What are the hardware requirements for NousResearch Meta Llama 3 8B HQQ 4bit Smashed?

NousResearch Meta Llama 3 8B HQQ 4bit Smashed 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 NousResearch Meta Llama 3 8B HQQ 4bit Smashed and how large is it?

NousResearch Meta Llama 3 8B HQQ 4bit Smashed is developed by PrunaAI, a model with 8b 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 NousResearch Meta Llama 3 8B HQQ 4bit Smashed?

NousResearch Meta Llama 3 8B HQQ 4bit Smashed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

NousResearch Meta Llama 3 8B HQQ 4bit Smashed by PrunaAI — VRAM 5.8GB, 8K context

Name: NousResearch Meta Llama 3 8B HQQ 4bit Smashed
Author: PrunaAI

NousResearch Meta Llama 3 8B HQQ 4bit Smashed is an open-source language model by PrunaAI. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, Quantized, LLM Explorer Score: 0.12.

4bit Base model:finetune:nousresear... Base model:nousresearch/meta-l... Llama Pruna-ai Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/PrunaAI/NousResearch-Meta-Llama-3-8B-HQQ-4bit-smashed

NousResearch Meta Llama 3 8B HQQ 4bit Smashed Parameters and Internals

Model Type

text generation

Use Cases

Considerations:

Efficiency depends on specific use-cases.

Additional Notes

Model format is safetensors. Sync and Async metrics available.

Training Details

Data Sources:

WikiText

Methodology:

compressed with hqq

Input Output

Input Format:

text prompt

Accepted Modalities:

text

Output Format:

generated text

Performance Tips:

Ensure recent python, cuda, and transformers versions are used.

LLM Name	NousResearch Meta Llama 3 8B HQQ 4bit Smashed
Repository 🤗	https://huggingface.co/PrunaAI/NousResearch-Meta-Llama-3-8B-HQQ-4bit-smashed
Base Model(s)	Meta Llama 3 8B NousResearch/Meta-Llama-3-8B
Model Size	8b
Required VRAM	5.8 GB
Updated	2025-09-23
Maintainer	PrunaAI
Model Type	llama
Model Files	5.8 GB
Quantization Type	4bit
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	float16

Rank the NousResearch Meta Llama 3 8B HQQ 4bit Smashed 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 NousResearch Meta Llama 3 8B HQQ 4bit Smashed

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...B Instruct Gradient 1048K 4bit	1024K / 4.5 GB	10	2
...B Instruct Gradient 1048K 8bit	1024K / 8.6 GB	11	1
...truct Gradient 1048K Bpw6 EXL2	1024K / 6.7 GB	2	2
...truct Gradient 1048K Bpw5 EXL2	1024K / 5.8 GB	5	0
Llama 3 8B Instruct 1048K 4bit	1024K / 4.5 GB	525	25
Llama 3 8B Instruct 1048K 8bit	1024K / 8.6 GB	213	17
... Gradient 1048K 8.0bpw H8 EXL2	1024K / 8.6 GB	2	3
...ct Gradient 1048K Bpw2.25 EXL2	1024K / 3.4 GB	5	1
Llama 3 8B Instruct 262K 2bit	256K / 2.5 GB	11	1
...B Instruct 262k V2 EXL2 5.0bpw	256K / 5.8 GB	5	1

Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/NousResearch-Meta-Llama-3-8B-HQQ-4bit-smashed.