What are the hardware requirements for Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym?

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym requires approximately 2.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym and how large is it?

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym is developed by nsbendre25. 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 Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym?

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym by nsbendre25 — VRAM 2.5GB, 128K context

Name: Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym
Author: nsbendre25

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym is an open-source language model by nsbendre25. Features: LLM, VRAM: 2.5GB, Context: 128K, License: mit, Quantized, Instruction-Based, LLM Explorer Score: 0.12.

Conversational Custom code En Endpoints compatible Fp16 Instruct Openvino Optimum-intel Phi-3 Phi3 Pytorch Quantized Region:us Weight compression

Model Card on HF 🤗: https://huggingface.co/nsbendre25/Phi-3-mini-128k-instruct-ov-fp16-int4-asym

Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, commercial applications

Primary Use Cases:

advanced natural language understanding and generation tasks

Limitations:

Not intended for use in creating or promoting harmful or illegal content

Additional Notes

The model is optimized using OpenVINO for devices with limited computational power through weight compression using the INT4_ASYM mode.

LLM Name	Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym
Repository 🤗	https://huggingface.co/nsbendre25/Phi-3-mini-128k-instruct-ov-fp16-int4-asym
Base Model(s)	...i 3 Mini 128K Instruct Ov Int4 fakezeta/Phi-3-mini-128k-instruct-ov-int4
Required VRAM	2.5 GB
Updated	2026-08-03
Maintainer	nsbendre25
Model Type	phi3
Instruction-Based	Yes
Model Files	2.5 GB
Supported Languages	en
Quantization Type	fp16
Model Architecture	Phi3ForCausalLM
License	mit
Context Length	131072
Model Max Length	131072
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32064
Torch Data Type	bfloat16

Rank the Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym 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 Phi 3 Mini 128K Instruct Ov Fp16 Int4 Asym

Best Alternatives	Context / RAM	Downloads	Likes
...m 128K Instruct 8.0bpw H8 EXL2	128K / 13.4 GB	7	4
...m 128K Instruct 6.0bpw H6 EXL2	128K / 10.7 GB	7	3
...dium 128K Instruct 8 0bpw EXL2	128K / 13.4 GB	4	1
...m 128K Instruct 3.0bpw H6 EXL2	128K / 5.6 GB	5	0
...m 128K Instruct 5.0bpw H6 EXL2	128K / 8.9 GB	5	0
...128K Instruct HQQ 2bit Smashed	128K / 1.4 GB	8	0
...128K Instruct HQQ 4bit Smashed	128K / 2.9 GB	7	0
NuExtract Bpw6 EXL2	4K / 3 GB	4	1
...Mini 4K Geminified 3 0bpw EXL2	4K / 1.6 GB	6	0
...uct Abliterated V3 2 2bpw EXL2	4K / 4.2 GB	9	0

Note: green Score (e.g. "73.2") means that the model is better than nsbendre25/Phi-3-mini-128k-instruct-ov-fp16-int4-asym.