What GPU do I need to run deepseek-ai/DeepSeek-V2-Chat?
235.7B parameters, published in BF16. View on Hugging Face
Set up DeepSeek-V2-ChatDeepSeek-V2-Chat is published by deepseek-ai on Hugging Face, with 17,806 downloads and 462 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 439.1 GB | 526.9 GB | RTX PRO 6000 | 6 | $8.25/hr |
| FP8 (quantized) | 219.6 GB | 263.5 GB | RTX 4090 | 6 | $2.64/hr |
| INT4 (quantized) | 109.8 GB | 131.7 GB | RTX A5000 | 6 | $1.06/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-V2-Chat at its published (BF16) precision: 6× RTX PRO 6000, at $1.38/hr per GPU ($8.25/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
DeepSeek-V2-Chat: common questions
Can DeepSeek-V2-Chat run on a single GPU?
No. At BF16 it needs 526.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 95.0 GB RTX PRO 6000, and it takes 6 of them.
How many GPUs do I need to run DeepSeek-V2-Chat?
6 at BF16. It needs 526.9 GB of VRAM and the cheapest capable live offer is a 95.0 GB RTX PRO 6000, so 6 of them come to $8.25/hr in total.
Does quantizing DeepSeek-V2-Chat lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 RTX PRO 6000 cards at $8.25/hr. At INT4 (quantized) it drops to 6 RTX A5000 cards at $1.06/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 V2 models
- DeepSeek-V2 (235.7B, BF16)
- DeepSeek-V2.5-1210-FP8 (235.7B, F8_E4M3)
- DeepSeek-V2-Lite-Chat (15.7B, BF16)
- DeepSeek-V2-Lite (15.7B, BF16)
Related reading: RTX PRO 6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
