Llama-3.2-1B
Q4 GGUF download: bartowski/Llama-3.2-1B-Instruct-GGUF - 0.81 GB on disk, read 2026-10-10. VRAM figures above are estimates; this is the actual file size.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
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Deep dive
Notes, sources, and the full write-up
Llama-3.2-1B is a 1.2B-parameter llama3.2 model from meta-llama. At Q4_K_M it needs roughly 1 GB of VRAM, placing it in the CPU / ≤4 GB hardware tier.
PopularityLink to this section
Llama-3.2-1B has 1,904,377 downloads in the last month on HuggingFace and 2,459 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Llama-3.2-1B need?
Llama-3.2-1B with 1.2B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Llama-3.2-1B better than other meta-llama models?
Llama-3.2-1B has 1.2B parameters with 8,192 context - a strong choice for general use.
What license is Llama-3.2-1B under?
Llama-3.2-1B is released under the llama3.2 license, making it suitable for most commercial and personal projects.
What hardware runs Llama-3.2-1B well?
With 1.2B parameters, Llama-3.2-1B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Llama-3.2-1B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with Llama-3.2-1B?
Llama-3.2-1B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec