What are the hardware requirements for Meta Llama 3.3 70B Instruct AWQ INT4?

Meta Llama 3.3 70B Instruct AWQ INT4 requires approximately 39.9 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 3.3 70B Instruct AWQ INT4 and how large is it?

Meta Llama 3.3 70B Instruct AWQ INT4 is developed by ibnzterrell, a model with 70b 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 3.3 70B Instruct AWQ INT4?

Meta Llama 3.3 70B Instruct AWQ INT4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Meta Llama 3.3 70B Instruct AWQ INT4 by ibnzterrell — VRAM 39.9GB, 128K context

Name: Meta Llama 3.3 70B Instruct AWQ INT4
Author: ibnzterrell

Meta Llama 3.3 70B Instruct AWQ INT4 is an open-source language model by ibnzterrell. Features: 70b LLM, VRAM: 39.9GB, Context: 128K, License: meta, Quantized, Instruction-Based, LLM Explorer Score: 0.22.

4-bit Autoawq Awq Base model:meta-llama/llama-3.... Base model:quantized:meta-llam... Conversational De Deploy:azure En Endpoints compatible Es Fr Hi Instruct It Llama Llama-3.3 Meta Pt Quantized Region:us Safetensors Sharded Tensorflow Th

Model Card on HF 🤗: https://huggingface.co/ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Meta Llama 3.3 70B Instruct AWQ INT4 Parameters and Internals

LLM Name	Meta Llama 3.3 70B Instruct AWQ INT4
Repository 🤗	https://huggingface.co/ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4
Base Model(s)	meta-llama/Llama-3.3-70B-Instruct meta-llama/Llama-3.3-70B-Instruct
Model Size	70b
Required VRAM	39.9 GB
Updated	2026-07-30
Maintainer	ibnzterrell
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-9 4.9 GB: 2-of-9 4.9 GB: 3-of-9 4.9 GB: 4-of-9 4.9 GB: 5-of-9 4.9 GB: 6-of-9 4.9 GB: 7-of-9 3.4 GB: 8-of-9 2.1 GB: 9-of-9
Supported Languages	en fr it pt hi es th de
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	meta
Context Length	131072
Model Max Length	131072
Transformers Version	4.47.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Meta Llama 3.3 70B Instruct AWQ INT4 capabilities

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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

Best Alternatives to Meta Llama 3.3 70B Instruct AWQ INT4

Best Alternatives	Context / RAM	Downloads	Likes
...0B Instruct Gradient 1048K AWQ	1024K / 39.9 GB	5	1
...70B Instruct Gradient 262K AWQ	256K / 39.9 GB	5	0
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	262002	46
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	16090	12
... SauerkrautLM 70B Instruct AWQ	128K / 39.9 GB	5	5
Llama 3 70B Instruct AWQ	8K / 39.9 GB	122175	71
Meta Llama 3 70B Instruct AWQ	8K / 39.9 GB	8	1
Llama 3 70B Instruct AWQ	8K / 39.9 GB	17	1
... Llama 3 70B Instruct AWQ GEMM	8K / 39.9 GB	6	0
... Llama 3 70B Instruct AWQ GEMM	8K / 39.9 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4.