What are the hardware requirements for Granite Ml Coder?

Granite Ml Coder requires approximately 5.3 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 Granite Ml Coder and how large is it?

Granite Ml Coder is developed by Tekimax, a model with 1b 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 Granite Ml Coder?

Granite Ml Coder is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Granite Ml Coder by Tekimax — VRAM 5.3GB, 128K context

Name: Granite Ml Coder
Author: Tekimax

Granite Ml Coder is an open-source language model by Tekimax. Features: 1b LLM, VRAM: 5.3GB, Context: 128K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.25.

Base model:finetune:ibm-granit... Base model:ibm-granite/granite... Code Conversational Data-science Dataset:iamtarun/python code i... En Endpoints compatible Finetuned Gguf Granite Granitemoe Instruct Machine-learning Mlx Quantized Region:us Safetensors Scikit-learn

Model Card on HF 🤗: https://huggingface.co/Tekimax/granite-ml-coder

Granite Ml Coder Parameters and Internals

LLM Name	Granite Ml Coder
Repository 🤗	https://huggingface.co/Tekimax/granite-ml-coder
Base Model(s)	ibm-granite/granite-3.1-1b-a400m-instruct ibm-granite/granite-3.1-1b-a400m-instruct
Model Size	1b
Required VRAM	5.3 GB
Updated	2026-07-22
Maintainer	Tekimax
Model Type	granitemoe
Instruction-Based	Yes
Model Files	5.3 GB 0.0 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	GraniteMoeForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	5.9.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|end_of_text\|>
Vocabulary Size	49155
Errors	replace

Rank the Granite Ml Coder 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 Granite Ml Coder

Best Alternatives	Context / RAM	Downloads	Likes
Granite 3.1 1B A400m Instruct	128K / 2.7 GB	6039	21
Granite 3.0 1B A400m Instruct	4K / 2.7 GB	2814	21