What are the hardware requirements for Llama 3.3 70B Instruct 4.65bpw H6 EXL2?

Llama 3.3 70B Instruct 4.65bpw H6 EXL2 requires approximately 42.7 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 4.65bpw H6 EXL2 and how large is it?

Llama 3.3 70B Instruct 4.65bpw H6 EXL2 is developed by LoneStriker, 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 4.65bpw H6 EXL2?

Llama 3.3 70B Instruct 4.65bpw H6 EXL2 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 4.65bpw H6 EXL2 by LoneStriker — VRAM 42.7GB, 128K context

Name: Llama 3.3 70B Instruct 4.65bpw H6 EXL2
Author: LoneStriker

Llama 3.3 70B Instruct 4.65bpw H6 EXL2 is an open-source language model by LoneStriker. Features: 70b LLM, VRAM: 42.7GB, Context: 128K, License: llama3.3, Quantized, Instruction-Based, LLM Explorer Score: 0.15.

Arxiv:2204.05149 Base model:meta-llama/llama-3.... Base model:quantized:meta-llam... Conversational De En Endpoints compatible Es Exl2 Facebook Fr Hi Instruct It Llama Llama-3 Meta Pt Pytorch Quantized Region:us Safetensors Sharded Tensorflow Th

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Llama-3.3-70B-Instruct-4.65bpw-h6-exl2

Llama 3.3 70B Instruct 4.65bpw H6 EXL2 Parameters and Internals

LLM Name	Llama 3.3 70B Instruct 4.65bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/Llama-3.3-70B-Instruct-4.65bpw-h6-exl2
Base Model(s)	meta-llama/Llama-3.1-70B meta-llama/Llama-3.1-70B
Model Size	70b
Required VRAM	42.7 GB
Updated	2026-07-20
Maintainer	LoneStriker
Model Type	llama
Instruction-Based	Yes
Model Files	8.5 GB: 1-of-6 8.5 GB: 2-of-6 8.5 GB: 3-of-6 8.5 GB: 4-of-6 7.9 GB: 5-of-6 0.8 GB: 6-of-6
Supported Languages	en fr it pt hi es th de
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
License	llama3.3
Context Length	131072
Model Max Length	131072
Transformers Version	4.47.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Llama 3.3 70B Instruct 4.65bpw H6 EXL2 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 4.65bpw H6 EXL2

Best Alternatives	Context / RAM	Downloads	Likes
...B Instruct Gradient 1048K 8bit	1024K / 75 GB	41	5
...B Instruct Gradient 1048K 4bit	1024K / 39.7 GB	25	3
...B Instruct Gradient 1048K 2bit	1024K / 21.9 GB	25	2
...0B Instruct Gradient 262K 4bit	256K / 39.7 GB	25	3
...0B Instruct Gradient 262K 8bit	256K / 75 GB	26	2
...0B Instruct Gradient 262K 2bit	256K / 21.9 GB	20	1
... Gradient 262K 2.25bpw H6 EXL2	256K / 22.2 GB	8	0
...t Gradient 262K 4.0bpw H6 EXL2	256K / 37.2 GB	7	1
...t Gradient 262K 2.4bpw H6 EXL2	256K / 23.5 GB	6	0
...t Gradient 262K 3.5bpw H6 EXL2	256K / 32.9 GB	6	0