ReasoningCodingAI CodingAI Building32.5B
QwQ 32B Preview
Qwen · Apache 2.0
Reasoning-focused model — uses chain-of-thought natively. Builders pair this with a coding model for complex architecture decisions. Approaches o1-mini on reasoning benchmarks. Same size as Qwen 2.5 Coder 32B, so they compete for VRAM — run one at a time, not concurrently.
- Parameters
- 32.5B
- Context
- 131,072 tokens
- Released
- 2024-11-28
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Open WebUI, LM Studio
QwQ 32B Preview (32.5B) requires 21.9 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 18.4 GB VRAM, making it compatible with the NVIDIA GeForce RTX 4090. On NVIDIA GeForce RTX 4090, expect approximately 24 tok/s at Q4_K_M. For the best experience, AMD AI Powerhouse ($1,818) is recommended.
— OwnRig methodology, data updated 2026-03-01
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| recommended | Q5_K_M | 21.9 GB | 19.5 GB |
| efficient | Q4_K_M | 18.4 GB | 16.3 GB |
| compressed | Q3_K_M | 14.8 GB | 12.7 GB |
| compressed | Q2_K | 11.6 GB | 9.8 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 410 MB | 22.3 GB |
| 4K | 819 MB | 22.7 GB |
| 8K | 1.5 GB | 23.4 GB |
| 16K | 3.1 GB | 25 GBexceeds 24 GB |
| 32K | 6.1 GB | 28 GBexceeds 24 GB |
| 64K | 12.3 GB | 34.2 GBexceeds 24 GB |
| 128K | 24.6 GB | 46.5 GBexceeds 24 GB |
Compatible GPUs
Performance data for QwQ 32B Preview across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4090 | Q4_K_M | 24 tok/s | Good | ✓ |
| Apple M4 Max (64GB Unified) | Q5_K_M | 17 tok/s | Good | ✓ |
| Apple M4 Max (128GB Unified) | Q8_0 | 14 tok/s | Good | ✓ |
| 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
QwQ 32B Preview is commonly used with Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
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Frequently Asked Questions
- How much VRAM does QwQ 32B Preview need?
- QwQ 32B Preview requires 21.9 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 11.6 GB.
- What is the best GPU for QwQ 32B Preview?
- The NVIDIA GeForce RTX 4090 delivers the best performance for QwQ 32B Preview, achieving 24 tok/s at Q4_K_M with an good rating.
- What quantization should I use for QwQ 32B Preview?
- For the best quality, use Q5_K_M (21.9 GB VRAM). If your GPU has limited VRAM, Q2_K (11.6 GB) is the most efficient option with acceptable quality.
- Is QwQ 32B Preview good for coding?
- Yes. QwQ 32B Preview is used with 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: 2026-03-01. Source