialization at 9B. Apache 2.0; positioned as a lighter alternative to Qwen 2.5 Coder for the 16GB tier.
Positioning
Yi Coder 9B is a dense 9B-parameter coding model released by 01.AI under the permissive Apache 2.0 license. With a 131K context window, it is designed for code completion, generation, and understanding tasks. It is positioned as a lighter alternative to Qwen 2.5 Coder for the 16GB GPU tier, making it accessible to a broad range of developers.
Strengths
- Permissive Apache 2.0 license: Allows unrestricted commercial use, modification, and redistribution, making it ideal for enterprise deployment.
- Large 131K context window: Supports long code files and multi-file projects without truncation, beneficial for complex coding tasks.
- Consumer-grade deployment: With 9B parameters, it can run on a single 8–16GB GPU at reasonable quantizations, lowering hardware barriers.
- Compact quantized sizes: Q4_K_M at ~5.1 GB and Q3_K_M at ~4.4 GB fit comfortably in 8GB VRAM, enabling local inference on many consumer GPUs.
Limitations
- No community-reported benchmarks available: Published vendor metrics should be treated as best-case; real-world performance may vary.
- Dense architecture: Unlike Mixture-of-Experts models, all 9B parameters are active per token, meaning compute cost scales linearly with parameter count.
- KV cache overhead at full context: At 131K tokens, the KV cache can exceed 10 GB, requiring careful memory management or reduced context length on consumer hardware.
- Niche specialization: As a coding-focused model, it may underperform on general language tasks compared to similarly sized general-purpose models.
What it takes to run this locally
At FP16, the model requires ~18 GB of disk space. Quantized versions reduce this significantly: Q8_0 ~10 GB, Q6_K ~7.4 GB, Q5_K_M ~6.4 GB, Q4_K_M ~5.1 GB, Q3_K_M ~4.4 GB, Q2_K ~2.9 GB. For inference, add ~30–50% for KV cache and framework overhead. A consumer GPU with 8–16 GB VRAM (e.g., RTX 3060/4060, RTX 3080/4080) can run Q4_K_M or Q3_K_M comfortably. For full 131K context, a 16 GB GPU or higher is recommended.
Should you run this locally?
Yes if you need a permissively licensed coding model that fits on a consumer GPU and you value a large context window for code tasks. No if you require a general-purpose model or need to run at full context on an 8 GB GPU without quantization.
Catalog cross-links
- Qwen 2.5 Coder 7B
- DeepSeek Coder 6.7B
- Code Llama 7B
Overview
01.AI's coding specialization at 9B. Apache 2.0; positioned as a lighter alternative to Qwen 2.5 Coder for the 16GB tier.
Strengths
- 9B fits 8GB consumer cards
- Apache 2.0
Weaknesses
- Trails Qwen 2.5 Coder 14B / 32B on benchmarks
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 5.4 GB | 8 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Yi Coder 9B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Yi Coder 9B?
Can I use Yi Coder 9B commercially?
What's the context length of Yi Coder 9B?
Source: huggingface.co/01-ai/Yi-Coder-9B
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Yi Coder 9B runs on your specific hardware before committing money.