Qwen3 32B vs DeepSeek R1 Distill Qwen 32B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Qwen3 32B is more hardware-efficient — it needs 22.2 GB at Q4_K_M vs 22.9 GB for DeepSeek R1 Distill Qwen 32B, fitting on 76 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Qwen3 32B | DeepSeek R1 Distill Qwen 32B | Diff |
|---|---|---|---|
| FP32 | 148.4 GB | 148.0 GB | +0% |
| BF16 | 75.0 GB | 75.2 GB | -0% |
| FP16 | 75.0 GB | 75.2 GB | -0% |
| Q8_0 | 38.2 GB | 38.8 GB | -1% |
| Q6_K | 31.6 GB | 32.3 GB | -2% |
| Q5_K_M | 25.2 GB | 25.8 GB | -3% |
| Q4_K_M | 22.2 GB | 22.9 GB | -3% |
| Q3_K_M | 17.3 GB | 18.1 GB | -4% |
| Q2_K | 13.6 GB | 14.4 GB | -6% |
| NVFP4 | 19.9 GB | 20.6 GB | -4% |
Diff is Qwen3 32B relative to DeepSeek R1 Distill Qwen 32B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Qwen3 32B | DeepSeek R1 Distill Qwen 32B |
|---|---|---|
| Org | Alibaba | DeepSeek |
| Parameters | 32.8B | 32.5B |
| Architecture | Dense | Dense |
| Context | 128k tokens | 125k tokens |
| Modalities | text | text |
| License | Apache 2.0 | MIT |
| Commercial | Yes | Yes |
| Released | 2025-04-29 | 2025-01-20 |
| GPUs (native) | 76 / 107 | 75 / 107 |
Benchmark scores
| Benchmark | Qwen3 32B | DeepSeek R1 Distill Qwen 32B |
|---|---|---|
| MMLU-Pro | 65.5 | 65.0 |
Green = higher score (better). — = not yet available.
GPUs that run only Qwen3 32B(1)
- Apple M3 Pro (18GB)18 GB
GPUs that run only DeepSeek R1 Distill Qwen 32B(0)
Every GPU that runs DeepSeek R1 Distill Qwen 32B also runs Qwen3 32B.
GPUs that run both natively(75)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 508016 GB
- NVIDIA RTX 5070 Ti16 GB
- NVIDIA RTX 5060 Ti 16GB16 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA H100 80GB80 GB
- NVIDIA A100 80GB80 GB
- NVIDIA A100 40GB40 GB
- +63 more GPUs run both
Which should you use?
Choose Qwen3 32B if:
- • You want maximum capability and have a 23 GB+ GPU
- • Long context matters — it supports 128k tokens vs 125k
- • Benchmark quality matters — scores 65.5 vs 65.0 on MMLU-Pro
Choose DeepSeek R1 Distill Qwen 32B if:
- • You have limited VRAM — it's a smaller model needing 22.9 GB vs 22.2 GB
Frequently asked questions
- Which is better, Qwen3 32B or DeepSeek R1 Distill Qwen 32B?
- Qwen3 32B has 32.8B parameters vs 32.5B for DeepSeek R1 Distill Qwen 32B, so Qwen3 32B is the larger model. Qwen3 32B is more hardware-efficient, needing 22.2 GB at Q4_K_M vs 22.9 GB. Qwen3 32B runs on more GPUs natively (76 vs 75). On MMLU-Pro, Qwen3 32B scores higher (65.5 vs 65.0).
- How much VRAM does Qwen3 32B need vs DeepSeek R1 Distill Qwen 32B?
- At Q4_K_M quantization with 8k context, Qwen3 32B needs approximately 22.2 GB of VRAM, while DeepSeek R1 Distill Qwen 32B needs 22.9 GB. At FP16, Qwen3 32B requires 75.0 GB vs 75.2 GB for DeepSeek R1 Distill Qwen 32B.
- Can you run Qwen3 32B on the same GPUs as DeepSeek R1 Distill Qwen 32B?
- Yes, 75 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 5080, NVIDIA RTX 5070 Ti. However, 1 GPUs can run Qwen3 32B but not DeepSeek R1 Distill Qwen 32B, and no GPU can run DeepSeek R1 Distill Qwen 32B without also fitting Qwen3 32B.
- What is the difference between Qwen3 32B and DeepSeek R1 Distill Qwen 32B?
- Qwen3 32B has 32.8B parameters (dense) with a 128k context window. DeepSeek R1 Distill Qwen 32B has 32.5B parameters (dense) with a 125k context window. Licensing differs: Qwen3 32B is Apache 2.0 while DeepSeek R1 Distill Qwen 32B is MIT.
- Which model fits in 24 GB of VRAM, Qwen3 32B or DeepSeek R1 Distill Qwen 32B?
- Both fit in 24 GB of VRAM at Q4_K_M — Qwen3 32B needs 22.2 GB and DeepSeek R1 Distill Qwen 32B needs 22.9 GB.