gemma-4-E4B

8.00B parameters, several kinds of input and output, 128K tokens of context.

Verified 2026-10-26 07:27 UTC Rank #180 in LLMs · score 90.3 method v1 Sources 3 Downloads 30d 550.1k · #160 of 619 tracked

Changed this week: 2 new quantized builds on the Hub (33 in all).2026-10-24

Can I run it?4.8GB at INT4Estimatefits a 16 GB cardmethod

Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-26

Do people use it?550.1kdownloads, 30 days#160 of 619 we track · top 26%+10 likes, downloads −3% this week478 likesHugging Face2026-10-26

Worth reading

Picked by rule from 42 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 868 HN pts

Gemma 4 on iPhone

apps.apple.com 2026-04-05 868 HN pts · 234 comments about the gemma 4 family family

Latest · 59 days ago

Gemma 4 E2B inference in 700 lines of C

github.com 2026-08-27 18 HN pts 722 words about the gemma 4 family family

25 best of 42 tracked

The 2 picks above, plus these 23. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

CodeShow HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Macgithub.com · 2026-07-29
ArticleGemma 4 12B: A unified, encoder-free multimodal modelblog.google · 2026-06-03 · official
ArticleRunning Gemma 4 26B at 5 tokens/sec on a 13-year-old Xeon with no GPUneomindlabs.com · 2026-07-15
CodeShow HN: Cactus Hybrid: We taught Gemma 4 to know when it's wronggithub.com · 2026-07-22 · 191 HN pts
ArticleGemma 4 QAT models: Optimizing compression for mobile and laptop efficiencyblog.google · 2026-06-05 · 406 HN pts · official
ArticleAccelerating Gemma 4: faster inference with multi-token prediction draftersblog.google · 2026-05-05 · 687 HN pts · official
ArticleGoogle releases Gemma 4 open modelsdeepmind.google · 2026-04-02 · 1,812 HN pts · official
ArticleFable 5 pushed Gemma 4 to 255 tok/s on WebGPUxcancel.com · 2026-06-18 · 48 HN pts
CodeGemma 4 E4B running inside Obsidian via WebGPU and Karpathy-style LLM Wikigithub.com · 2026-09-09 · 4 HN pts
ArticleGoogle Gemma 4 Runs Natively on iPhone with Full Offline AI Inferencegizmoweek.com · 2026-04-15 · 303 HN pts
CodeApril 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac minigist.github.com · 2026-04-03
ArticleShow HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XPtokendelivery.ai · 2026-09-12 · 9 HN pts
ArticleI ran Gemma 4 as a local model in Codex CLIblog.danielvaughan.com · 2026-04-12
ArticleRunning Gemma 4 locally with LM Studio's new headless CLI and Claude Codeai.georgeliu.com · 2026-04-05
ArticleGemma 4 on Cerebras - The Fastest Inference Is Now Multimodalcerebras.ai · 2026-06-30
CodeShow HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicongithub.com · 2026-04-07
ArticleShow HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)teamchong.github.io · 2026-04-19
CodeShow HN: Local privacy-first Microsoft Recall alternative with Gemma 4github.com · 2026-07-04
ArticleGemma 4: Byte for byte, the most capable open modelsblog.google · 2026-04-02 · official
ArticleA Visual Guide to Gemma 4 12Bnewsletter.maartengrootendorst.com · 2026-06-03
CodeShow HN: Local LLM code-generation with Gemma 4 e2B via JSON AST to Clojuregithub.com · 2026-05-19
ArticleShow HN: Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPUapeg.dev · 2026-06-30
ArticleShow HN: I made a Gemma 4 Mac app that names screenshots with local AIsnapname.app · 2026-05-31

Show all 23

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: 33 quantized builds and 14 Spaces.

As a service0no API provider listed
On own hardware33quantized builds
Built on166143 fine-tunes · 23 adapters · 0 merges
In apps14Hugging Face Spaces

Top Spaces running it: build-small-hackathon/chalchitra ♥ 2

Will it fit?

