What are the hardware requirements for Llama 3.3 70B Instruct AutoRound GPTQ 4bit?

Llama 3.3 70B Instruct AutoRound GPTQ 4bit 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 Llama 3.3 70B Instruct AutoRound GPTQ 4bit and how large is it?

Llama 3.3 70B Instruct AutoRound GPTQ 4bit is developed by Satwik11, 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 Llama 3.3 70B Instruct AutoRound GPTQ 4bit?

Llama 3.3 70B Instruct AutoRound GPTQ 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.3 70B Instruct AutoRound GPTQ 4bit by Satwik11 — VRAM 39.9GB, 128K context

Name: Llama 3.3 70B Instruct AutoRound GPTQ 4bit
Author: Satwik11

Llama 3.3 70B Instruct AutoRound GPTQ 4bit is an open-source language model by Satwik11. Features: 70b LLM, VRAM: 39.9GB, Context: 128K, License: llama3.3, Quantized, Instruction-Based, LLM Explorer Score: 0.16.

4-bit 4bit Autoregressive Base model:meta-llama/llama-3.... Base model:quantized:meta-llam... Conversational Dataset:meta-llama/llama-3.3-7... Deploy:azure Endpoints compatible Gptq Instruct Llama Quantization Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Satwik11/Llama-3.3-70B-Instruct-AutoRound-GPTQ-4bit

Llama 3.3 70B Instruct AutoRound GPTQ 4bit Parameters and Internals

LLM Name	Llama 3.3 70B Instruct AutoRound GPTQ 4bit
Repository 🤗	https://huggingface.co/Satwik11/Llama-3.3-70B-Instruct-AutoRound-GPTQ-4bit
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	2026-07-24
Maintainer	Satwik11
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
GPTQ Quantization	Yes
Quantization Type	gptq\|4bit
Model Architecture	LlamaForCausalLM
License	llama3.3
Context Length	131072
Model Max Length	131072
Transformers Version	4.47.1
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Llama 3.3 70B Instruct AutoRound GPTQ 4bit 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

Best Alternatives to Llama 3.3 70B Instruct AutoRound GPTQ 4bit

Best Alternatives	Context / RAM	Downloads	Likes
...B Instruct AutoRound GPTQ 4bit	128K / 39.9 GB	517	6
...ama 3.1 70B Instruct Gptq 4bit	128K / 39.9 GB	7	4
...ta Llama 3 70B Instruct Marlin	8K / 39.5 GB	141	6
...ama 3 Taiwan 70B Instruct GPTQ	8K / 39.8 GB	6	2
Meta Llama 3 70B Instruct GPTQ	8K / 39.8 GB	905	16
Meta Llama 3 70B Instruct GPTQ	8K / 39.8 GB	763	19
...g Llama 3 70B Instruct GPTQ 4B	8K / 39.8 GB	6	0
...g Llama 3 70B Instruct GPTQ 8B	8K / 74.4 GB	3	1
...a Llama 3 70B Instruct GPTQ 8B	8K / 74.4 GB	17	0
...erkrautLM 70B Instruct GPTQ 8B	8K / 74.4 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than Satwik11/Llama-3.3-70B-Instruct-AutoRound-GPTQ-4bit.