What are the hardware requirements for Meta Llama 3 8B Instruct AWQ?

Meta Llama 3 8B Instruct AWQ requires approximately 5.8 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 Meta Llama 3 8B Instruct AWQ and how large is it?

Meta Llama 3 8B Instruct AWQ is developed by aspenita, a model with 8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Meta Llama 3 8B Instruct AWQ perform on standard benchmarks?

Meta Llama 3 8B Instruct AWQ has the following published scores: MMLU 65.06. Compare against reference models on this page or on the LLM Explorer leaderboards.

Meta Llama 3 8B Instruct AWQ by aspenita — VRAM 5.8GB, 8K context

Name: Meta Llama 3 8B Instruct AWQ
Author: aspenita

Meta Llama 3 8B Instruct AWQ is an open-source language model by aspenita. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, License: llama3, Quantized, Instruction-Based, LLM Explorer Score: 0.14, Arc: 59.6, HellaSwag: 78.8, MMLU: 65.1, GSM8K: 57.3.

4-bit Autoquant Autotrain compatible Awq Conversational En Endpoints compatible Facebook Instruct Llama Llama-3 Meta Pytorch Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ

Meta Llama 3 8B Instruct AWQ Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

commercial, research

Applications:

assistant-like chat

Primary Use Cases:

instruction-tuned models for dialogue

Limitations:

Use must comply with laws and Llama 3 license policy

Considerations:

Developers should perform safety testing and tuning prior to deployment.

Additional Notes

Models are optimized for helpfulness and safety through RLHF and SFT.

Supported Languages

primary (English)

Training Details

Data Sources:

publicly available online data

Data Volume:

15 trillion tokens

Methodology:

pre-trained, instruction-tuned, RLHF

Context Length:

8000

Hardware Used:

Meta's Research SuperCluster, H100-80GB GPUs

Model Architecture:

auto-regressive transformer with Grouped-Query Attention

Safety Evaluation

Methodologies:

red teaming, adversarial evaluations

Findings:

reduced residual risk via safety mitigations

Risk Categories:

misinformation, cybersecurity, child safety

Ethical Considerations:

developers must assess risks for specific use cases

Responsible Ai Considerations

Fairness:

Safety benchmarks are transparent and rigorous.

Transparency:

Evaluations and benchmarks are publicly accessible.

Accountability:

Users and developers must adhere to guidelines and policies.

Mitigation Strategies:

Incorporate safeguards like Meta Llama Guard 2 and Code Shield

Input Output

Input Format:

text

Accepted Modalities:

text

Output Format:

text and code

Performance Tips:

Use with transformers pipeline or llama3 codebase for best results.

Release Notes

Version:

April 18, 2024

Date:

April 18, 2024

Notes:

Initial release of Llama 3 models, including optimized transformers architecture.

LLM Name	Meta Llama 3 8B Instruct AWQ
Repository 🤗	https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ
Base Model(s)	Meta Llama 3 13B Instruct andrijdavid/Meta-Llama-3-13B-Instruct
Model Size	8b
Required VRAM	5.8 GB
Updated	2025-09-23
Maintainer	aspenita
Model Type	llama
Instruction-Based	Yes
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	llama3
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.2
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	float16

Rank the Meta Llama 3 8B Instruct AWQ 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 Meta Llama 3 8B Instruct AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...8B Instruct Gradient 1048K AWQ	1024K / 5.8 GB	8	0
...radient 1048K AWQ 4bit Smashed	1024K / 5.8 GB	7	1
...Instruct 262K AWQ 4bit Smashed	256K / 5.8 GB	5	4
...ta Llama 3 8B Instruct 64K AWQ	64K / 5.8 GB	6	0
... Instruct 8B 32K V0.1 4bit AWQ	64K / 5.8 GB	9	0
Llama 3 8B Instruct AWQ	8K / 5.8 GB	25458	31
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	6	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	563	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	401	5
...eta Llama 3 8B Instruct Hf AWQ	8K / 5.8 GB	179	9

Note: green Score (e.g. "73.2") means that the model is better than aspenita/Meta-Llama-3-8B-Instruct-AWQ.