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

Fits a 16 GB card at INT4

About 4.8 GB at INT4, 9.6 GB at FP8, 19 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.

10 dated articles and 6 repo commits so far

Teal is the weights (2026-03-02), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

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

199 derivatives on the Hub, most of them fine-tunes

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

GGUF mradermacher/gemma-4-E4B-GGUF for llama.cpp, Ollama, LM Studio 52.4k downloads MLX majentik/gemma-4-E4B-RotorQuant-MLX-8bit for Apple silicon 1.6k downloads

All variants →

Reference

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

Benchmarks

14 rows

Benchmarks 14 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (14)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.

Benchmark	Value	Setting / notes	Reported by	Date
author-reported AIME 2026 · Reason through hard problems	42.5% percent	Gemma 4 E4B	model author	2026-10-07
author-reported BBEH · Reason through hard problems	33.1% percent	Gemma 4 E4B	model author	2026-10-07
author-reported CoVoST	35.54	Gemma 4 E4B	model author	2026-10-07
author-reported Codeforces · Write code with agents	940 elo	Gemma 4 E4B	model author	2026-10-07
author-reported FLEURS (lower is better)	0.08 other	Gemma 4 E4B	model author	2026-10-07
author-reported GPQA Diamond · Reason through hard problems	58.6% percent	Gemma 4 E4B	model author	2026-10-07
author-reported LiveCodeBench · Write code with agents	52.0% percent	Gemma 4 E4B	model author	2026-10-07
author-reported MMLU-Pro · Reason through hard problems	69.4% percent	Gemma 4 E4B	model author	2026-10-07
author-reported MMMLU · Work across languages	76.6% percent	Gemma 4 E4B	model author	2026-10-07
author-reported MMMU-Pro · Understand images and video	52.6% percent	Gemma 4 E4B	model author	2026-10-07
author-reported MathVision · Understand images and video	59.5% percent	Gemma 4 E4B	model author	2026-10-07
author-reported MedXPertQA MM	28.7% percent	Gemma 4 E4B	model author	2026-10-07
author-reported OmniDocBench · Extract text from documents	0.18 other	Gemma 4 E4B	model author	2026-10-07
author-reported tau-bench · Call tools and APIs	42.2% percent	Gemma 4 E4B	model author	2026-10-07

What the card says

1 cited sentence

Summary generated, cited

This model supports text, image, and audio inputs and is released under the Apache 2.0 license.^[1]^[2]

“Text, Image, Audio”
license

Generated 2026-10-07 15:03 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

9 use cases

What it is good at 9 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

21 fields

Listed in LLMs

Facts

gemma-4-E4B has 8.00B parameters. (safetensors metadata, observed 2026-10-26)
Context length is 131,072 tokens (128K). (config.json max_position_embeddings, observed 2026-10-26)
License: Apache 2.0 (friction: none). Permissive; patent grant. (license file, observed 2026-10-26)
Created 2026-03-02, last modified 2026-07-15. (HF API, observed 2026-10-26)

Pipeline tag	any-to-any
Library	transformers
Architectures	Gemma4ForConditionalGeneration
Model type	gemma4
Dtype	bfloat16
Params by dtype	BF16: 7,996,156,490
Size band	M
MoE	no
Context length	131,072 (config.json max_position_embeddings)
Papers	arXiv:2607.02770
License	Apache 2.0 (apache-2.0)
Hub snapshot	2026-10-26

Servability

vLLM · SGLang

Servability

vLLM: supported · Gemma4ForConditionalGeneration · 4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60SGLang: supported · Gemma4ForConditionalGeneration · 60d941293e3af27a12c6eb64e2c3e2e1a17f100f

vLLM registry (4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60) · SGLang registry (60d941293e3af27a12c6eb64e2c3e2e1a17f100f) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for gemma-4-E4B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)	19 GB · Single GPU (<24 GB)
FP8 (1 byte/param)	9.6 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)	4.8 GB · Single GPU (<24 GB)

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

All variants

40 variants

Lineage

Original model Family: google/gemma-4 199 derivatives on the Hub

quantized: 33finetune: 143adapter: 23

Top variants (40 shown, ranked by downloads)

