gemma-3-12b-it

google Gemma Terms of Use out 20 months ago

12.2B parameters, images and text in, text out, 128K tokens of context. 3 providers serve it from $0.040 per million input tokens.

Verified 2026-10-25 Rank #63 in LLMs · score 105.1 method v1 Sources 4 Downloads 30d 330.4k · #209 of 619 tracked

Changed this week: Cheapest input price fell 20%: $0.050 → $0.040 per 1M (OpenRouter).2026-10-24

Can I run it?7.3GB at INT4Estimatefits a 16 GB cardmethod

What does it cost?$0.040/1M incheapest of 2DeepInfrabfloat162026-10-25

Can I use it?Gemma Terms of UseGoogle's terms + prohibited-use policy; must pass terms downstreamlicence file2026-10-24

Does it work?30.43on BFCL#18 of 28 measuredleaderboard2026-10-25

Do people use it?330.4kdownloads, 30 days#209 of 619 we track · top 34%+10 likes, downloads −11% this week883 likesHugging Face2026-10-24

Worth reading

Picked by rule from 35 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 488 HN pts

Gemma 3 Technical Report [pdf]

storage.googleapis.com 2025-03-12 488 HN pts · 245 comments about the gemma 3 family family

Latest · 4 months ago

Show HN: Gemma 3 inference in pure C++ with Metal acceleration

MetalChat is a Metal-accelerated C++ framework and command line interpreter for inference of Meta Llama, Google Gemma, and Liquid models.

github.com 2026-07-04 6 HN pts · 2 comments 193 words about the gemma 3 family family

25 best of 35 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.

CodeOpen-jev: One-pass option scoring with Gemma 3 4B, similar to jevgithub.com · 2026-09-17
ArticleGemma 3 270M: Compact model for hyper-efficient AIdevelopers.googleblog.com · 2025-08-14 · official
ArticleGemma 3 QAT Models: Bringing AI to Consumer GPUsdevelopers.googleblog.com · 2025-04-18 · 602 HN pts · official
ArticleIntroducing Gemma 3ndevelopers.googleblog.com · 2025-06-26 · 405 HN pts · official
ArticleGemma 3n preview: Mobile-first AIdevelopers.googleblog.com · 2025-05-20 · 441 HN pts · official
ArticleGemma3 Function Callingai.google.dev · 2025-03-23 · 202 HN pts · official
ArticleFine-tune Google's Gemma 3unsloth.ai · 2025-03-19 · 226 HN pts
CodeGemma 3 270M re-implemented in pure PyTorch for local tinkeringgithub.com · 2025-08-20
CodeI have written gemma3 inference in pure Cgithub.com · 2026-01-26 · 65 HN pts
ArticleGemma3 – The current strongest model that fits on a single GPUollama.com · 2025-03-12 · 252 HN pts
ArticleGoogle calls Gemma 3 the most powerful AI model you can run on one GPUtheverge.com · 2025-03-20 · 127 HN pts
Paper[2503.19786] Gemma 3 Technical Reportar5iv.labs.arxiv.org · undated
ArticleGemma 3: Google's new multimodal modelsai.google.dev · 2025-03-12 · official
ArticleGemma 3: The most capable model you can run on a single GPU or TPUblog.google · 2025-03-12 · official
ArticleOwn your AI: Learn how to fine-tune Gemma 3 270M and run it on-devicedevelopers.googleblog.com · 2025-10-10 · official
ArticleGemma 3 QAT Modelssimonwillison.net · 2025-04-19
DiscussionGemma 3n Architectural Innovations – Speculation and poking around in the modelold.reddit.com · 2025-05-25
ArticleGemma 3n available in the open-source ecosystemhuggingface.co · 2025-06-26
ArticleGemma 3n: The Developer Guidesimonwillison.net · 2025-06-27
ArticleLandrecords – cheap nationwide parcel dataset standardized using gemma3landrecords.us · 2025-08-20
CodeA C++ library to efficiently run Gemma-3N across various platformgithub.com · 2025-06-11
ArticleSelectively reducing eval awareness and murder in Gemma 3 27B via steeringlesswrong.com · 2026-03-12
ArticleI Gave Up Trying to Run Gemma 3 27B on AWS P3dn.24xlargemedium.com · 2025-08-11

Show all 23

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

Prices for the same weights vary 2×

2 providers with a live price, from $0.040 to $0.075 per million input tokens; the median is $0.057. One dot per provider, cheapest in teal.

