Can I Run Llama 3.2 Vision on 96 GB system 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Superseded model. Llama 3.2 Vision has been superseded by Llama 4.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
Technically yes, but not recommended
It loads, but it is not worth running — Llama 3.2 Vision 90B at Q5_K_M fits in 96 GB system RAM's 76.8 GB, yet the memory bandwidth limits it to ~1 tok/s (estimated), well below usable interactive speed.
Confidence: high · Recommended quantization: Q5_K_M · Estimated speed: ~1 tok/s
96 GB system RAM — what it gives a model
| Usable memory for models | 76.8 GB |
| Memory bandwidth | 90 GB/s |
Llama 3.2 Vision on 96 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 76.8 GB? | Max context | Est. speed | Download |
|---|
| F16 | 181.8 GB | ✗ No | — | — | 177.6 GB |
| Q8_0 | 98.5 GB | ✗ No | — | — | 94.4 GB |
| Q6_K | 77 GB | ✗ No | — | — | 72.8 GB |
| Q5_K_M | 67.1 GB | ✓ Yes | 16K | ~1 tok/s | 62.9 GB |
| Q4_K_M | 57.8 GB | ✓ Yes | 32K | ~1.2 tok/s | 53.6 GB |
| Q3_K_M | 42 GB | ✓ Yes | 64K | ~1.7 tok/s | 37.9 GB |
| Q2_K | 33.3 GB | ✓ Yes | 64K | ~2.2 tok/s | 29.2 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 | ✓ Fits | ~1.2 tok/s |
| Llama 3.2 Vision 11B | 8.5 GB | ✓ Fits | ~9.3 tok/s |
What to watch out for
- At ~1 tok/s this loads but is too slow for interactive use — expect roughly 60 seconds per 60 tokens.
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 76.8 GB of the 96 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- 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 96 GB system RAM?
It loads, but it is not worth running — Llama 3.2 Vision 90B at Q5_K_M fits in 96 GB system RAM's 76.8 GB, yet the memory bandwidth limits it to ~1 tok/s (estimated), well below usable interactive speed.
Which quantization of Llama 3.2 Vision should I use on 96 GB system RAM?
Q5_K_M — it needs about 67.1 GB of the 76.8 GB available, downloads as roughly 62.9 GB, and runs at an estimated 1 tokens/sec with up to 16K of context.
What limits Llama 3.2 Vision on 96 GB system RAM?
Memory bandwidth. The model fits, but at 89.6 GB/s it can only be read fast enough for roughly 1 tokens/sec.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 96 GB system RAM
Llama 3.2 Vision on GPUs
What This Model Is Good At
Model & Tools
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