DeepSeek-R1 is the open MoE reasoning model that closed the gap with OpenAI o1 — and the first model to publish RL-from-verifiable-reward methodology. With 671 B total / 37 B active parameters, it requires datacenter hardware: ~1340 GB at FP16, ~370 GB at Q4_K_M. Practically, an 8x H100 or 8x B200 server is the floor; some users run Q4 across 8x RTX 6000 Ada (384 GB total) but throughput is constrained.
DeepSeek-R1 671B needs at minimum 406 GB of VRAM at Q4_K_M quantization (1476.2 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the NVIDIA GH200 Grace Hopper (576 GB VRAM, $65,000 MSRP). Community benchmark submissions are open. This model exceeds 48 GB at Q4, so plan for a 2-or-more-GPU split.
Weights only. Add ~20-30% for KV cache at typical context lengths.
| Quant | VRAM | Quality | Notes |
|---|---|---|---|
| FP16 | 1476.2 GB | Reference | Training-precision weights |
| Q8_0 | 738.1 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 597.9 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 509.3 GB | High | Strong middle ground |
| Q4_K_M | 406.0 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 415.2 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 317.4 GB | Lossy | When VRAM is very tight |
| Q2_K | 236.2 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 1476.2 GB; Q4_K_M needs 406.0 GB.
| GPU | Per-card VRAM | 2x | 4x | 8x |
|---|---|---|---|---|
| AMD Instinct MI355X [VERIFY] | 288 GB | 576 | 1152 | 2304 |
| AMD Instinct MI325X | 256 GB | 512 | 1024 | 2048 |
| Apple Mac Studio (M3 Ultra, 256 GB) | 256 GB | 512 | 1024 | 2048 |
| AMD Instinct MI300X | 192 GB | 384 | 768 | 1536 |
| Apple Mac Studio (M2 Ultra, 192 GB) | 192 GB | 384 | 768 | 1536 |
| Apple Mac Pro (M2 Ultra, 192 GB) | 192 GB | 384 | 768 | 1536 |
We don't have community benchmarks for this exact model yet.
Official weights + popular runtime tags.
ollama pull deepseek-r1:671bStep-by-step guides that use this model.
Models in a similar size or capability class.
Use these in papers, blog posts, or internal docs.
@misc{deepseek-r1-671b-2025,
title={DeepSeek-R1 671B},
author={DeepSeek AI},
year={2025},
url={https://huggingface.co/deepseek-ai/DeepSeek-R1}
}DeepSeek AI (2025). DeepSeek-R1 671B [Model card]. Hugging Face. https://huggingface.co/deepseek-ai/DeepSeek-R1
DeepSeek-R1 671B (DeepSeek R1, DeepSeek AI, 2025). Available at https://huggingface.co/deepseek-ai/DeepSeek-R1.