What are the hardware requirements for Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix?

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix requires a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

How does Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix perform on standard benchmarks?

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix has the following published scores: MMLU 69.08, LMSYS ELO 1081. Compare against reference models on this page or on the LLM Explorer leaderboards.

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix by amd — 4K context

Name: Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix
Author: amd

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix is an open-source language model by amd. Features: LLM, Context: 4K, License: mit, Quantized, Instruction-Based, ELO: 1081, Arc: 63, HellaSwag: 80.6, MMLU: 69.1, GSM8K: 74.5.

Amd Awq Base model:microsoft/phi-3-min... Base model:quantized:microsoft... Code Conversational Custom code En Fr Instruct Onnx Phi3 Quantized Region:us Ryzenai-npu

Model Card on HF 🤗: https://huggingface.co/amd/Phi-3-mini-4k-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix Benchmarks

LMSys ELO 1081 vs 1272 (so35) -15%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix Parameters and Internals

LLM Name	Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix
Repository 🤗	https://huggingface.co/amd/Phi-3-mini-4k-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix
Base Model(s)	Phi 3 Mini 4K Instruct microsoft/Phi-3-mini-4k-instruct
Updated	2026-08-09
Maintainer	amd
Model Type	phi3
Instruction-Based	Yes
Supported Languages	en fr
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Phi3ForCausalLM
License	mit
Context Length	4096
Model Max Length	4096
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32064
Torch Data Type	bfloat16

Rank the Phi 3 Mini 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix 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 4K Instruct AWQ G128 Int4 Asym Bf16 Onnx Ryzen Strix

Best Alternatives	Context / RAM	Downloads	Likes
...nt4 Asym Bf16 Onnx Ryzen Strix	128K / GB	11	3
...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
...28K Instruct Ov Fp16 Int4 Asym	128K / 2.5 GB	9	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

Note: green Score (e.g. "73.2") means that the model is better than amd/Phi-3-mini-4k-instruct-awq-g128-int4-asym-bf16-onnx-ryzen-strix.