DeepSeek R2 32B
By DeepSeek · China
Updated 2026-07-13
Overview
DeepSeek's dense 32B reasoning model under MIT, scoring 92.7% on AIME. Fits on a single RTX 4090 in Q4 and is the best consumer-GPU reasoner available.
When to pick this model
- Math, competition, and STEM reasoning
- Single-GPU production reasoning workloads
- Chain-of-thought research on consumer hardware
- Commercial deployments under MIT
- Replacing closed reasoning APIs on a 4090
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 64 GB |
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 R2 32B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 64 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, DeepSeek R2 32B needs roughly 32 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 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches DeepSeek R2 32B 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.
| GPU memory | Example cards | Best fit for DeepSeek R2 32B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run DeepSeek R2 32B?
To run DeepSeek R2 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| AIME | 92.7 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- 92.7% on AIME, frontier-level math reasoning
- Runs on a single RTX 4090 in Q4
- MIT license with full commercial rights
- Best consumer-GPU reasoner of its generation
Limitations
- Verbose chain-of-thought inflates token costs
- Specialized for reasoning, less polished for chat
- Latency can spike on hard problems
Typical workloads
In our catalog grid, DeepSeek R2 32B is filed under Workstation Reasoning, Advanced 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: Dense 32B · MIT · reasoner
Training: Successor to R1 and R1-Distill.
The best open reasoning model that fits on a single consumer GPU.
Quick start
# HuggingFace : deepseek-ai/DeepSeek-R2 (pas encore de tag Ollama officiel)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 R2 32B need?
At the recommended Q4_K_M quantization, DeepSeek R2 32B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 64 GB.
Can DeepSeek R2 32B run without a GPU?
Yes — with roughly 32 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 R2 32B support?
DeepSeek R2 32B supports a 125k-token context window (128,000 tokens).
Can I use DeepSeek R2 32B commercially?
Yes. DeepSeek R2 32B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is DeepSeek R2 32B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of DeepSeek R2 32B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.