What are the hardware requirements for Llama 3 70B Fp16?

Llama 3 70B Fp16 requires approximately 141.9 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3 70B Fp16 and how large is it?

Llama 3 70B Fp16 is developed by casperhansen, a model with 70b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Llama 3 70B Fp16 perform on standard benchmarks?

Llama 3 70B Fp16 has the following published scores: MMLU 69.37. Compare against reference models on this page or on the LLM Explorer leaderboards.

Llama 3 70B Fp16 by casperhansen — VRAM 141.9GB, 8K context

Name: Llama 3 70B Fp16
Author: casperhansen

Llama 3 70B Fp16 is an open-source language model by casperhansen. Features: 70b LLM, VRAM: 141.9GB, Context: 8K, License: other, Quantized, LLM Explorer Score: 0.13, Arc: 68.9, HellaSwag: 86.9, MMLU: 69.4, GSM8K: 31.8.

Conversational Deploy:azure En Endpoints compatible Facebook Fp16 Llama Llama-3 Meta Pytorch Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/casperhansen/llama-3-70b-fp16

Llama 3 70B Fp16 Parameters and Internals

Model Type

text generation

Use Cases

Areas:

commercial, research

Applications:

assistant-like chat

Primary Use Cases:

English text and code generation

Limitations:

Out-of-scope for languages other than English without compliance, Risk of misuse if violated acceptable use policy

Considerations:

Developers should tune model for safety based on specific applications.

Additional Notes

Focused on safe and inclusive text generation practices. Special measures for sensitive applications implemented.

Supported Languages

en (proficient)

Training Details

Data Sources:

publicly available online data

Data Volume:

15T+ tokens pretraining, 10M human-annotated examples fine-tuning

Methodology:

Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)

Context Length:

8000

Training Time:

7.7M GPU hours

Hardware Used:

H100-80GB GPUs

Model Architecture:

Auto-regressive language model with transformer architecture

Safety Evaluation

Methodologies:

red teaming, adversarial evaluations

Findings:

Residual risks remain; improved model helpfulness and reduced false refusals compared to Llama 2.

Risk Categories:

child safety, cybersecurity

Ethical Considerations:

Residual risks highlighted

Responsible Ai Considerations

Fairness:

Emphasis on inclusivity and openness

Transparency:

Open source license and transparency on safety standards

Accountability:

Meta and developers accountable for use adhering to license terms

Mitigation Strategies:

Llama Guard 2 and Code Shield tools for safety

Input Output

Input Format:

Text input only

Accepted Modalities:

text

Output Format:

Text and code generation

Performance Tips:

Use Reinforcement Learning with Human Feedback for optimal tuning.

Release Notes

Version:

Meta Llama 3

Date:

April 18, 2024

Notes:

Initial release with enhanced helpfulness and safety measures.

LLM Name	Llama 3 70B Fp16
Repository 🤗	https://huggingface.co/casperhansen/llama-3-70b-fp16
Base Model(s)	LLaMA 2 Wizard 70B QLoRA v2ray/LLaMA-2-Wizard-70B-QLoRA
Model Size	70b
Required VRAM	141.9 GB
Updated	2026-07-27
Maintainer	casperhansen
Model Type	llama
Model Files	4.6 GB: 1-of-30 4.7 GB: 2-of-30 5.0 GB: 3-of-30 5.0 GB: 4-of-30 4.7 GB: 5-of-30 4.7 GB: 6-of-30 4.7 GB: 7-of-30 5.0 GB: 8-of-30 5.0 GB: 9-of-30 4.7 GB: 10-of-30 4.7 GB: 11-of-30 4.7 GB: 12-of-30 5.0 GB: 13-of-30 5.0 GB: 14-of-30 4.7 GB: 15-of-30 4.7 GB: 16-of-30 4.7 GB: 17-of-30 5.0 GB: 18-of-30 5.0 GB: 19-of-30 4.7 GB: 20-of-30 4.7 GB: 21-of-30 4.7 GB: 22-of-30 5.0 GB: 23-of-30 5.0 GB: 24-of-30 4.7 GB: 25-of-30 4.7 GB: 26-of-30 4.7 GB: 27-of-30 5.0 GB: 28-of-30 5.0 GB: 29-of-30 2.1 GB: 30-of-30
Supported Languages	en
Quantization Type	fp16
Model Architecture	LlamaForCausalLM
License	other
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Llama 3 70B Fp16 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

Quantized Models of the Llama 3 70B Fp16

Model	Likes	Downloads	VRAM
NorskGPT Llama 3 70B Adapter	7	6	0 GB

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