ReasoningCodingChat32.5B
DeepSeek R1 Distill Qwen 32B
DeepSeek · MIT
Distilled reasoning model with strong coding and chat capabilities.
- Parameters
- 32.5B
- Context
- 32,768 tokens
- Released
- 2025-01-20
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Aider, Open WebUI, LM Studio
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 GeForce RTX 5090, expect approximately 42 tok/s at Q5_K_M. For the best experience, High-End Home AI Server ($3,842) is recommended.
— OwnRig methodology, data updated 2026-03-15
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 |
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
Performance data for DeepSeek R1 Distill Qwen 32B across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4090 | Q4_K_M | 24 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4070 Ti Super | Q3_K_M | 15 tok/s | Acceptable | ✓ |
| Apple M4 Max (64GB Unified) | Q4_K_M | 17 tok/s | Good | ✓ |
| Apple M4 Max (128GB Unified) | Q5_K_M | 16 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 5090 | Q5_K_M | 42 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q2_K | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q2_K | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 3080 10GB | Q2_K | — | Not Viable | ✗ (offload) |
| Apple M3 Pro (18GB Unified) | Q3_K_M | — | Not Viable | ✗ (offload) |
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.
High-End
Mid-RangeExtreme
High-EndHome AI Server
High-End Home AI Server
2x NVIDIA GeForce RTX 3090 24GB (Used) + NVLink Bridge · 128GB DDR5-5600 (4x32GB)
$3,84248 GB VRAM
Runs 12 models
Mid-RangeHome AI Server
Mid-Range Home AI Server
NVIDIA GeForce RTX 3090 24GB (Used) · 64GB DDR5-5600 (2x32GB)
$1,89224 GB VRAM
Runs 9 models
ExtremeFull AI Builder
Next-Gen AI Workstation
NVIDIA GeForce RTX 5090 32GB · 64GB DDR5-6000 (2x32GB)
$4,03232 GB VRAM
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 15.5 GB.
- What is the best GPU for DeepSeek R1 Distill Qwen 32B?
- The NVIDIA GeForce RTX 5090 delivers the best performance for DeepSeek R1 Distill Qwen 32B, achieving 42 tok/s at Q5_K_M 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, Q3_K_M (15.5 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. Last updated: 2026-03-15. Source