#	Cheapest 3	In /1M	Out /1M	Context	Quant	Seen
1	DeepInfra	$0.040	$0.12	128K	bfloat16	Oct 25
2	NextBit	$0.075	$0.15	128K	int4	Oct 24
3	Featherless	—	—	—	—	Oct 25

All 3 providers →

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.

Served by 3 API providers; heavily run locally (164 quantized builds); a big base for builders (869 fine-tunes, adapters and merges).

As a service3DeepInfra, Featherless, NextBit
On own hardware164quantized builds
Built on869424 fine-tunes · 434 adapters · 11 merges
In apps100+Hugging Face Spaces

Top Spaces running it: pliny-the-prompter/obliteratus ♥ 621 · huggingface-projects/gemma-3-12b-it ♥ 165 · build-small-hackathon/claim-ready ♥ 71 · ginigen-ai/smol-worldcup ♥ 59

Will it fit?

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

Fits a 16 GB card at INT4

About 7.3 GB at INT4, 15 GB at FP8, 29 GB at BF16: weights only, so leave room for the KV cache.

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

How it scores

Third-party measurements only; author-reported numbers stay in Reference, marked as such.

#18 of 28 on BFCL so far

30.43. Each tick is a model on this site with a measured BFCL row; author-reported numbers stay in Reference. Percent (overall accuracy). Prompt

Source · leaderboard · 2026-10-25

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 (2025-03-01), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

1,033 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

1,033 derivatives on the Hub, led by 434 adapters

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

GGUF MaziyarPanahi/gemma-3-12b-it-GGUF for llama.cpp, Ollama, LM Studio 3.4M downloads INT4 gaunernst/gemma-3-12b-it-int4-awq for vLLM, SGLang 905.2k downloads

All variants →

Reference

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

Benchmarks

17 rows

Benchmarks 17 rows

measured third-party leaderboard row (1)author-reported number from the model card or eval files (16)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
measured BFCL · Call tools and APIs	30.43 percent (overall accuracy)	Prompt 5 sub-scores Non-Live AST Acc: 79.44 Live Acc: 74.24 Multi Turn Acc: 5.75 Web Search Acc: 4 Memory Acc: 27.53	leaderboard	2026-10-25
author-reported AGIEval	57.4	Gemma 3 PT 12B	model author	2026-10-07
author-reported ARC Challenge · Chat and follow instructions	68.9	Gemma 3 PT 12B	model author	2026-10-07
author-reported BBH · Reason through hard problems	72.6	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	78.8	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	81.8	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	53.4	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	78.2	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	31.4	Gemma 3 PT 12B	model author	2026-10-07
author-reported Commonsense QA sets · Chat and follow instructions	88.3	Gemma 3 PT 12B	model author	2026-10-07
author-reported DROP	72.2	Gemma 3 PT 12B	model author	2026-10-07
author-reported GSM8K · Reason through hard problems	71	Gemma 3 PT 12B	model author	2026-10-07
author-reported HellaSwag · Chat and follow instructions	84.2	Gemma 3 PT 12B	model author	2026-10-07
author-reported MATH-500 · Reason through hard problems	43.3	Gemma 3 PT 12B	model author	2026-10-07
author-reported MMLU · Reason through hard problems	74.5	Gemma 3 PT 12B	model author	2026-10-07
author-reported MMLU-Pro · Reason through hard problems	45.3	Gemma 3 PT 12B	model author	2026-10-07
author-reported Winogrande · Chat and follow instructions	74.3	Gemma 3 PT 12B	model author	2026-10-07

