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Qwen 2.5 32B needs 19 GB VRAM at Q4. The RTX 4090 24GB has 24 GB.
🌐 Qwen 2.5 32B (Alibaba) is a 32B-parameter model for advanced coding, math, long-form reasoning. One of the best open 32B models, rivals much larger ones at code. The question is whether the RTX 4090 24GB’s 24 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4090 24GB |
|---|---|---|
| Q4_K_M (recommended) | 19 GB | ✅ |
| Q8_0 (near-lossless) | 34 GB | ❌ |
Expected performance
Running Qwen 2.5 32B at Q4 on the RTX 4090 24GB, expect roughly ~24 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 19 GB used of 24 GB leaves about 5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the RTX 4090 24GB
Architecture: Ada · VRAM: 24 GB · Price: ~$1,800.
Good: Fastest 24GB consumer GPU — excellent real-time inference
Watch out: Expensive; 24GB still caps 70B without heavy quantization
Check current RTX 4090 24GB price on Amazon →
Related checks
Other models on the RTX 4090 24GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4090 24GB run Qwen 2.5 32B?
Yes — runs great at Q4 (recommended). Qwen 2.5 32B needs 19 GB VRAM at Q4_K_M; the RTX 4090 24GB has 24 GB.
How much VRAM does Qwen 2.5 32B need?
Qwen 2.5 32B needs about 19 GB at Q4_K_M (recommended) and 34 GB at Q8_0.
How many tokens per second will the RTX 4090 24GB get on Qwen 2.5 32B?
Roughly ~24 tokens/sec at Q4 in Ollama or llama.cpp.