Variant	Relation	Quant	Params	Band	License	Author	Likes	Downloads
mradermacher/gemma-4-E4B-GGUF	quantized	GGUF	—	—	apache-2.0	mradermacher	6	52.4k
unsloth/gemma-4-E4B	finetune	—	8.00B	M	apache-2.0	unsloth	12	25k
RedHatAI/gemma-4-E4B-it	finetune	—	8.00B	M	apache-2.0	RedHatAI	1	19k
mradermacher/gemma-4-E4B-i1-GGUF	quantized	GGUF	—	—	apache-2.0	mradermacher	1	15.2k
Mungert/gemma-4-E4B-it-GGUF	quantized	GGUF	—	—	apache-2.0	Mungert	0	13.2k
ggml-org/gemma-4-E4B-GGUF	quantized	GGUF	—	—	apache-2.0	ggml-org	2	5.8k
majentik/gemma-4-E4B-RotorQuant-GGUF-IQ4_XS	quantized	GGUF	—	—	apache-2.0	majentik	0	4.7k
LLM-OS-Models/gemma-4-E4B-Terminal-SFT-Native-Liquid-2Epoch	finetune	—	8.19B	M	—	LLM-OS-Models	1	4.2k
OS-Software/gemma-4-E4B-it-uncensored-heretic-GGUF	quantized	GGUF	—	—	apache-2.0	OS-Software	2	4.1k
Lythri/Lythri-7B-A4B	finetune	—	7.46B	M	apache-2.0	Lythri	11	3.6k
LLM-OS-Models/gemma-4-E4B-Terminal-SFT-Native-Liquid-1Epoch	finetune	—	8.19B	M	—	LLM-OS-Models	1	4.1k
Cannae-AI/Gemini-3.1-pro-Gemma-4-E4B-Distill	finetune	—	8.00B	M	apache-2.0	Cannae-AI	9	3.4k
majentik/gemma-4-E4B-RotorQuant-GGUF-Q4_K_M	quantized	GGUF	—	—	apache-2.0	majentik	2	3.6k
diarizers-community/DiarizationLM-Gemma-4-E4B-v1	quantized	GGUF	8.00B	M	apache-2.0	diarizers-community	1	3.6k
Zainab-Jamshaid/Gemini-3.1-pro-Gemma-4-E4B-Distill	finetune	—	8.00B	M	apache-2.0	Zainab-Jamshaid	0	2.3k
0penAGI/0pen	adapter	GGUF	—	—	mit	0penAGI	4	2k
majentik/gemma-4-E4B-RotorQuant-GGUF-Q5_K_M	quantized	GGUF	—	—	apache-2.0	majentik	0	1.9k
majentik/gemma-4-E4B-RotorQuant-GGUF-Q3_K_M	quantized	GGUF	—	—	apache-2.0	majentik	0	1.8k
majentik/gemma-4-E4B-turboquant	finetune	—	8.00B	M	apache-2.0	majentik	9	1.3k
Roborn/Gemini-3.1-pro-Gemma-4-E4B-Distill	finetune	—	8.00B	M	apache-2.0	Roborn	1	1.6k
majentik/gemma-4-E4B-RotorQuant-MLX-8bit	quantized	MLX	8.00B	M	apache-2.0	majentik	1	1.6k
majentik/gemma-4-E4B-RotorQuant-GGUF-Q2_K	quantized	GGUF	—	—	apache-2.0	majentik	0	1.6k
L0uu/gemma4-e4b-etafakna-gguf	adapter	gguf	—	—	gemma	L0uu	1	1.4k
majentik/gemma-4-E4B-RotorQuant-MLX-4bit	quantized	MLX	8.00B	M	apache-2.0	majentik	1	1.3k
majentik/gemma-4-E4B-RotorQuant-GGUF-Q8_0	quantized	GGUF	—	—	apache-2.0	majentik	0	1.2k
zanish-labs/gemma-4-E4B-it-Q4_K_M-gguf	quantized	gguf	—	—	apache-2.0	zanish-labs	0	1.2k
majentik/gemma-4-E4B-RotorQuant-MLX-2bit	quantized	MLX	8.00B	M	apache-2.0	majentik	0	1.2k
Phonsiri/Thai-Legal-Gemma-4B-CPT	finetune	—	7.94B	M	gemma	Phonsiri	0	1.1k
SandLogicTechnologies/gemma-4-E4B-GGUF	quantized	GGUF	—	—	apache-2.0	SandLogicTechnologies	0	958
thai00/hypegemma-4-e4b	finetune	—	7.94B	M	apache-2.0	thai00	1	897
JMingo/gemma-4-E4B-it-GGUF	quantized	GGUF	—	—	apache-2.0	JMingo	0	872
Felldude/gemma-4-E4B-uncensored	finetune	—	7.94B	M	apache-2.0	Felldude	2	743
TheUmbraWalker/gemma-4-E4B-it-2-heretic	finetune	—	7.94B	M	apache-2.0	TheUmbraWalker	2	631
kinguniverse129/gemma-4-E4B-4bit	quantized	-4bit	7.52B	M	apache-2.0	kinguniverse129	0	720
Jerem62/gemma-4-E4B-Q5_K_M-GGUF	quantized	GGUF	—	—	apache-2.0	Jerem62	0	715
nikimori/gemma-4-e4b-6bit-slim	quantized	—	7.52B	M	apache-2.0	nikimori	1	597
sweetprincessluna/gemma-4-E4B-Q4_K_S-GGUF	quantized	GGUF	—	—	apache-2.0	sweetprincessluna	0	634
Jerem62/gemma-4-E4B-Q6_K-GGUF	quantized	Q6_K	—	—	apache-2.0	Jerem62	0	593
Siarhei/gemma-4-E4B-4bit	quantized	-4bit	7.52B	M	apache-2.0	Siarhei	1	515
ContentLens-AI/Audio-optim	quantized	—	8.19B	M	apache-2.0	ContentLens-AI	1	494