What the card says

2 cited sentences

Summary generated, cited

The model uses a Gemma3ForConditionalGeneration architecture to process text and image inputs for text generation tasks.^[1] It is released under the Gemma Terms of Use and supports a 128K context window.^[2]^[3]

“Gemma3ForConditionalGeneration”
license
“128K context window”

Generated 2026-10-07 15:00 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

7 use cases

What it is good at 7 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

20 fields

Listed in LLMs

Facts

gemma-3-12b-it has 12.2B parameters. (safetensors metadata, observed 2026-10-24)
Context length is 131,072 tokens (128K). (provider listing, observed 2026-10-24)
License: Gemma Terms of Use (friction: attribution). Google's terms + prohibited-use policy; must pass terms downstream. (license file, observed 2026-10-24)
Created 2025-03-01, last modified 2025-03-21. (HF API, observed 2026-10-24)
Access is gated on Hugging Face (manual). (HF API, observed 2026-10-24)

Pipeline tag	image-text-to-text
Library	transformers
Architectures	Gemma3ForConditionalGeneration
Model type	gemma3
Dtype	bfloat16
Params by dtype	BF16: 12,187,325,040
Size band	L
MoE	no
Context length	131,072 (provider listing)
Gated	manual
Papers	arXiv:1905.07830, arXiv:1905.10044, arXiv:1911.11641, arXiv:1904.09728, arXiv:1705.03551, arXiv:1911.01547, arXiv:1907.10641, arXiv:1903.00161, arXiv:2009.03300, arXiv:2304.06364, arXiv:2103.03874, arXiv:2110.14168, arXiv:2311.12022, arXiv:2108.07732, arXiv:2107.03374, arXiv:2210.03057, arXiv:2106.03193, arXiv:1910.11856, arXiv:2502.12404, arXiv:2502.21228, arXiv:2404.16816, arXiv:2104.12756, arXiv:2311.16502, arXiv:2203.10244, arXiv:2404.12390, arXiv:1810.12440, arXiv:1908.02660, arXiv:2312.11805
License	Gemma Terms of Use (gemma)
Hub snapshot	2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Gemma3ForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Gemma3ForConditionalGeneration · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · 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-3-12b-it: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)	29 GB · Single high-end GPU (<80 GB)
FP8 (1 byte/param)	15 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)	7.3 GB · Single GPU (<24 GB)

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

All variants

40 variants

Lineage

Fine-tuned from google/gemma-3-12b-pt (same organization) Family: google/gemma-3 1,033 derivatives on the Hub

Base model: google/gemma-3-12b-pt

quantized: 164finetune: 424merge: 11adapter: 434

Top variants (40 shown, ranked by downloads)

