DeepSeek R1 Distill 7BvsQwen 3 8B
Complete comparison between DeepSeek R1 Distill 7B (7B parameters, DeepSeek) and Qwen 3 8B (8B, 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 R1 Distill 7B | Qwen 3 8B |
|---|---|---|
| Settings | 7B | 8B |
| Family | DeepSeek | Qwen |
| Author | DeepSeek | Alibaba |
| Origin | CN | CN |
| License | MIT | Apache 2.0 |
| Context | 32 768 tokens | 131,072 tokens |
| Output | January 2025 | April 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 R1 Distill 7B | Qwen 3 8B |
|---|---|---|
| Q4_K_M (lightweight) | 5 GB | 5 GB |
| Q5_K_M (balanced) | 6 GB | 6 GB |
| Q8 (near-lossless) | 9 GB | 9 GB |
| FP16 (max quality) | 16 GB | 16 GB |
| CPU-only RAM | 8 GB | 10 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 R1 Distill 7B | Qwen 3 8B |
|---|---|---|
| RTX 4090 (24 GB) | 90 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 12 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
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 R1 Distill 7B
Distilled chain-of-thought reasoning. Impressive at math.
- Explicit CoT reasoning in 7B
- MIT License
- 32k ctx
- Surprisingly strong on AIME/MATH for its size
- Very verbose (thinking tokens)
- Worse than R1 32B at complex reasoning
Installation
Qwen 3 8B
Hybrid thinking/fast mode. 119 languages, 32k native (131k via YaRN).
- Toggleable thinking mode
- 119 languages
- 131k ctx (with YaRN)
- Apache 2.0
- Verbose thinking mode
- Less mature than Qwen 2.5 in terms of ecosystem
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): DeepSeek R1 Distill 7B or Qwen 3 8B?
On an RTX 4090, DeepSeek R1 Distill 7B runs at FP16 (~90 tok/s), Qwen 3 8B in FP16 (~90 tok/s). In pure throughput, Qwen 3 8B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (5 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
DeepSeek R1 Distill 7B is under the MIT license — free for commercial use. Qwen 3 8B 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 R1 Distill 7B (7B). More capable: Qwen 3 8B (8B). 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.