DeepSeek R1 671B

When to pick this model

• You're running a dedicated inference server and need frontier reasoning
• You want the strongest open math, code, and logic model available
• You need an MIT-licensed model with no commercial restrictions
• You're benchmarking against closed frontier models like o1 or o3

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, DeepSeek R1 671B is server-class even at Q4_K_M (400 GB). Stepping up to Q8_0 nearly doubles the footprint to 720 GB, and unquantized FP16 weights take 1342 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DeepSeek R1 671B needs roughly 512 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 5 tokens/sec on a mid-range card, and up to 15 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DeepSeek R1 671B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run DeepSeek R1 671B?

To run DeepSeek R1 671B locally at Q4, you need ~400 GB for Q4 weights alone. Hardware option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395). This model exceeds the practical GPU memory of this mini PC. Choose a smaller model or larger infrastructure.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put DeepSeek R1 671B in context: its MMLU score of 90.8 ranks #1 of the 34 catalog models with a published MMLU result (catalog median 73.4). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• MIT license — no commercial restrictions
• Reference open reasoning model
• MATH-500 score of 97.3
• R1-0528 update further sharpens reasoning

Limitations

• 400GB+ in Q4 — server-class hardware required
• Out of reach for any single-machine local setup
• Very long reasoning traces drive up latency

Typical workloads

In our catalog grid, DeepSeek R1 671B is filed under Frontier Reasoning, Math/Science — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: MoE (inherited from V3) · Multi-head Latent Attention · auxiliary-loss-free · RL-trained

Training: Distillation + multi-stage RL. R1-0528 update (May 2025).

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

ollama run deepseek-r1:671b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does DeepSeek R1 671B need?

At the recommended Q4_K_M quantization, DeepSeek R1 671B needs about 400 GB of VRAM. Q8_0 takes 720 GB, and unquantized FP16 weights take 1342 GB.

Can DeepSeek R1 671B run without a GPU?

Yes — with roughly 512 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DeepSeek R1 671B support?

DeepSeek R1 671B supports a 125k-token context window (128,000 tokens).

Can I use DeepSeek R1 671B commercially?

Yes. DeepSeek R1 671B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is DeepSeek R1 671B on consumer hardware?

Our compatibility engine estimates on the order of 5 tokens/sec on a mid-range GPU and up to 15 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DeepSeek R1 671B should I download first?

Start with Q4_K_M (400 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.