What are the hardware requirements for GPT Oss 20B Ja Fin Thinking MLX 4bit?

GPT Oss 20B Ja Fin Thinking MLX 4bit requires approximately 11.8 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 GPT Oss 20B Ja Fin Thinking MLX 4bit and how large is it?

GPT Oss 20B Ja Fin Thinking MLX 4bit is developed by mlx-community, a model with 20b 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 GPT Oss 20B Ja Fin Thinking MLX 4bit?

GPT Oss 20B Ja Fin Thinking MLX 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GPT Oss 20B Ja Fin Thinking MLX 4bit by mlx-community — VRAM 11.8GB, 128K context

Name: GPT Oss 20B Ja Fin Thinking MLX 4bit
Author: mlx-community

GPT Oss 20B Ja Fin Thinking MLX 4bit is an open-source language model by mlx-community. Features: 20b LLM, VRAM: 11.8GB, Context: 128K, License: apache-2.0, Quantized, LLM Explorer Score: 0.24.

4-bit 4bit Base model:nri-ai/gpt-oss-20b-... Base model:quantized:nri-ai/gp... Conversational En Gpt oss Ja Mlx Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mlx-community/gpt-oss-20b-Ja-Fin-Thinking-MLX-4bit

GPT Oss 20B Ja Fin Thinking MLX 4bit Parameters and Internals

LLM Name	GPT Oss 20B Ja Fin Thinking MLX 4bit
Repository 🤗	https://huggingface.co/mlx-community/gpt-oss-20b-Ja-Fin-Thinking-MLX-4bit
Base Model(s)	GPT Oss 20B Ja Fin Thinking nri-ai/gpt-oss-20b-Ja-Fin-Thinking
Model Size	20b
Required VRAM	11.8 GB
Updated	2026-08-10
Maintainer	mlx-community
Model Type	gpt_oss
Model Files	5.3 GB: 1-of-3 5.3 GB: 2-of-3 1.2 GB: 3-of-3
Supported Languages	ja en
Quantization Type	4bit
Model Architecture	GptOssForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.57.1
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Vocabulary Size	201088

Rank the GPT Oss 20B Ja Fin Thinking MLX 4bit capabilities

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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 GPT Oss 20B Ja Fin Thinking MLX 4bit

Best Alternatives	Context / RAM	Downloads	Likes
GPT Oss 20B MXFP4 Q8	128K / 12.1 GB	354421	79
GPT Oss 20B OptiQ 4bit	128K / 11.6 GB	1690	6
GPT Oss 20B Unsloth Bnb 4bit	128K / 12.6 GB	64914	38
...s 20B Ja Fin Thinking MLX 8bit	128K / 22.1 GB	102	1
GPT Oss 20B MLX 8bit	128K / 22.1 GB	2890	58
GPT Oss 20B MXFP4 Q4	128K / 11.2 GB	2178	16
GPT Oss 20B Bnb 4bit	128K / 40.8 GB	2402	14
GPT Oss Coder 20B	128K / 13.8 GB	13	13
GPT Oss 20B	128K / 13.8 GB	7983620	4849
OTel LLM 20B Reasoning	128K / 83.8 GB	1040	1

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/gpt-oss-20b-Ja-Fin-Thinking-MLX-4bit.