Written by Jakub Rusinowski · Last updated August 15, 2026
Yes — comfortably
Yes, comfortably — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~18.6 GB spare and running at ~89.9 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~89.9 tok/s
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 23.3 GB | ✓ Yes | 32K | ~54 tok/s | 21.2 GB |
| Q8_0 | 13.4 GB | ✓ Yes | 64K | ~89.9 tok/s | 11.3 GB |
| Q6_K | 10.8 GB | ✓ Yes | 64K | ~108.5 tok/s | 8.7 GB |
| Q5_K_M | 9.7 GB | ✓ Yes | 64K | ~120 tok/s | 7.5 GB |
| Q4_K_M | 8.5 GB | ✓ Yes | 64K | ~133.2 tok/s | 6.4 GB |
| Q3_K_M | 6.7 GB | ✓ Yes | 64K | ~163.7 tok/s | 4.5 GB |
| Q2_K | 5.6 GB | ✓ Yes | 64K | ~187.3 tok/s | 3.5 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Llama 3.2 90B Vision Instruct | 57.8 GB | ✗ Too large | — |
| Llama 3.2 11B Vision Instruct | 8.5 GB | ✓ Fits | ~133.2 tok/s |
| Llama 3.2 3B Instruct | 3.7 GB | ✓ Fits | ~247.7 tok/s |
| Llama 3.2 1B Instruct | 1.8 GB | ✓ Fits | ~344.5 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~18.6 GB spare and running at ~89.9 tok/s (estimated), with room for about 65,536 tokens of context.
Q8_0 — it needs about 13.4 GB of the 32 GB available, downloads as roughly 11.3 GB, and runs at an estimated 89.9 tokens/sec with up to 64K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
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