What are the hardware requirements for Llama 2 70B GPTQ?

Llama 2 70B GPTQ requires approximately 35.3 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 2 70B GPTQ and how large is it?

Llama 2 70B GPTQ is developed by TheBloke, 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 2 70B GPTQ?

Llama 2 70B GPTQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 2 70B GPTQ by TheBloke — VRAM 35.3GB, 4K context

Name: Llama 2 70B GPTQ
Author: TheBloke

Llama 2 70B GPTQ is an open-source language model by TheBloke. Features: 70b LLM, VRAM: 35.3GB, Context: 4K, License: llama2, Quantized, LLM Explorer Score: 0.1.

Arxiv:2307.09288 4-bit Base model:meta-llama/llama-2-... Base model:quantized:meta-llam... Deploy:azure En Facebook Gptq Llama Llama2 Meta Pytorch Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TheBloke/Llama-2-70B-GPTQ

Llama 2 70B GPTQ Benchmarks

LLME Score: 0.09588

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Llama 2 70B GPTQ (TheBloke/Llama-2-70B-GPTQ)

🌟 Advertise your project 🚀

Llama 2 70B GPTQ Parameters and Internals

Model Type

text-generation

Use Cases

Applications:

Chat assistants, Natural language generation

Primary Use Cases:

Assistant-like chat, GPTQ quantized for GPU inference

Limitations:

Testing conducted in English, outputs in other languages are out-of-scope

Considerations:

Compliance with Meta's Acceptable Use Policy

Additional Notes

Model architecture uses 4-bit quantized versions for different VRAM requirements and inference quality optimization; supported by AutoGPTQ

Training Details

Data Sources:

Publicly available online data, Publicly available instruction datasets, Over one million new human-annotated examples

Data Volume:

2 trillion tokens for pretraining

Methodology:

Auto-regressive language modeling with transformer architecture. Fine-tuned with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).

Context Length:

4096

Training Time:

Llama 2 70B required 1720320 GPU hours

Hardware Used:

Meta's Research Super Cluster, Production clusters, 3.3M GPU hours on A100-80GB GPUs

Model Architecture:

Auto-regressive transformer

Safety Evaluation

Methodologies:

Human evaluations, Internal benchmarks

Findings:

Outperformed open-source chat models on benchmarks, On par with closed-source models like ChatGPT for helpfulness and safety

Risk Categories:

Misinformation, Bias

Ethical Considerations:

Testing conducted in English and has not covered all scenarios; may produce inaccurate or biased outputs

Responsible Ai Considerations

Fairness:

Testing conducted indicates model may produce inaccurate, biased outputs

Transparency:

Safety testing and tuning should be performed for specific applications

Accountability:

Developers need to ensure safety before deploying applications

Mitigation Strategies:

Use safety testing and tuning tailored to specific applications

Input Output

Input Format:

Text input

Accepted Modalities:

Text

Output Format:

Text output

LLM Name	Llama 2 70B GPTQ
Repository 🤗	https://huggingface.co/TheBloke/Llama-2-70B-GPTQ
Model Name	Llama 2 70B
Model Creator	Meta Llama 2
Base Model(s)	Llama 2 70B Hf meta-llama/Llama-2-70b-hf
Model Size	70b
Required VRAM	35.3 GB
Updated	2026-05-19
Maintainer	TheBloke
Model Type	llama
Model Files	35.3 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.32.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Best Alternatives to Llama 2 70B GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
...B Instruct AutoRound GPTQ 4bit	128K / 39.9 GB	506	6
...B Instruct AutoRound GPTQ 4bit	128K / 39.9 GB	23	0
...ama 3.1 70B Instruct Gptq 4bit	128K / 39.9 GB	58	4
Opus V1.2 70B Marlin	32K / 36.4 GB	10	0
MoMo 70B Lora 1.8.6 DPO GPTQ	32K / 41.3 GB	10	1
MoMo 70B Lora 1.8.4 DPO GPTQ	32K / 41.3 GB	2	1
Tess 70B V1.6 Marlin	31K / 36.3 GB	7	1
Midnight Miqu 70B V1.5 GPTQ32G	31K / 40.7 GB	41	4
...Midnight Miqu 70B V1.0 GPTQ32G	31K / 40.7 GB	32	2
Senku 70B GPTQ 4bit	31K / 36.7 GB	5	1

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Llama-2-70B-GPTQ.

Rank the Llama 2 70B GPTQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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

What open-source LLMs or SLMs are you in search of? 53999 in total.

Email us: info@extractum.io. Our Privacy Policy | Terms and Conditions | Suggest an improvement.

Our Social Media →

Original data from HuggingFace, OpenCompass and various public git repos.

Check out Ag3ntum — our secure, self-hosted AI agent for server management.

Release v20260328a

Support LLM Explorer

Llama 2 70B GPTQ by TheBloke

» All LLMs » TheBloke » Llama 2 70B GPTQ URL Share it on

Llama 2 70B GPTQ Benchmarks

Llama 2 70B GPTQ Parameters and Internals

Best Alternatives to Llama 2 70B GPTQ

Rank the Llama 2 70B GPTQ Capabilities

What open-source LLMs or SLMs are you in search of? 53999 in total.