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

Meta Llama 3 8B Instruct Hf 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 Hf AWQ and how large is it?

Meta Llama 3 8B Instruct Hf AWQ is developed by solidrust, 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 Hf AWQ perform on standard benchmarks?

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

Name: Meta Llama 3 8B Instruct Hf AWQ
Author: solidrust

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

4-bit Autotrain compatible Awq Base model:quantized:undi95/me... Base model:undi95/meta-llama-3... Conversational Endpoints compatible Facebook Instruct Llama Llama-3 Meta Pytorch Quantized Region:us Safetensors Sharded Tensorflow

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

Meta Llama 3 8B Instruct Hf AWQ Parameters and Internals

Model Type

text-generation

Use Cases

Primary Use Cases:

dialogue use cases

Additional Notes

Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open-source chat models on common industry benchmarks.

Training Details

Methodology:

The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Model Architecture:

Llama 3 is an auto-regressive language model that uses an optimized transformer architecture.

Responsible Ai Considerations

Fairness:

The tuned versions are optimized for human preferences for helpfulness and safety.

Input Output

Input Format:

Models input text only.

Output Format:

Models generate text and code only.

LLM Name	Meta Llama 3 8B Instruct Hf AWQ
Repository 🤗	https://huggingface.co/solidrust/Meta-Llama-3-8B-Instruct-hf-AWQ
Base Model(s)	Meta Llama 3 8B Instruct Hf Undi95/Meta-Llama-3-8B-Instruct-hf
Model Size	8b
Required VRAM	5.8 GB
Updated	2026-07-12
Maintainer	solidrust
Model Type	llama
Instruction-Based	Yes
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	other
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 Hf 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 Hf 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	7	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	401	5

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