What GPU do I need to run deepseek-ai/DeepSeek-R1-0528?
A 685B-parameter reasoning model tuned for math, coding and multi-step logic. 684.5B parameters, published in F8_E4M3. View on Hugging Face
DeepSeek-R1-0528 is published by deepseek-ai on Hugging Face, with 204,228 downloads and 2,459 likes to date. It's a DeepseekV3ForCausalLM model built for text-generation, published natively in F8_E4M3.
What DeepSeek-R1-0528 is
DeepSeek-R1-0528 is DeepSeek's reasoning-focused language model: it works through a problem step by step before producing a final answer, rather than answering directly. DeepSeek's own card reports a jump to 87.5% on AIME 2025 (up from 70% on the prior release) along with stronger coding and function-calling results.
License note: permissive: DeepSeek's card states it supports commercial use and distillation into other models. Facts in this section are sourced from DeepSeek-R1-0528's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Multi-step reasoning and math
- Code generation
- Function calling / tool use
- Long-context analysis
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) |
|---|---|---|---|---|---|
| FP8 (native) | 637.5 GB | 765.0 GB | RTX PRO 6000 WS | 8 | $11.98/hr |
| INT4 (quantized) | 318.8 GB | 382.5 GB | RTX A6000 | 8 | $2.90/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-R1-0528 at its published (F8_E4M3) precision: 8× RTX PRO 6000 WS, at $1.50/hr per GPU ($11.98/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
DeepSeek-R1-0528: common questions
Can DeepSeek-R1-0528 run on a single GPU?
No. At FP8 (native) it needs 765.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 96.0 GB RTX PRO 6000 WS, and it takes 8 of them.
Is DeepSeek-R1-0528 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 765.0 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 382.5 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run DeepSeek-R1-0528?
8 at FP8 (native). It needs 765.0 GB of VRAM and the cheapest capable live offer is a 96.0 GB RTX PRO 6000 WS, so 8 of them come to $11.98/hr in total.
Does quantizing DeepSeek-R1-0528 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is 8 RTX PRO 6000 WS cards at $11.98/hr. At INT4 (quantized) it drops to 8 RTX A6000 cards at $2.90/hr, provided a quantized checkpoint exists for it.
How to run DeepSeek-R1-0528
Run DeepSeek-R1-0528 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-R1-0528 --tensor-parallel-size 8Run DeepSeek-R1-0528 with GGUF quantizations
Prebuilt GGUF weights published at unsloth/DeepSeek-R1-0528-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/DeepSeek-R1-0528-GGUFSource: https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF
Deploy DeepSeek-R1-0528 on Aquanode
Aquanode has no one-click deploy template for DeepSeek-R1-0528; 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 (8× RTX PRO 6000 WS 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 R1 models
- DeepSeek-R1 (684.5B, F8_E4M3)
- DeepSeek-R1-Distill-Llama-70B-FP8-dynamic (70.6B, F8_E4M3)
- DeepSeek-R1-Distill-Llama-70B (70.6B, BF16)
- DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic (32.8B, F8_E4M3)
- DeepSeek-R1-Distill-Qwen-32B (32.8B, BF16)
Related reading: RTX PRO 6000 WS 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.