Variant	Relation	Quant	Params	Band	License	Author	Likes	Downloads
MaziyarPanahi/gemma-3-12b-it-GGUF	quantized	GGUF	—	—	—	MaziyarPanahi	19	3.4M
Comfy-Org/ltx-2	finetune	—	—	—	other	Comfy-Org	168	3M
abhishekchohan/gemma-3-12b-it-quantized-W4A16	quantized	-quant	12.3B	L	gemma	abhishekchohan	8	2.5M
unsloth/gemma-3-12b-it-GGUF	quantized	GGUF	—	—	gemma	unsloth	202	1.5M
lmstudio-community/gemma-3-12b-it-GGUF	quantized	GGUF	—	—	gemma	lmstudio-community	45	1.5M
unsloth/gemma-3-12b-it-unsloth-bnb-4bit	quantized	-bnb	12.4B	L	gemma	unsloth	23	1.2M
gaunernst/gemma-3-12b-it-int4-awq	quantized	int4	12.2B	L	gemma	gaunernst	25	905.2k
google/gemma-3-12b-it-qat-q4_0-gguf	quantized	q4_0	—	—	gemma	google	304	845.1k
google/gemma-3-12b-it-qat-q4_0-unquantized	finetune	q4_0	12.2B	L	gemma	google	208	515.4k
ggml-org/gemma-3-12b-it-GGUF	quantized	GGUF	—	—	gemma	ggml-org	32	437.6k
bartowski/google_gemma-3-12b-it-GGUF	quantized	GGUF	—	—	gemma	bartowski	51	308.9k
Lightricks/gemma-3-12b-it-qat-q4_0-unquantized	finetune	q4_0	12.2B	L	gemma	Lightricks	9	247.2k
ISTA-DASLab/gemma-3-12b-it-GPTQ-4b-128g	quantized	GPTQ	12.3B	L	gemma	ISTA-DASLab	6	200.9k
unsloth/gemma-3-12b-it-bnb-4bit	quantized	-bnb	12.5B	L	gemma	unsloth	45	187.8k
RedHatAI/gemma-3-12b-it-quantized.w4a16	quantized	-quant	13.3B	L	gemma	RedHatAI	3	174.5k
farbodtavakkoli/OTel-LLM-12B-Safety	finetune	—	—	—	apache-2.0	farbodtavakkoli	0	174.3k
farbodtavakkoli/OTel-LLM-12B-IT	finetune	—	—	—	apache-2.0	farbodtavakkoli	0	166.8k
DreamFast/gemma-3-12b-it-heretic	quantized	GGUF	12.2B	L	gemma	DreamFast	94	145.6k
mlabonne/gemma-3-12b-it-abliterated	finetune	—	12.2B	L	gemma	mlabonne	33	139.2k
lmstudio-community/gemma-3-12B-it-qat-GGUF	quantized	GGUF	—	—	gemma	lmstudio-community	13	131.4k
mlabonne/gemma-3-12b-it-abliterated-GGUF	quantized	GGUF	—	—	gemma	mlabonne	55	118k
DreamFast/gemma-3-12b-it-heretic-v2	quantized	GGUF	12.2B	L	gemma	DreamFast	104	79.7k
pytorch/gemma-3-12b-it-FP8	quantized	-FP8	—	—	apache-2.0	pytorch	2	70.3k
leon-se/gemma-3-12b-it-FP8-Dynamic	quantized	-FP8	12.2B	L	gemma	leon-se	13	67.1k
RedHatAI/gemma-3-12b-it-FP8-dynamic	quantized	-FP8	12.2B	L	apache-2.0	RedHatAI	7	60.2k
NeoChen1024/gemma-3-12b-it-NVFP4	quantized	NVFP4	8.49B	M	gemma	NeoChen1024	2	57.3k
pytorch/gemma-3-12b-it-AWQ-INT4	quantized	AWQ	—	—	apache-2.0	pytorch	1	48.6k
TheDrummer/Tiger-Gemma-12B-v3-GGUF	quantized	GGUF	—	—	—	TheDrummer	23	43.5k
INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0	finetune	—	12.2B	L	gemma	INSAIT-Institute	22	39.1k
RedHatAI/gemma-3-12b-it-quantized.w8a8	quantized	-quant	13.2B	L	gemma	RedHatAI	6	35.2k
pytorch/gemma-3-12b-it-INT4	quantized	INT4	2.80B	M	apache-2.0	pytorch	2	34.9k
google/gemma-3-12b-it-qat-int4-unquantized	finetune	int4	12.2B	L	gemma	google	12	34.3k
nocturne23/gemma-3-12b-it-Q4_K_M-GGUF	quantized	GGUF	—	—	gemma	nocturne23	9	27.3k
Andycurrent/gemma-3-12b-it-uncensored-GGUF	quantized	GGUF	—	—	gemma	Andycurrent	11	23.7k
DavidAU/Gemma-3-12b-it-MAX-HORROR-Imatrix-GGUF	quantized	Imatrix	—	—	gemma	DavidAU	17	23k
allura-org/Gemma-3-Glitter-12B	merge	—	12.2B	L	—	allura-org	28	20.7k
TheDrummer/Tiger-Gemma-12B-v3	finetune	—	—	—	—	TheDrummer	32	19.7k
second-state/gemma-3-12b-it-GGUF	quantized	GGUF	—	—	gemma	second-state	2	20.9k
mlabonne/gemma-3-12b-it-abliterated-v2	finetune	—	11.8B	L	gemma	mlabonne	30	16.9k
gaunernst/gemma-3-12b-it-qat-autoawq	quantized	awq	12.2B	L	gemma	gaunernst	7	17.9k

