Project AURIONAI Use-Cases Research & Innovation
Meta
edge LLM

Llama 3.2 1B

Llama 3.2 1B BF16 model, base of Meta's quantized edge versions.

Chart of accuracy against decoding speed for Llama 3.2 1B in BF16, QLoRA and SpinQuant forms.
Levelmodel
Classedge LLM
Parameters1B
Contextnot in the notes
Released24 Oct 2024
Opennessunknown
Runs onnot in the notes
Statusactive

Lineage

quantize
Llama 3.2 1B to Llama 3.2 1B quantized (QLoRA): quantize, high confidenceLlama 3.2 1B to Llama 3.2 1B quantized (SpinQuant): quantize, high confidenceLlama 3.2 1BLlama 3.2 1BMeta, Oct 2024Llama 3.2 1B quantized (QLoRA)Llama 3.2 1B quantized (QL…MetaLlama 3.2 1B quantized (SpinQuant)Llama 3.2 1B quantized (Sp…Meta
MetaLlama 3.2 1B quantized (QLoRA) is a child (quantize)high confidencepress claim
MetaLlama 3.2 1B quantized (SpinQuant) is a child (quantize)high confidencepress claim
Where this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 3 models. Confidence and evidence are the notes' own.

R&D Topics

Identity

Developer
Meta
Family
Llama 3.2
Released
24 Oct 2024 sourcesnotes
Weights published
–
Record checked
10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
not in the notes
Total parameters
1B sources
Active parameters
–
Checkpoint parameters
–
Note
1B
Context length
not in the notes
Input
text
Output
text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
unknown
Licence
Llama 3.2 Community Licence sources
Access routes
not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
not in the notes
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. Introducing quantized Llama models with increased speed and a reduced memory footprint ai.meta.com https://ai.meta.com/blog/meta-llama-quantized-lightweight-models/
  2. Open-sourcing circuit-tracing tools anthropic.com https://anthropic.com/research/open-source-circuit-tracing

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Llama 3.2 1B", https://aurion.semai.id/models/meta-llama-3-2-1b/, records of October 2026.