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Qwen 2.5 32B needs 19 GB VRAM at Q4. The RTX 4080 Super 16GB has 16 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 4080 Super 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4080 Super 16GB |
|---|---|---|
| Q4_K_M (recommended) | 19 GB | ❌ |
| Q8_0 (near-lossless) | 34 GB | ❌ |
Why it won’t fit (and what to do)
Qwen 2.5 32B needs 19 GB at Q4 but the RTX 4080 Super 16GB has only 16 GB — you’re 3 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4080 Super 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$1,000.
Good: Very fast inference, excellent for real-time 14-27B chat
Watch out: Still 16GB — 70B needs offloading or a 24GB+ card
Check current RTX 4080 Super 16GB price on Amazon →
Related checks
Other models on the RTX 4080 Super 16GB
🎯 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 4080 Super 16GB run Qwen 2.5 32B?
No — not enough VRAM without CPU offloading. Qwen 2.5 32B needs 19 GB VRAM at Q4_K_M; the RTX 4080 Super 16GB has 16 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 4080 Super 16GB get on Qwen 2.5 32B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.