Repository changelog

6 commits

changed in the last 7 daysOther: 2026-10-24all changes

Repository changelog 6 commits

Date	Commit	SHA
2026-07-15	Update README (#6)	411aa17
2026-07-10	Add Technical Report	58c4d7b
2026-06-03	Update README.md	a24c937
2026-06-02	Update README.md	4f1d443
2026-04-02	Upload tokenizer.json	7aa32e6
2026-04-02	Preparing for release!	9f9f0f2

Articles and coverage

42 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.

Official3

Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency

blog.google2026-06-05officialauthorityHN front pageabout the gemma 4 familyHN 406 pts · 130 comments714 words

Google released new Gemma 4 checkpoints optimized with Quantization-Aware Training to improve efficiency on edge devices and consumer GPUs. The E2B model achieves a 1GB memory footprint using a mobile-specialized schema that applies targeted 2-bit quantization to token generation layers. Developers can access GGUF formats ready for llama.cpp and MTP QAT checkpoints to preserve inference speedups while running locally.written, reviewed, cited

The mobile-specialized schema pre-calculates activation scaling settings during training to reduce workload on mobile chips.
Targeted 2-bit quantization compresses token generation parts while keeping core reasoning layers at higher precision.
Deploying only needed modalities allows the Gemma 4 E2B text-only model to require less than 1 GB of memory.

Accelerating Gemma 4: faster inference with multi-token prediction drafters

blog.google2026-05-05officialauthorityHN front pageabout the gemma 4 familyHN 687 pts · 330 comments796 words

Google released Multi-Token Prediction (MTP) drafters for the Gemma 4 family under the same open-source Apache 2.0 license as Gemma 4. These drafters deliver up to a 3x speedup for models like the 26B MoE and 31B Dense versions without quality degradation. Benchmarks on hardware such as the NVIDIA RTX PRO 6000 show that increasing batch sizes to 4 to 8 unlocks up to a ~2.2x speedup locally.written, reviewed, cited

The MTP drafter shares the target model's KV cache to avoid recalculating context the larger model has already processed.
For E2B and E4B edge models, an efficient clustering technique in the embedder accelerates generation where logit calculation is a bottleneck.
The 26B mixture-of-experts model presents unique routing challenges at a batch size of 1 on Apple Silicon.

