~/can-i-run/llama-3-2-1b
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Can I run Llama 3.2 1B?

Short answer: yes, on a RTX 3070 (8GB) at FP16/BF16. Long answer below.

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The math, in one paragraph.

$ ./vrambudget --explain llama-3-2-1b

Llama 3.2 1B has 1.23B parameters. At FP16 that's 2.5 GB of raw weights. Quantization shrinks that, but you also need budget for the KV cache (definition), framework overhead, and safety headroom. The rule of thumb: real usable budget on a card is roughly its nameplate VRAM minus 25%. That's how the table below was computed.

What hardware actually fits.

$ grep "fits" gpus.json
FP16/BF16
2.5GB
42 GPUs fit
RTX 30708GBRTX 40608GBRTX 308010GBRTX 3060 12GB12GB+ 38 more
Q8_0
1.3GB
42 GPUs fit
RTX 30708GBRTX 40608GBRTX 308010GBRTX 3060 12GB12GB+ 38 more
Q5_K_M
0.85GB
42 GPUs fit
RTX 30708GBRTX 40608GBRTX 308010GBRTX 3060 12GB12GB+ 38 more
Q4_K_M
0.69GB
42 GPUs fit
RTX 30708GBRTX 40608GBRTX 308010GBRTX 3060 12GB12GB+ 38 more
Q3_K_M
0.53GB
42 GPUs fit
RTX 30708GBRTX 40608GBRTX 308010GBRTX 3060 12GB12GB+ 38 more

Pick your path.

$ ls strategies/
Tightest budget

Smallest GPU that fits Llama 3.2 1B at any quant: RTX 3070 at FP16/BF16.

Reference quality (FP16)

Lossless inference needs 2.5 GB. Pick from 42 cards.

Best quality on a 24GB card

FP16/BF16 fits comfortably (2.5 GB weights).

Tune the math yourself

Open the calculator pre-tuned for Llama 3.2 1B: ↗ /calc?model=llama-3-2-1b

See the full model page.

$ ./open
$ openLlama 3.2 1B1.23BMeta · ctx 128K$ rankBest GPU for Llama 3.2 1Bsee the ranking →ranked by fit quality

Discussion.

$ gh discussion list

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