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Llama 3.2 Instruct (11B)

Efficient open model for edge and private deployments

Model Summary

Family

Llama 3.2

Version

3.2

Parameters

11B (est.)

Parameter counts for closed models are estimates; vendors rarely publish exact sizes.

VRAM Requirements by Quantization

Memory needed to serve Llama 3.2 Instruct (11B) for inference, including a 20% overhead for activations and KV cache.

PrecisionVRAM neededSmallest single GPU that fits
INT4 (4-bit)6.15 GBNVIDIA P100 SXM2 (16 GB)
INT8 (8-bit)12.29 GBNVIDIA P100 SXM2 (16 GB)
FP16 (16-bit)24.59 GBNVIDIA V100 SXM2 32GB (32 GB)
FP32 (32-bit)49.17 GBNVIDIA H100 SXM5 (80 GB)

Recommended GPU Configurations

Cheapest on-demand configurations to serve Llama 3.2 Instruct (11B) at 8-bit (12 GB VRAM).

1x NVIDIA T4

16 GB total VRAM · Turing

~$0.50/h

1x NVIDIA P100 SXM2

16 GB total VRAM · Pascal

~$0.60/h

1x NVIDIA L4

24 GB total VRAM · Ada Lovelace

~$1.00/h

Quick GPU Planning

Use the calculator pre-filled with this exact version to estimate memory, speed, and compute requirements in a few clicks.

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Frequently Asked Questions

How much VRAM do you need to run Llama 3.2 Instruct (11B)?

With an estimated 11B parameters, Llama 3.2 Instruct (11B) needs roughly 12 GB of VRAM in 8-bit (INT8), 6 GB in 4-bit, and 25 GB in FP16, including a 20% overhead for activations and KV cache.

Which GPUs can run Llama 3.2 Instruct (11B)?

At 8-bit quantization, the most cost-effective option is 1x NVIDIA T4 (16 GB combined VRAM, around $0.50/hour on-demand). Higher-end cards like the NVIDIA B200 or AMD MI355X reduce the GPU count needed.

Can Llama 3.2 Instruct (11B) run on a single GPU?

Yes. In 8-bit, a single NVIDIA P100 SXM2 (16 GB) fits the model.

How much does it cost to serve Llama 3.2 Instruct (11B) in the cloud?

Renting 1x T4 costs on the order of $0.50/hour, i.e. about $365/month running 24/7. Actual prices vary by provider and commitment; spot and reserved capacity can be significantly cheaper.