Google releases Gemma 4 open models

deepmind.google2026-04-02officialauthorityHN front pageabout the gemma 4 familyHN 1,812 pts · 474 comments253 words

Analysis7

Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong

github.com2026-07-22authorityHN front pageabout the gemma 4 familyHN 191 pts · 44 comments714 words

Cactus Hybrid introduces on-device models that score every answer with a confidence between 0 and 1 to decide when to hand off to a bigger model. The Gemma 4 E2B Hybrid model matches Gemini 3.1 Flash-Lite performance by routing only 15–55% of queries to the cloud while running the rest locally. It is MIT-licensed and achieves a 0.770 AUROC on MMLU, with a probe trained on zero audio data yet reaching 0.79–0.88 AUROC on four audio benchmarks.written, reviewed, cited

Weights on the meta device crash the confidence read because probes score generations outside the module forward path.
The probe is a patch you compile into the llama.cpp engine rather than a standalone library component.
Transformers version 5.14 and above cause a segfault on this specific checkpoint.

Gemma 4 E2B inference in 700 lines of C

github.com2026-08-27authorityabout the gemma 4 familyHN 18 pts · 0 comments722 words

Fable 5 pushed Gemma 4 to 255 tok/s on WebGPU

xcancel.com2026-06-18about the gemma 4 familyHN 48 pts · 22 comments

Gemma 4 on iPhone

apps.apple.com2026-04-05HN front pageabout the gemma 4 familyHN 868 pts · 234 comments

Gemma 4 E4B running inside Obsidian via WebGPU and Karpathy-style LLM Wiki

github.com2026-09-09authorityHN 4 pts · 1 comments

Google Gemma 4 Runs Natively on iPhone with Full Offline AI Inference

gizmoweek.com2026-04-15HN front pageabout the gemma 4 familyHN 303 pts · 187 comments364 words

