What are the hardware requirements for Meta Llama Llama 3.2 1B Instruct FP16?

Meta Llama Llama 3.2 1B Instruct FP16 requires approximately 2.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Meta Llama Llama 3.2 1B Instruct FP16 and how large is it?

Meta Llama Llama 3.2 1B Instruct FP16 is developed by context-labs, a model with 1b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Meta Llama Llama 3.2 1B Instruct FP16?

Meta Llama Llama 3.2 1B Instruct FP16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Meta Llama Llama 3.2 1B Instruct FP16 by context-labs — VRAM 2.5GB, 128K context

Name: Meta Llama Llama 3.2 1B Instruct FP16
Author: context-labs

Meta Llama Llama 3.2 1B Instruct FP16 is an open-source language model by context-labs. Features: 1b LLM, VRAM: 2.5GB, Context: 128K, License: llama3.2, Quantized, Instruction-Based, LLM Explorer Score: 0.16.

Model Card on HF 🤗: https://huggingface.co/context-labs/meta-llama-Llama-3.2-1B-Instruct-FP16

Meta Llama Llama 3.2 1B Instruct FP16 Parameters and Internals

LLM Name	Meta Llama Llama 3.2 1B Instruct FP16
Repository 🤗	https://huggingface.co/context-labs/meta-llama-Llama-3.2-1B-Instruct-FP16
Model Size	1b
Required VRAM	2.5 GB
Updated	2026-07-26
Maintainer	context-labs
Model Type	llama
Instruction-Based	Yes
Model Files	2.5 GB
Supported Languages	en de fr it pt hi es th
Quantization Type	fp16
Model Architecture	LlamaForCausalLM
License	llama3.2
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Meta Llama Llama 3.2 1B Instruct FP16 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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... Fable5 V2 Thinking OptiQ 4bit	128K / 0.9 GB	1564	5
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...lama 3.2 1B Legal Id Finetuned	128K / 2.5 GB	192	0
HRA Non Instruct Merged	128K / 2.5 GB	78	0
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...2 1B Instruct Unsloth Bnb 4bit	128K / 1.1 GB	30960	4

Note: green Score (e.g. "73.2") means that the model is better than context-labs/meta-llama-Llama-3.2-1B-Instruct-FP16.