What GPU do I need to run deepseek-ai/DeepSeek-R1-Distill-Llama-70B?

70.6B
Parameters
BF16
Native precision
LlamaForCausalLM
Architecture
text-generation
Pipeline

DeepSeek-R1-Distill-Llama-70B is published by deepseek-ai on Hugging Face, with 94,032 downloads and 798 likes to date. It's a LlamaForCausalLM 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
131.4 GB
157.7 GB
RTX 3090 (akash)
7
$1.03/hr
FP8 (quantized)
65.7 GB
78.8 GB
1
$1.33/hr
cheaper alt.
RTX 4070 (simplepod)
7
$0.630/hr
INT4 (quantized)
32.9 GB
39.4 GB
RTX 8000 (akash)
1
$0.221/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-R1-Distill-Llama-70B at its published (BF16) precision: 7× RTX 3090 on akash, at $0.147/hr per GPU ($1.03/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

DeepSeek-R1-Distill-Llama-70B: common questions

Can DeepSeek-R1-Distill-Llama-70B run on a single GPU?

No. At BF16 it needs 157.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX 3090, and it takes 7 of them.

How many GPUs do I need to run DeepSeek-R1-Distill-Llama-70B?

7 at BF16. It needs 157.7 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX 3090 on akash, so 7 of them come to $1.03/hr in total.

Does quantizing DeepSeek-R1-Distill-Llama-70B lower the GPU bill?

Yes. At BF16 the cheapest live fit is 7 RTX 3090 cards on akash at $1.03/hr. At INT4 (quantized) it drops to one RTX 8000 on akash at $0.221/hr, provided a quantized checkpoint exists for it.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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