Can I Run Llama 3.2 Family on 48 GB system RAM?

Written by Jakub Rusinowski · Last updated August 15, 2026

48 GB system RAM — what it gives a model

Usable memory for models	38.4 GB
Memory bandwidth	90 GB/s

Llama 3.2 Family on 48 GB system RAM: memory by quantization

Quant	Memory needed	Fits 38.4 GB?	Max context	Est. speed	Download
F16	23.3 GB	✓ Yes	64K	~3.1 tok/s	21.2 GB
Q8_0	13.4 GB	✓ Yes	64K	~5.6 tok/s	11.3 GB
Q6_K	10.8 GB	✓ Yes	64K	~7.1 tok/s	8.7 GB
Q5_K_M	9.7 GB	✓ Yes	64K	~8.1 tok/s	7.5 GB
Q4_K_M	8.5 GB	✓ Yes	64K	~9.3 tok/s	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	64K	~12.6 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	64K	~15.6 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	~9.3 tok/s
Llama 3.2 3B Instruct	3.7 GB	✓ Fits	~26.3 tok/s
Llama 3.2 1B Instruct	1.8 GB	✓ Fits	~65 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.
• 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.
• 38.4 GB of the 48 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 Family on 48 GB system RAM?

Yes, comfortably — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 38.4 GB usable on 48 GB system RAM, leaving ~25 GB spare and running at ~5.6 tok/s (estimated), with room for about 65,536 tokens of context.

Which quantization of Llama 3.2 Family should I use on 48 GB system RAM?

Q8_0 — it needs about 13.4 GB of the 38.4 GB available, downloads as roughly 11.3 GB, and runs at an estimated 5.6 tokens/sec with up to 64K of context.

What limits Llama 3.2 Family on 48 GB system RAM?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

llama.cpp (CPU build) or Ollama — both run without a GPU

Other RAM Capacities

• Llama 3.2 Family on 32 GB RAM
• Llama 3.2 Family on 64 GB RAM
• Llama 3.2 Family on 24 GB RAM
• Llama 3.2 Family on 16 GB RAM

Other Models on 48 GB system RAM

• Llama 3.2 Vision on 48 GB system RAM
• Llama 3.3 on 48 GB system RAM
• Llama 4 on 48 GB system RAM
• Ministral on 48 GB system RAM
• Mistral Family on 48 GB system 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

• Best local LLMs for general assistant
• Best local LLMs for rag
• Best local LLMs for writing

Model & Tools

• Llama 3.2 Family model page
• VRAM calculator
• Check your own hardware

← Can I Run It? | Llama 3.2 Family model page | Check your hardware