What are the hardware requirements for Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed?

Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed requires approximately 5.8 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed?

Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed by PrunaAI — VRAM 5.8GB, 256K context

Name: Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed
Author: PrunaAI

Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed is an open-source language model by PrunaAI. Features: 8b LLM, VRAM: 5.8GB, Context: 256K, Quantized, Instruction-Based, LLM Explorer Score: 0.12.

4-bit 4bit Awq Base model:prunaai/gradientai-... Base model:quantized:prunaai/g... Instruct Llama Pruna-ai Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/PrunaAI/gradientai-Llama-3-8B-Instruct-262k-AWQ-4bit-smashed

Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed Parameters and Internals

Model Type

compression, efficiency

Additional Notes

The model uses safetensors format and calibration data from WikiText for compression. Different metrics for efficiency evaluation include inference latency, memory, energy consumption, and CO2 emissions. Efficiency evaluation results might vary based on the hardware and settings used.

LLM Name	Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed
Repository 🤗	https://huggingface.co/PrunaAI/gradientai-Llama-3-8B-Instruct-262k-AWQ-4bit-smashed
Base Model(s)	...Instruct 262K AWQ 4bit Smashed PrunaAI/gradientai-Llama-3-8B-Instruct-262k-AWQ-4bit-smashed
Model Size	8b
Required VRAM	5.8 GB
Updated	2025-10-02
Maintainer	PrunaAI
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\|4bit
Model Architecture	LlamaForCausalLM
Context Length	262144
Model Max Length	262144
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	float16

Rank the Gradientai Llama 3 8B Instruct 262K AWQ 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

Quantized Models of the Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed

Model	Likes	Downloads	VRAM
...Instruct 262K AWQ 4bit Smashed	4	5	5 GB

Best Alternatives to Gradientai Llama 3 8B Instruct 262K AWQ 4bit Smashed

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
...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
...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 PrunaAI/gradientai-Llama-3-8B-Instruct-262k-AWQ-4bit-smashed.