DeepSeek R2 32BvsQwQ 32B
Complete comparison between DeepSeek R2 32B (32B parameters, DeepSeek) and QwQ 32B (32B, Alibaba). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data — no copy-pasting between pages.
In brief
| Feature | DeepSeek R2 32B | QwQ 32B |
|---|---|---|
| Settings | 32B | 32B |
| Family | DeepSeek | Qwen |
| Author | DeepSeek | Alibaba |
| Origin | CN | CN |
| License | MIT | Apache 2.0 |
| Context | 128,000 tokens | 131,072 tokens |
| Output | — | March 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | DeepSeek R2 32B | QwQ 32B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 19 GB |
| Q5_K_M (balanced) | 23 GB | 23 GB |
| Q8 (near-lossless) | 35 GB | 35 GB |
| FP16 (max quality) | 64 GB | 64 GB |
| CPU-only RAM | 32 GB | 32 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | DeepSeek R2 32B | QwQ 32B |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 12 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
DeepSeek R2 32B
Dense 32B reasoner. AIME 92.7%. Fits on RTX 4090 in Q4.
- Dense 32B reasoner. AIME 92.7%. Fits on RTX 4090 in Q4.
- 32B parameters
- MIT License
Installation
QwQ 32B
Apache 2.0 RL reasoner. AIME24 79.5, MATH-500 90.6. Direct competitor to DeepSeek R1.
- Direct competitor to R1
- 131k ctx
- Apache 2.0
- Verbose in thinking mode
- YaRN required beyond 8k
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): DeepSeek R2 32B or QwQ 32B?
On an RTX 4090, DeepSeek R2 32B runs at Q5_K_M (~30 tok/s), QwQ 32B at Q5_K_M (~30 tok/s). In pure throughput, QwQ 32B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (19 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
DeepSeek R2 32B is under the MIT license — free for commercial use. QwQ 32B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: DeepSeek R2 32B (32B). More capable: DeepSeek R2 32B (32B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.