Llama 3.2 1B Instruct — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 25, 2024

Model library → Llama 3.2 Family → Llama 3.2 1B Instruct

Meta's smallest production-quality instruction model. Runs in under 1GB VRAM — perfect for Raspberry Pi, phones, and embedded devices.

Llama 3.2 1B Instruct needs about 2 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters1 Billion
Context window128,000
ArchitectureDense
ProviderMeta
LicenceLlama Community
Specified atQ4_K_M
System RAM4 GB
Record updated2024-09-25

Licence

Llama Community — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K0.4 GB1.5 GB~338 tok/s (est.)Fits comfortably
Q3_K_M0.5 GB1.6 GB~320 tok/s (est.)Fits comfortably
Q4_K_M0.7 GB1.8 GB~293 tok/s (est.)Fits comfortably
Q5_K_M0.9 GB1.9 GB~279 tok/s (est.)Fits comfortably
Q6_K1.0 GB2.1 GB~265 tok/s (est.)Fits comfortably
Q8_01.3 GB2.4 GB~240 tok/s (est.)Fits comfortably
F162.5 GB3.5 GB~175 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Llama 3.2 1B Instruct VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Llama 3.2 1B Instruct is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run Llama 3.2 1B Instruct

Install Ollama, then run:

ollama run llama3.2:1b

Weights on Hugging Face: meta-llama/Llama-3.2-1B-Instruct.

Best for: edge devices, mobile, fast chat, low vram.

Can I Run Llama 3.2 1B Instruct on My GPU?

Other Llama 3.2 Family Sizes

Llama 3.2 1B Instruct — Frequently Asked Questions

How much VRAM does Llama 3.2 1B Instruct need?
About 2 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Llama 3.2 1B Instruct run on an RTX 4090 (24 GB)?
Yes. Llama 3.2 1B Instruct needs about 2 GB at Q4_K_M, inside a 24 GB card, at an estimated 293 tokens/sec.
How do I run Llama 3.2 1B Instruct locally?
Install Ollama and run `ollama run llama3.2:1b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Llama 3.2 Family come in?
Llama 3.2 1B Instruct (2 GB), Llama 3.2 3B Instruct (3 GB), Llama 3.2 11B Vision Instruct (7 GB), Llama 3.2 90B Vision Instruct (54 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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