What GPU do I need to run deepseek-ai/deepseek-coder-6.7b-instruct?
A 6.7B model tuned for code generation. 6.7B parameters, published in BF16. View on Hugging Face
deepseek-coder-6.7b-instruct is published by deepseek-ai on Hugging Face, with 396,087 downloads and 509 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
What deepseek-coder-6.7b-instruct is
deepseek-coder-6.7b-instruct is a 6.7B-parameter language model published by DeepSeek on Hugging Face. It is tuned specifically for code generation and completion. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from deepseek-coder-6.7b-instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Code generation and completion
- Code review / refactoring assistants
- IDE copilots
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 12.6 GB | 15.1 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 6.3 GB | 7.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.1 GB | 3.8 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run deepseek-coder-6.7b-instruct at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
deepseek-coder-6.7b-instruct: common questions
Does deepseek-coder-6.7b-instruct fit on a 16 GB GPU?
Yes. At BF16 it needs 15.1 GB of VRAM, so a 16 GB card holds it with 0.9 GB to spare. A 12 GB card is not enough for it at BF16.
What is the least VRAM deepseek-coder-6.7b-instruct can run in?
3.8 GB, at INT4 (quantized), which fits a 6 GB card, against 15.1 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
How to run deepseek-coder-6.7b-instruct
Run deepseek-coder-6.7b-instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve deepseek-ai/deepseek-coder-6.7b-instruct --tensor-parallel-size 1Run deepseek-coder-6.7b-instruct with Ollama
Verified against Ollama's own library listing.
ollama run deepseek-coder:6.7bRun deepseek-coder-6.7b-instruct with GGUF quantizations
Prebuilt GGUF weights published at TheBloke/deepseek-coder-6.7B-instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf TheBloke/deepseek-coder-6.7B-instruct-GGUFSource: https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF
Deploy deepseek-coder-6.7b-instruct on Aquanode
Aquanode has no one-click deploy template for deepseek-coder-6.7b-instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 5060 Ti or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More DeepSeek Coder models
- deepseek-coder-6.7b-base (6.7B, BF16)
- deepseek-coder-7b-instruct-v1.5 (6.9B, BF16)
- deepseek-coder-1.3b-instruct (1.3B, BF16)
- DeepSeek-Coder-V2-Lite-Instruct (15.7B, BF16)
- DeepSeek-Coder-V2-Lite-Instruct-FP8 (15.7B, F8_E4M3)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