Show HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XP

tokendelivery.ai2026-09-12about the gemma 4 familyHN 9 pts · 1 comments

All coverage tracked for this model (42) — append-only, nothing is ever removed

Title	Source	Published	First seen	Signals
Gemma 4 Technical Report	arxiv.org	—	2026-10-07	exa
Gemma 4: Frontier multimodal intelligence on device	huggingface.co	—	2026-10-07	exa
Show HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XP	tokendelivery.ai	2026-09-12	2026-10-07	hn
Gemma 4 E4B running inside Obsidian via WebGPU and Karpathy-style LLM Wiki	github.com	2026-09-09	2026-10-07	hn
Gemma 4 E2B inference in 700 lines of C	github.com	2026-08-27	2026-10-07	hn
What it takes to run Gemma 4 E4B on an iPhone	amoli.app	2026-08-25	2026-10-07	hn
Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac	github.com	2026-07-29	2026-10-07	hn
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong	github.com	2026-07-22	2026-10-07	hn
Running Gemma 4 26B at 5 tokens/sec on a 13-year-old Xeon with no GPU	neomindlabs.com	2026-07-15	2026-10-07	hn
Show HN: Local privacy-first Microsoft Recall alternative with Gemma 4	github.com	2026-07-04	2026-10-07	hn
Show HN: Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU	apeg.dev	2026-06-30	2026-10-07	hn
Gemma 4 on Cerebras - The Fastest Inference Is Now Multimodal	cerebras.ai	2026-06-30	2026-10-07	hn
Fable 5 pushed Gemma 4 to 255 tok/s on WebGPU	xcancel.com	2026-06-18	2026-10-07	hn
Gemma 4 E4B as a primary local LLM (replaced Qwen)	digg.com	2026-06-07	2026-10-07	hn
Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency	blog.google official	2026-06-05	2026-10-07	hn
Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM	arstechnica.com	2026-06-03	2026-10-07	hn
A Visual Guide to Gemma 4 12B	newsletter.maartengrootendorst.com	2026-06-03	2026-10-07	hn
Gemma 4 12B: A unified, encoder-free multimodal model	blog.google official	2026-06-03	2026-10-07	hn
Show HN: I made a Gemma 4 Mac app that names screenshots with local AI	snapname.app	2026-05-31	2026-10-07	hn
Show HN: Local LLM code-generation with Gemma 4 e2B via JSON AST to Clojure	github.com	2026-05-19	2026-10-07	hn
Grafting a Speech Head onto Gemma 4 E4B	frisson-labs.com	2026-05-08	2026-10-07	hn
Accelerating Gemma 4: faster inference with multi-token prediction drafters	blog.google official	2026-05-05	2026-10-07	hn
Using Google's Gemma 4 E4B Local AI Model to Reverse Engineer a Simple Crackme	github.com	2026-04-27	2026-10-07	hn
A weekend with LoRA on Gemma 4 E2B: instrumenting what fine-tuning changes	aiexplr.com	2026-04-26	2026-10-07	hn
Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)	teamchong.github.io	2026-04-19	2026-10-07	hn
Google Gemma 4 Runs Natively on iPhone with Full Offline AI Inference	gizmoweek.com	2026-04-15	2026-10-07	hn
Show HN: I benchmarked Gemma 4 E2B – the 2B model beat the 12B on multi-turn	aiexplr.com	2026-04-13	2026-10-07	hn
I ran Gemma 4 as a local model in Codex CLI	blog.danielvaughan.com	2026-04-12	2026-10-07	hn
Gemma 4 E2B vs E4B Benchmark: The Hidden Thinking Mode That Makes the Smaller Model 20× Slower — the KodeLab	thekodelab.com	2026-04-11	2026-10-07	exa
A Visual Guide to Gemma 4	newsletter.maartengrootendorst.com	2026-04-09	2026-10-07	hn, exa
Gemma 4 Benchmarks: Ultimate Local AI Performance Guide 2026 - Gemma 4 Wiki	gemma4.wiki	2026-04-09	2026-10-07	exa
Gemma 4 E4B vs. Gemma Family: Enterprise Benchmark Across 8 Task Suites	aiexplorer-blog.vercel.app	2026-04-08	2026-10-07	hn
Show HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicon	github.com	2026-04-07	2026-10-07	hn
Gemma 4 on iPhone	apps.apple.com	2026-04-05	2026-10-07	hn
Running Gemma 4 locally with LM Studio's new headless CLI and Claude Code	ai.georgeliu.com	2026-04-05	2026-10-07	hn
Gemma 4: Google DeepMind's Most Intelligent Open Models \| AI \| Tobias Weiss	tobias-weiss.org	2026-04-04	2026-10-07	exa
Google Gemma 4 Review: Open Source AI Agent Model 2026 \| by Jahanzaib Ahmed \| Apr, 2026 \| Medium	medium.com	2026-04-04	2026-10-07	exa
April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini	gist.github.com	2026-04-03	2026-10-07	hn
Gemma 4 Deep Dive — How Apache 2.0 Opens the Door to Sovereign AI \| Pebblous	blog.pebblous.ai	2026-04-03	2026-10-07	exa
Google releases Gemma 4 open models	deepmind.google official	2026-04-02	2026-10-07	hn, exa
Gemma 4: Byte for byte, the most capable open models	blog.google official	2026-04-02	2026-10-07	hn
Gemma 4 Architecture and Benchmark Notes \| Sebastian Raschka, PhD	sebastianraschka.com	2026-04-02	2026-10-07	exa

Papers

1 paper

Papers 1

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).

Gemma 4 Technical Report this model

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320 · 2026-07-02 · PDF · HF paper page (85 upvotes)

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Sources

3 sources

Sources

Hub snapshot: 2026-10-26 · HF API: 2026-10-26 07:17 UTC · Providers: 2026-10-26 07:26 UTC · Articles: 2026-10-26 07:27 UTC · Summary: 2026-10-07 15:03 UTC · Last verified 2026-10-26 07:27 UTC

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