What are the hardware requirements for Deepseek V3.1 MLX 5.5bit?

Deepseek V3.1 MLX 5.5bit requires approximately 231 GB of VRAM and supports a context window of 160K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Deepseek V3.1 MLX 5.5bit and how large is it?

Deepseek V3.1 MLX 5.5bit is developed by inferencerlabs, a model with 671b 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 Deepseek V3.1 MLX 5.5bit?

Deepseek V3.1 MLX 5.5bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Deepseek V3.1 MLX 5.5bit by inferencerlabs — VRAM 231GB, 160K context

Name: Deepseek V3.1 MLX 5.5bit
Author: inferencerlabs

Deepseek V3.1 MLX 5.5bit is an open-source language model by inferencerlabs. Features: 671b LLM, VRAM: 231GB, Context: 160K, License: mit, LLM Explorer Score: 0.2.

5-bit Base model:deepseek-ai/deepsee... Base model:quantized:deepseek-... Conversational Custom code Deepseek v3 Mlx Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/inferencerlabs/deepseek-v3.1-MLX-5.5bit

Deepseek V3.1 MLX 5.5bit Parameters and Internals

LLM Name	Deepseek V3.1 MLX 5.5bit
Repository 🤗	https://huggingface.co/inferencerlabs/deepseek-v3.1-MLX-5.5bit
Base Model(s)	deepseek-ai/DeepSeek-V3.1 deepseek-ai/DeepSeek-V3.1
Model Size	671b
Required VRAM	231 GB
Updated	2025-11-12
Maintainer	inferencerlabs
Model Type	deepseek_v3
Model Files	4.5 GB: 1-of-88 5.3 GB: 2-of-88 5.2 GB: 3-of-88 5.3 GB: 4-of-88 5.3 GB: 5-of-88 5.2 GB: 6-of-88 5.3 GB: 7-of-88 5.3 GB: 8-of-88 5.2 GB: 9-of-88 5.3 GB: 10-of-88 5.3 GB: 11-of-88 5.2 GB: 12-of-88 5.3 GB: 13-of-88 5.3 GB: 14-of-88 5.2 GB: 15-of-88 5.3 GB: 16-of-88 5.3 GB: 17-of-88 5.2 GB: 18-of-88 5.3 GB: 19-of-88 5.3 GB: 20-of-88 5.2 GB: 21-of-88 5.3 GB: 22-of-88 5.3 GB: 23-of-88 5.2 GB: 24-of-88 5.3 GB: 25-of-88 5.3 GB: 26-of-88 5.2 GB: 27-of-88 5.3 GB: 28-of-88 5.3 GB: 29-of-88 5.2 GB: 30-of-88 5.3 GB: 31-of-88 5.3 GB: 32-of-88 5.2 GB: 33-of-88 5.3 GB: 34-of-88 5.3 GB: 35-of-88 5.2 GB: 36-of-88 5.3 GB: 37-of-88 5.3 GB: 38-of-88 5.2 GB: 39-of-88 5.3 GB: 40-of-88 5.3 GB: 41-of-88 5.2 GB: 42-of-88 5.3 GB: 43-of-88 5.3 GB: 44-of-88
Model Architecture	DeepseekV3ForCausalLM
License	mit
Context Length	163840
Model Max Length	163840
Transformers Version	4.44.2
Vocabulary Size	129280
Torch Data Type	bfloat16

Rank the Deepseek V3.1 MLX 5.5bit 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 Deepseek V3.1 MLX 5.5bit

Best Alternatives	Context / RAM	Downloads	Likes
R1 1776	160K / 243.4 GB	855	2324
DeepSeek Prover V2 671B	160K / 189 GB	562	831
Cogito 671B V2.1	160K / 219.9 GB	85	29
MAI DS R1	160K / 171.7 GB	333	306
...o V2 Preview Deepseek 671B MoE	160K / 219.9 GB	118	37
DeepSeek Prover V2 671B	160K / 184.7 GB	9	3
DeepSeek V3.1 Terminus 4bit	160K / 190.1 GB	42	2
DeepSeek V3.1 8bit	160K / 177.8 GB	4268	3
RakutenAI 3.0 MLX 4bit	160K / 198.7 GB	28	2
DeepSeek V3.1 4bit	160K / 190.1 GB	2974	6

Note: green Score (e.g. "73.2") means that the model is better than inferencerlabs/deepseek-v3.1-MLX-5.5bit.