What GPU do I need to run deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct?
15.7B parameters, published in BF16. View on Hugging Face
Set up DeepSeek-Co…V2-Lite-InstructDeepSeek-Coder-V2-Lite-Instruct is published by deepseek-ai on Hugging Face, with 717,765 downloads and 648 likes to date. It's a DeepseekV2ForCausalLM model built for text-generation, published natively in BF16.
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.
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-V2-Lite-Instruct at its published (BF16) precision: 1× RTX A6000 on runpod, at $0.330/hr per GPU ($0.330/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-V2-Lite-Instruct: common questions
Can DeepSeek-Coder-V2-Lite-Instruct run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 35.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 on runpod at $0.330/hr.
What is the least VRAM DeepSeek-Coder-V2-Lite-Instruct can run in?
8.8 GB, at INT4 (quantized), which fits a 12 GB card, against 35.1 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.
Does quantizing DeepSeek-Coder-V2-Lite-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A6000 on runpod at $0.330/hr. At INT4 (quantized) it drops to one RTX 3080 on simplepod at $0.070/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More deepseek-ai models
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- DeepSeek-V3.2 (685.4B, F8_E4M3)
- DeepSeek-V3-0324 (684.5B, F8_E4M3)
- DeepSeek-V3 (684.5B, F8_E4M3)
- DeepSeek-OCR-2 (3.4B, BF16)