Repository changelog

6 commits

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

Repository changelog 6 commits

Date	Commit	SHA
2025-03-21	Update README.md (#15)	96b6f1e
2025-03-21	Update README.md (#14)	bf0e2fc
2025-03-19	Adding top_k and top_p to generation_config.json	d82ecc1
2025-03-12	Update README.md	7553b6f
2025-03-12	Update chat_template (#4)	6df4c7d
2025-03-12	Super-squash branch 'main' using huggingface_hub	3aa32c4

All providers

3 providers · 3 endpoints

Where to run 3 providers · 3 endpoints

Hosted endpoints observed for gemma-3-12b-it, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.04/M.

Provider	Model ref	Context	Quant	$/M in	$/M out	tok/s	TTFT	Tools	Status	Observed
DeepInfra hf, deepinfra, openrouter	google/gemma-3-12b-it	128K	bfloat16	$0.04	$0.12	72	302 ms	yes	live	2026-10-25
NextBit openrouter	google/gemma-3-12b-it	128K	int4	$0.07	$0.15	—	—	—	live	2026-10-24
Featherless hf	google/gemma-3-12b-it	—	—	—	—	—	—	—	live	2026-10-25

Articles and coverage

35 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.

Official4

Gemma 3 QAT Models: Bringing AI to Consumer GPUs

developers.googleblog.com2025-04-18officialauthorityHN front pageabout the gemma 3 familyHN 602 pts · 276 comments1,077 words

Google released Gemma 3 Quantization-Aware Training (QAT) models that incorporate quantization during training to reduce memory usage while maintaining accuracy. Applying QAT on ~5,000 steps reduces the perplexity drop by 54% when quantizing down to Q4_0, allowing the 27B variant to fit in 14.1 GB of VRAM instead of 54 GB. These int4 models run efficiently on consumer hardware like the NVIDIA RTX 3090 and are available via Ollama, llama.cpp, and MLX.written, reviewed, cited

Deploying the largest models often requires lower-precision formats like FP8, potentially accepting a performance trade-off for feasibility.
The Gemmaverse offers many alternatives using Post-Training Quantization (PTQ) with contributions from members such as Bartowski, Unsloth, and GGML.

Introducing Gemma 3n

developers.googleblog.com2025-06-26officialauthorityHN front pageabout the gemma 3 familyHN 405 pts · 191 comments1,690 words

Gemma 3n is a mobile-first AI model family designed for on-device use with multimodal capabilities. The E2B and E4B versions have raw parameters of 5B and 8B but operate with as little as 2GB and 3GB of memory respectively. It supports 140 languages for text and includes a MobileNet-V5-300M vision encoder that delivers a 13x speedup with quantization, requires 46% fewer parameters, and has a 4x smaller memory footprint.written, reviewed, cited

Developers can create custom-sized models between E2B and E4B by adjusting feed forward network hidden dimensions from 8192 to 16384.
The Gemma 3n audio encoder processes clips up to 30 seconds at launch but supports arbitrarily long audios with additional training.

