Code
DeepSeek Coder 6.7B
DeepSeek Coder 6.7B is a smaller coding model useful for local and lower-resource developer workflow tests.
DeepSeek · DeepSeek
Editorial review
Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.
Best for
Local coding experiments and lightweight code generation baselines.
Who should use it
- Local coding experiments and lightweight code generation baselines.
- Builders who want local or self-hosted testing options.
- Developers evaluating coding assistant, repo-editing, and code review workflows.
Common workflows
- Smaller coding assistant workflows
- coding workflows
- local workflows
- legacy workflows
Deployment and hardware notes
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
License and usage notes
DeepSeek License. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.
Strengths
- Open weights where released model option for DeepSeek workflows.
- Local coding experiments and lightweight code generation baselines.
- Use the exact checkpoint and quantization that matches your hardware and latency target.
Limitations
- Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
- Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
- Context window and limits: 16,384 tokens.
- Verify the exact model card, provider docs, license, and serving support before production use.
Local workflow notes
Use the exact checkpoint and quantization that matches your hardware and latency target.
Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers
Platforms: Windows, macOS, Linux
Will DeepSeek Coder 6.7B run on your machine?
DeepSeek Coder 6.7B is 6.7B parameters and needs 5.9 GB of VRAM at Q4_K_M — 4.4 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.
VRAM by quantization
| Quantization | Weights | Needs (with overhead) | Quality |
|---|---|---|---|
| Q4_K_M | 4.4 GB | 5.9 GB | good |
| Q8_0 | 7.4 GB | 8.9 GB | high |
| FP16 | 13.5 GB | 15 GB | reference |
Fit on common hardware at Q4_K_M
| Hardware | Memory the model can use | System RAM | Verdict |
|---|---|---|---|
| CPU Only | None (CPU only) | 16 GB | CPU offload |
| RTX 4060 Laptop | 8 GB | 16 GB | Comfortable |
| RTX 3060 (12GB) | 12 GB | 32 GB | Comfortable |
| RTX 4060 Ti (16GB) | 16 GB | 32 GB | Comfortable |
Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.
Need more hardware for DeepSeek Coder 6.7B? Open the PC Builder for the 7B / 8B tier →
VRAM fit by quantization level
Enter your GPU VRAM below to see which quantization of DeepSeek Coder 6.7B fits and get the Ollama run command.
Sources to verify
Related resources
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Setup and deployment
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