granite-vision-4.1-4b

4.00B parameters, images and text in, text out.

Verified 2026-10-25 Rank #733 in LLMs · score 71.6 method v1 Sources 3 Downloads 30d 123.9k · #294 of 619 tracked

Can I run it?2.4GB at INT4Estimatefits a 16 GB cardmethod

Can I use it?Apache 2.0Permissive; patent grantlicence file

Do people use it?123.9kdownloads, 30 days#294 of 619 we track · top 47%111 likesHugging Face

Worth reading

Picked by rule from 3 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 328 HN pts

Granite 4.1: IBM's 8B Model Matching 32B MoE

IBM released Granite 4.1, a family of open source models trained on 15 trillion tokens with a rigorous data quality pipeline.

The 3B model extends context only to 128K tokens, not the 512K available to larger variants.
Phase 5 extended the context window eventually to 512K tokens for the 8B and 30B models.

firethering.com 2026-04-30 328 HN pts · 209 comments 1.9k words about the granite 4.1 family family

Latest · 6 months ago

The IBM Granite 4.1 family of models

IBM released the Granite 4.1 family of models under an Apache 2.0 license, offering language models in 3B, 8B, and 30B parameter sizes alongside vision, speech, embedding, and guardian capabilities.

Granite Speech 4.1 2B NAR generates entire sequences at once instead of one token at a time.
The Granite 4.1 8B instruct model matches or outperforms the Granite 4.0 32B Mixture-of-Experts model.

research.ibm.com 2026-04-30 88 HN pts · 16 comments 1.7k words about the granite 4.1 family family

3 best of 3 tracked

The 2 picks above, plus these 1. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

ArticleGranite 4.1 LLMs: How They're Builthuggingface.co · 2026-04-29 · 1 HN pts

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 3 quantized builds.

As a service0no API provider listed
On own hardware3quantized builds
Built on74 fine-tunes · 3 adapters · 0 merges
In apps0Hugging Face Spaces

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 2.4 GB at INT4, 4.8 GB at FP8, 9.6 GB at BF16: weights only, so leave room for the KV cache.

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2026-04-16), amber triangles are 3 dated articles (each links to it).

What people built on it

10 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

10 derivatives on the Hub, led by 4 fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

GGUF ibm-granite/granite-vision-4.1-4b-GGUF for llama.cpp, Ollama, LM Studio 10k downloads MLX inferencerlabs/granite-vision-4.1-4b-MLX-Q9 for Apple silicon 1.3k downloads

All variants →

Reference

Everything else, folded: the full tables, every source.

What the card says

1 cited sentence

Summary generated, cited

IBM Granite Vision 4.1 4B is an image-text-to-text model with 4.0B parameters.^[1]^[2]

parameters human
“image-text-to-text”

Generated 2026-10-23 20:12 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

15 fields

Listed in LLMs

Facts

granite-vision-4.1-4b has 4.00B parameters. (safetensors metadata, observed —)
License: Apache 2.0 (friction: none). Permissive; patent grant. (license file, observed —)
Created 2026-04-16, last modified 2026-07-13. (HF API, observed —)

Pipeline tag	image-text-to-text
Library	transformers
Architectures	Granite4VisionForConditionalGeneration
Model type	granite4_vision
Params by dtype	BF16: 3,997,203,904
Size band	M
MoE	no
Papers	arXiv:2603.27064, arXiv:2208.00385, arXiv:2502.09927, arXiv:2406.04334
License	Apache 2.0 (apache-2.0)
Hub snapshot	2026-10-25

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for granite-vision-4.1-4b: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)	9.6 GB · Single GPU (<24 GB)
FP8 (1 byte/param)	4.8 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)	2.4 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

10 variants

Lineage

Original model Family: ibm-granite/granite-4.1 10 derivatives on the Hub

quantized: 3finetune: 4adapter: 3

Top variants (10 shown, ranked by downloads)

