Can I Run Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes
Yes — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~53.1 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~53.1 tok/s
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RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 23.3 GB | ✗ No | — | — | 21.2 GB |
| Q8_0 | 13.4 GB | ✓ Yes | 16K | ~53.1 tok/s | 11.3 GB |
| Q6_K | 10.8 GB | ✓ Yes | 32K | ~65.6 tok/s | 8.7 GB |
| Q5_K_M | 9.7 GB | ✓ Yes | 32K | ~73.5 tok/s | 7.5 GB |
| Q4_K_M | 8.5 GB | ✓ Yes | 32K | ~82.9 tok/s | 6.4 GB |
| Q3_K_M | 6.7 GB | ✓ Yes | 32K | ~105.8 tok/s | 4.5 GB |
| Q2_K | 5.6 GB | ✓ Yes | 64K | ~124.7 tok/s | 3.5 GB |
Which Llama 3.2 Family sizes fit
| 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 | ~82.9 tok/s |
| Llama 3.2 3B Instruct | 3.7 GB | ✓ Fits | ~178.9 tok/s |
| Llama 3.2 1B Instruct | 1.8 GB | ✓ Fits | ~288 tok/s |
What to watch out for
- 1 larger variant of Llama 3.2 Family does not fit and would need CPU offload or different hardware.
RTX 5080 desktop limitations
- 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~53.1 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Llama 3.2 Family should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 13.4 GB of the 16 GB available, downloads as roughly 11.3 GB, and runs at an estimated 53.1 tokens/sec with up to 16K of context.
What limits Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Llama 3.2 Vision on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Magistral Small on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- MiniCPM-V on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Ministral on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Ministral 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Llama 3.2 Family on GPUs
- Llama 3.2 Family on NVIDIA GeForce RTX 5070
- Llama 3.2 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Llama 3.2 Family on NVIDIA GeForce RTX 5060
- Llama 3.2 Family on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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