DeepSeek R1 Distill Qwen 32B
DeepSeek · MIT
Distilled reasoning model with capable coding and chat performance.
- Parameters
- 32.5B
- Architecture
- Dense
- Context
- 32,768 tokens
- Released
- 2025-01-20
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Aider, Open WebUI, LM Studio
Parameters
32.5B
VRAM
28 GB
Context
32K
Formats
6
GPUs
22
DeepSeek R1 Distill Qwen 32B (32.5B) requires 28 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 19 GB VRAM, making it compatible with the NVIDIA GeForce RTX 4070 Ti Super. On NVIDIA Grace Blackwell Ultra GB300, expect approximately 120 tok/s at Q8_0. For the best experience, High-End Home AI Server ($3,842) is recommended.
Source: OwnRig methodology
28 GB
Q6_K
24 GB
32K tokens
Reasoning
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 36 GB | 32 GB |
| recommended | Q6_K | 28 GB | 24 GB |
| recommended | Q5_K_M | 24 GB | 20 GB |
| efficient | Q4_K_M | 19 GB | 16 GB |
| compressed | Q3_K_M | 15.5 GB | 12.5 GB |
| compressed | Q2_K | 12.2 GB | 10 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 512 MB | 28.5 GBexceeds 24 GB |
| 4K | 1 GB | 29 GBexceeds 24 GB |
| 8K | 2 GB | 30 GBexceeds 24 GB |
| 16K | 4.1 GB | 32.1 GBexceeds 24 GB |
| 32K | 8.2 GB | 36.2 GBexceeds 24 GB |
Compatible GPUs
22 devices| NVIDIA Grace Blackwell Ultra GB300 | Q8_0 | 120 tok/s | Excellent |
| NVIDIA GeForce RTX 5090 | Q5_K_M | 42 tok/s | Excellent |
| Apple M4 Max (128GB Unified) | Q5_K_M | 16 tok/s | Good |
| Apple M4 Max (64GB Unified) | Q4_K_M | 17 tok/s | Good |
| Apple M4 Ultra (192GB) | Q5_K_M | 24 tok/s | Good |
| NVIDIA GeForce RTX 4090 | Q4_K_M | 24 tok/s | Good |
| AMD Radeon RX 7900 XTX | Q4_K_M | 21 tok/s | Good |
| AMD Radeon Pro W7900 | Q4_K_M | 18 tok/s | Good |
| NVIDIA GeForce RTX 4070 Ti Super | Q3_K_M | 15 tok/s | Acceptable |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | 30 tok/s | Acceptable |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Q8_0 | 28 tok/s | Acceptable |
| AMD Radeon RX 7600 | Q3_K_M | 2 tok/s | Marginal |
| AMD Radeon RX 9070 | Q3_K_M | 8 tok/s | Marginal |
| AMD Radeon RX 9060 XT 16GB | Q3_K_M | 4 tok/s | Marginal |
| Apple M3 Pro (18GB Unified) | Q3_K_M | – | Not viable |
| NVIDIA GeForce RTX 3080 10GB | Q2_K | – | Not viable |
| NVIDIA GeForce RTX 4060 8GB | Q2_K | – | Not viable |
| NVIDIA RTX 4060 Laptop (40-60W) | Q2_K | – | Not viable |
| NVIDIA RTX 4070 Laptop (80-115W) | Q2_K | – | Not viable |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q2_K | – | Not viable |
| AMD Radeon RX 9060 XT 8GB | Q3_K_M | – | Not viable |
| NVIDIA GeForce RTX 5060 8GB | Q2_K | – | Not viable |
Showing 22 of 22 entries
Builder Context
DeepSeek R1 Distill Qwen 32B is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
Complete PC builds that can run DeepSeek R1 Distill Qwen 32B.
AMD AI Powerhouse
24 GB of AI power at nearly half the NVIDIA price
Runs 7 models
High-End Home AI Server
Your household's private AI: chatbots, code tools, and more
Runs 12 models
Mid-Range Home AI Server
Serve multiple AI models to every device at home
Runs 9 models
Next-Gen AI Workstation
The fastest single GPU, built around the new RTX 5090
Runs 6 models
Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Qwen 32B need?
- DeepSeek R1 Distill Qwen 32B requires 28 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 12.2 GB.
- What is the best GPU for DeepSeek R1 Distill Qwen 32B?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for DeepSeek R1 Distill Qwen 32B, achieving 120 tok/s at Q8_0 with an excellent rating.
- What quantization should I use for DeepSeek R1 Distill Qwen 32B?
- For the best quality, use Q6_K (28 GB VRAM). If your GPU has limited VRAM, Q2_K (12.2 GB) is the most efficient option with acceptable quality.
- Is DeepSeek R1 Distill Qwen 32B good for coding?
- Yes. DeepSeek R1 Distill Qwen 32B is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: estimated. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.DeepSeek is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.