v2.2 · 2026-06-12

Meta Llama 3.2 1B Instruct

llama-3-2-1b-instruct

TEXT-CHAT

Meta Llama 3.2 1B Instruct is a 1B text chat model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

Meta

FAMILY

llama

MODEL TYPE

text-chat

PARAMETERS

MODALITIES

text

ARCHITECTURE

decoder-only Transformer (LlamaForCausalLM)

CONTEXT WINDOW

128,000 tokens

TRAINING TOKENS

—

RELEASE DATE

2024-09-25

LICENSE & LINKS

Llama 3.2 Community Licenselicense text

MODEL CARD

huggingface.co/meta-llama/Llama-3.2-1B-Instruct

HUGGING FACE

meta-llama/Llama-3.2-1B-Instruct

GGUF REPOSITORIES

bartowski/Llama-3.2-1B-Instruct-GGUF community conversion

Community GGUF conversion with Q4_K_M, Q5_K_M and Q8_0 file-size table.

SETUP HINTS

OLLAMA

ollama run llama3.2:1b

LM STUDIO

Use bartowski/Llama-3.2-1B-Instruct-GGUF and select Q4_K_M or Q5_K_M.

LLAMA.CPP

llama-server -hf bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

0.81 GB

Q5_K_M

0.91 GB

Q8_0

1.32 GB

Quantization note: Q4_K_M

RAM / VRAM ESTIMATES

MIN RAM

2 GB

COMFORTABLE RAM

4 GB

MIN VRAM

2 GB

COMFORTABLE VRAM

4 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

yes

8 GB RAM

comfortable

16 GB RAM

comfortable

32 GB RAM

comfortable

8 GB VRAM

comfortable

12 GB VRAM

comfortable

24 GB VRAM

comfortable

Apple Silicon (unified memory)

comfortable

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·very small local chat deployments

·edge devices

·summarization and rewriting

·personal information management experiments

AVOID IF

·you need strong reasoning

·you need high factual accuracy on complex tasks

·you need verified explicit tool-call support rather than agentic-retrieval support

CAVEATS

·RAM and VRAM estimates are conservative local-inference estimates based on GGUF file size plus runtime and KV-cache overhead, not official Meta requirements.

·The fetched reliable sources describe agentic retrieval and summarization, but explicit tool-call support for the 1B variant was not independently verified; supportsTools is therefore null.

·Training-token count was not confirmed in cited sources from the fetched sources.

SOURCE URLS