Gemma 3n preview: Mobile-first AI

developers.googleblog.com2025-05-20officialauthorityHN front pageabout the gemma 3 familyHN 441 pts · 163 comments904 words

Gemma3 Function Calling

ai.google.dev2025-03-23officialauthorityHN front pageabout the gemma 3 familyHN 202 pts · 32 comments2,144 words

Analysis6

Show HN: Gemma 3 inference in pure C++ with Metal acceleration

github.com2026-07-04authorityabout the gemma 3 familyHN 6 pts · 2 comments193 words

MetalChat is a Metal-accelerated C++ framework and command line interpreter for inference of Meta Llama, Google Gemma, and Liquid models. The library API and CLI are under active development, therefore they may change without any deprecation notice. The framework is distributed under GPLv3 license and can be installed using Homebrew package manager with brew tap ybubnov/metalchat.written, reviewed, cited

Fine-tune Google's Gemma 3

unsloth.ai2025-03-19authorityHN front pageabout the gemma 3 familyHN 226 pts · 78 comments

Gemma 3 Technical Report [pdf]

storage.googleapis.com2025-03-12HN front pageabout the gemma 3 familyHN 488 pts · 245 comments

I have written gemma3 inference in pure C

github.com2026-01-26authorityabout the gemma 3 familyHN 65 pts · 23 comments2,207 words

Gemma3 – The current strongest model that fits on a single GPU

ollama.com2025-03-12HN front pageabout the gemma 3 familyHN 252 pts · 138 comments

Google calls Gemma 3 the most powerful AI model you can run on one GPU

theverge.com2025-03-20HN front pageabout the gemma 3 familyHN 127 pts · 100 comments

All coverage tracked for this model (35) — append-only, nothing is ever removed

Title	Source	Published	First seen	Signals
[2503.19786] Gemma 3 Technical Report	ar5iv.labs.arxiv.org	—	2026-10-07	exa
Gemma 3 12B \| Open Laboratory	openlaboratory.com	—	2026-10-07	exa
Gemma 3 12B IT · LLM Model Review \| CrucibleMark	cruciblemark.com	—	2026-10-07	exa
gemma-3-12b — 12B Dense LLM \| FitMyLLM	fitmyllm.com	—	2026-10-07	exa
Gemma 3 12B: Benchmarks, Pricing & Context Window — Benchgen	benchgen.com	—	2026-10-07	exa
Gemma-3-12b-it: Open Multimodal Transformer	emergentmind.com	—	2026-10-07	exa
Open-jev: One-pass option scoring with Gemma 3 4B, similar to jev	github.com	2026-09-17	2026-10-07	hn
Show HN: Gemma 3 inference in pure C++ with Metal acceleration	github.com	2026-07-04	2026-10-07	hn
Google Gemma 3 — Open Weights, 128K Context, and the QAT Advantage — ChatForest	chatforest.com	2026-05-12	2026-10-07	exa
Selectively reducing eval awareness and murder in Gemma 3 27B via steering	lesswrong.com	2026-03-12	2026-10-07	hn
I have written gemma3 inference in pure C	github.com	2026-01-26	2026-10-07	hn
Own your AI: Learn how to fine-tune Gemma 3 270M and run it on-device	developers.googleblog.com official	2025-10-10	2026-10-07	hn
Gemma 3 270M re-implemented in pure PyTorch for local tinkering	github.com	2025-08-20	2026-10-07	hn
Landrecords – cheap nationwide parcel dataset standardized using gemma3	landrecords.us	2025-08-20	2026-10-07	hn
Gemma 3 270M: Compact model for hyper-efficient AI	developers.googleblog.com official	2025-08-14	2026-10-07	hn
I Gave Up Trying to Run Gemma 3 27B on AWS P3dn.24xlarge	medium.com	2025-08-11	2026-10-07	hn
Gemma 3n: The Developer Guide	simonwillison.net	2025-06-27	2026-10-07	hn
Gemma 3n available in the open-source ecosystem	huggingface.co	2025-06-26	2026-10-07	hn
Introducing Gemma 3n	developers.googleblog.com official	2025-06-26	2026-10-07	hn
A C++ library to efficiently run Gemma-3N across various platform	github.com	2025-06-11	2026-10-07	hn
Gemma 3n Architectural Innovations – Speculation and poking around in the model	old.reddit.com	2025-05-25	2026-10-07	hn
Gemma 3n preview: Mobile-first AI	developers.googleblog.com official	2025-05-20	2026-10-07	hn
Gemma 3 QAT Models	simonwillison.net	2025-04-19	2026-10-07	hn
QAT optimized Gemma 3 slash VRAM needs 54GB –> 14.1GB while maintaining quality	old.reddit.com	2025-04-19	2026-10-07	hn
Gemma 3 QAT Models: Bringing AI to Consumer GPUs	developers.googleblog.com official	2025-04-18	2026-10-07	hn
Technical Deep-dive into Gemma3 🤖: Next generation models for small and mid range tasks	vizuara.substack.com	2025-04-03	2026-10-07	exa
Gemma3 Function Calling	ai.google.dev official	2025-03-23	2026-10-07	hn
Google calls Gemma 3 the most powerful AI model you can run on one GPU	theverge.com	2025-03-20	2026-10-07	hn
Fine-tune Google's Gemma 3	unsloth.ai	2025-03-19	2026-10-07	hn
Google's Gemma 3 is an open source, multimodal single-GPU AI 128K context window	arstechnica.com	2025-03-13	2026-10-07	hn
Gemma3 – The current strongest model that fits on a single GPU	ollama.com	2025-03-12	2026-10-07	hn
Gemma 3: The most capable model you can run on a single GPU or TPU	blog.google official	2025-03-12	2026-10-07	hn
Gemma 3: Google's new multimodal models	ai.google.dev official	2025-03-12	2026-10-07	hn
Gemma 3 Technical Report [pdf]	storage.googleapis.com	2025-03-12	2026-10-07	hn
Gemma 3 12B by Google: Complete Performance Review & Benchmarks (2026)	airank.dev	2025-03-12	2026-10-07	exa