Variant	Relation	Quant	Params	Band	License	Author	Likes	Downloads
ibm-granite/granite-vision-4.1-4b-GGUF	quantized	GGUF	—	—	apache-2.0	ibm-granite	9	10k
inferencerlabs/granite-vision-4.1-4b-MLX-Q9	quantized	MLX	8.08B	M	—	inferencerlabs	0	1.3k
taronaeo/Granite-Vision-4.1-4B-BE-GGUF	quantized	GGUF	—	—	apache-2.0	taronaeo	0	718
heretic-org/granite-vision-4.1-4b-heretic	finetune	—	4.00B	M	apache-2.0	heretic-org	2	325
btbtyler09/shrew-ocr-preview	finetune	—	4.00B	M	apache-2.0	btbtyler09	0	138
iLearn-Lab/CVPRW26-ChartLens	adapter	—	—	—	apache-2.0	iLearn-Lab	0	68
soyrsoyr/granite-vision-4.1-0.2B-tiny	finetune	—	162M	S	—	soyrsoyr	0	56
btbtyler09/shrew-ocr-preview-lora	adapter	—	—	—	apache-2.0	btbtyler09	0	48
inference-optimization/granite-vision-4.1-0.6B-small	finetune	—	565M	S	apache-2.0	inference-optimization	0	40
dineshananthi/colgranite-4.1-4b-lora-72k	adapter	—	—	—	—	dineshananthi	0	35

Repository changelog

1 change

changed in the last 7 daysNew on the watch list: 2026-10-23all changes

Articles and coverage

3 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official2

The IBM Granite 4.1 family of models

research.ibm.com2026-04-30officialauthorityabout the granite 4.1 familyHN 88 pts · 16 comments1,677 words

IBM released the Granite 4.1 family of models under an Apache 2.0 license, offering language models in 3B, 8B, and 30B parameter sizes alongside vision, speech, embedding, and guardian capabilities. These language models are trained on approximately 15 trillion tokens and extend context length to as much as 512K tokens, while the 97M-parameter embedding model supports retrieval across more than 200 languages. Developers can access these models on watsonx, Hugging Face, and via optimized inference runtimes including vLLM, SGLang, and llama.cpp.written, reviewed, cited

Granite Speech 4.1 2B NAR generates entire sequences at once instead of one token at a time.
The Granite 4.1 8B instruct model matches or outperforms the Granite 4.0 32B Mixture-of-Experts model.
Granite Guardian 4.1 is a direct replacement for Granite Guardian 3.3 8B and was fine-tuned on top of Granite 4.1 8B.

Granite 4.1 LLMs: How They're Built

huggingface.co2026-04-29authorityabout the granite 4.1 familyHN 1 pts · 0 comments3,171 words

Granite 4.1 is a family of dense, decoder-only LLMs (3B, 8B, and 30B) released under the Apache 2.0 license. The models are trained on approximately 15 trillion tokens using a five-phase pipeline that extends context windows up to 512K tokens. They are further refined with supervised fine-tuning on approximately 4.1 million high-quality curated samples and reinforcement learning via on-policy GRPO with DAPO loss.written, reviewed, cited

The 8B instruct model matches or surpasses the previous Granite 4.0-H-Small (32B-A9B MoE) despite using a simpler dense architecture.
The LCE phase uses an exponential learning rate schedule starting at 1e-4 and decaying to 0 for long context training.
Due to the computationally intensive nature of dynamic sampling, the team switches the DAPO loss off during training runs.

Analysis1

Granite 4.1: IBM's 8B Model Matching 32B MoE

firethering.com2026-04-30HN front pageabout the granite 4.1 familyHN 328 pts · 209 comments1,865 words

IBM released Granite 4.1, a family of open source models trained on 15 trillion tokens with a rigorous data quality pipeline. The 8B dense model outperforms the previous 32B MoE variant on benchmarks like ArenaHard and GSM8K. Development involved four sequential RL stages and resulted in 4.1 million curated samples.written, reviewed, cited

The 3B model extends context only to 128K tokens, not the 512K available to larger variants.
Phase 5 extended the context window eventually to 512K tokens for the 8B and 30B models.
Stage three was a short identity and knowledge calibration run about 40 training steps to stabilize representation.

Papers

4 papers

Papers 4

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence this model

Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60 · 2025-02-14 · PDF · HF paper page (1 upvotes)
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding cited in card

Jovana Kondic, Pengyuan Li, Dhiraj Joshi +24 · 2026-03-28 · PDF · HF paper page (26 upvotes)
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs cited in card

Lingchen Meng, Jianwei Yang, Rui Tian +4 · 2024-06-06 · PDF · HF paper page
Evaluating Table Structure Recognition: A New Perspective cited in card

Tarun Kumar, Himanshu Sharad Bhatt · 2022-07-31 · PDF · HF paper page

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-23 20:12 UTC · Last verified 2026-10-25

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