Can I Run Llama 3.2 Vision on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Superseded model. Llama 3.2 Vision has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point.
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Written by Jakub Rusinowski · Last updated September 25, 2024
Yes
Yes — Llama 3.2 Vision 11B at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~40.9 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~40.9 tok/s
RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Llama 3.2 Vision on RTX 4090 Laptop (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 | ~40.9 tok/s | 11.3 GB |
| Q6_K | 10.8 GB | ✓ Yes | 32K | ~50.9 tok/s | 8.7 GB |
| Q5_K_M | 9.7 GB | ✓ Yes | 32K | ~57.3 tok/s | 7.5 GB |
| Q4_K_M | 8.5 GB | ✓ Yes | 32K | ~65 tok/s | 6.4 GB |
| Q3_K_M | 6.7 GB | ✓ Yes | 32K | ~84.1 tok/s | 4.5 GB |
| Q2_K | 5.6 GB | ✓ Yes | 64K | ~100.3 tok/s | 3.5 GB |
Which Llama 3.2 Vision sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| Llama 3.2 Vision 90B | 57.8 GB | ✗ Too large | — |
| Llama 3.2 Vision 11B | 8.5 GB | ✓ Fits | ~65 tok/s |
What to watch out for
- 1 larger variant of Llama 3.2 Vision does not fit and would need CPU offload or different hardware.
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
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 4080 at 717 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 Vision on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes — Llama 3.2 Vision 11B at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~40.9 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Llama 3.2 Vision should I use on RTX 4090 Laptop (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 40.9 tokens/sec with up to 16K of context.
What limits Llama 3.2 Vision on RTX 4090 Laptop (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 4090 Laptop (16 GB VRAM, 32 GB RAM)
Llama 3.2 Vision on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Llama 3.2 Vision model page | Check your hardware