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Can the RTX 4090 24GB Run Qwen 2.5 32B? (✅ 19GB VRAM Needed, 2026)

Can the RTX 4090 24GB run Qwen 2.5 32B locally? Yes — runs great at Q4 (recommended). VRAM requirements, expected tokens/sec, the right quantization, and the cheapest GPU that fits if it doesn’t.

← Can It Run? — GPU compatibility matrix · Local LLMs guide

✅ Yes — runs great at Q4 (recommended)
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

QuantizationVRAM neededRTX 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

Qwen 2.5 32B on other GPUs

🎯 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.

About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.

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