Llama 3.2 1B
A solid pick for self-hosting and full data control.
Overview
Llama 3.2 1B is the smallest model in Meta's Llama family: just 1.24 billion parameters, paired with a 128,000-token context window that's unusually generous for a model this size. Released as open weights under the Llama 3.2 Community licence, which permits commercial use, it's built for on-device work — local assistants, lightweight summarisation, offline prototyping. The trade-off is straightforward: reasoning depth and factual reliability trail well behind larger models. Its open weights make it easy to self-host on European infrastructure, keeping data on-premise without relying on external cloud providers.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- Lagging on Arena
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Local on-device assistant
- Lightweight laptop summarisation
- Offline rapid prototyping
- Edge/IoT automation
- On-site confidential processing
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/llama-3-2-1b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
6/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 0.7 GB | 3 GB | Any recent PC/Mac |
| Q8 · balanced | 1.3 GB | 3 GB | Any recent PC/Mac |
| FP16 · max quality | 2.5 GB | 5 GB | Any recent PC/Mac |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
Easiest way to try it on a workstation. Install Ollama, then:
ollama run llama3.2:1bAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | 1111 |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |