Llama 3.2 1B
The smallest Llama 3 release, designed for on-device inference on phones and laptops. The 1B model runs comfortably in <2 GB of RAM at Q4 quantization and is fast enough for real-time chat on a modern smartphone. Useful for edge inference, on-device assistants where round-tripping to a server is undesirable, and as a draft model for speculative decoding in front of a larger Llama 3 variant.
- Parameters
- 1B
- Context length
- 128K
- Modality
- text
- Released
- 2024-09-25
Memory & hardware
- VRAM (fp16)
- 2 GB
- VRAM (Q4)
- 0.6 GB
- Recommended
- CPU or any GPU
- Quantizations
- fp16, q8_0, q4_k_m, gguf
License: Llama 3 Community License
- SPDX
- —
- Commercial use
- Yes
- Modification
- Yes
- Redistribution
- Yes
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| groqCheapest | $0.04 | $0.04 |
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
ollama run llama3.2:1b
vllm serve meta-llama/Llama-3.2-1B
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.2-1B", device_map="auto", torch_dtype="auto"
)meta-llama/Llama-3.2-1B Related models
Same family or similar size — useful when shopping around.
Compact Gemma variant designed for on-device inference. Trained with knowledge distillation from larger Gemma 2 teachers. Runs comfortably on a phone at Q4.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 1.6 GB
Pocket-sized Llama 3 variant for edge deployment. Surprising chat quality after instruction tuning makes it competitive with much larger models from a previous generation. At Q4 it fits in ~2 GB of VRAM and runs on consumer GPUs and recent Apple Silicon. A strong default for on-device chat, summarisation, and structured extraction tasks where the workload doesn't need frontier reasoning quality.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 1.8 GB
The original 7B RLHF chat model. Historically important — the first widely-adopted commercially-usable open-weights chat model. Still cited as a baseline in most 2024–25 papers.
- Context
- 4K
- License
- llama-2
- VRAM Q4
- 4.2 GB
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
Llama 3's first vision-language model. Image understanding via a separately-trained ViT adapter bolted onto Llama 3 weights. Useful for OCR-adjacent workloads, document understanding, and image captioning at a permissive licence. The 11B size makes it cheap to host. Combined with the 128K text context, it handles long PDF-with-images workflows comfortably on a single 4090.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 6.6 GB
Mid-size Llama 2 chat model. Deprecated in most 2025 workloads by Llama 3.1 8B, but remains the baseline against which many post-2023 fine-tunes report.
- Context
- 4K
- License
- llama-2
- VRAM Q4
- 7.8 GB