Papers

8 papers

Papers 8

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).

PIQA: Reasoning about Physical Commonsense in Natural Language cited in card peer-reviewed · measured

Yonatan Bisk, Rowan Zellers, Ronan Le Bras +2 · 2019-11-26 · Proceedings of the AAAI Conference on Artificial Intelligence · PDF · HF paper page (5 upvotes) · evidence
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension cited in card peer-reviewed · measured

Mandar Joshi, Eunsol Choi, Daniel S. Weld +1 · 2017-05-09 · Annual Meeting of the Association for Computational Linguistics (ACL) · PDF · HF paper page (2 upvotes) · evidence
On the Cross-lingual Transferability of Monolingual Representations cited in card peer-reviewed · measured

Mikel Artetxe, Sebastian Ruder, Dani Yogatama · 2019-10-25 · Annual Meeting of the Association for Computational Linguistics (ACL) · PDF · HF paper page · evidence
Gemini: A Family of Highly Capable Multimodal Models cited in card

Gemini Team, Rohan Anil, Sebastian Borgeaud +1348 · 2023-12-19 · PDF · HF paper page (50 upvotes)
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI cited in card

Xiang Yue, Yuansheng Ni, Kai Zhang +19 · 2023-11-27 · PDF · HF paper page (40 upvotes)
GPQA: A Graduate-Level Google-Proof Q&A Benchmark cited in card

David Rein, Betty Li Hou, Asa Cooper Stickland +5 · 2023-11-20 · PDF · HF paper page (38 upvotes)
BLINK: Multimodal Large Language Models Can See but Not Perceive cited in card

Xingyu Fu, Yushi Hu, Bangzheng Li +7 · 2024-04-18 · PDF · HF paper page (27 upvotes)
Evaluating Large Language Models Trained on Code cited in card

Mark Chen, Jerry Tworek, Heewoo Jun +55 · 2021-07-07 · PDF · HF paper page (12 upvotes)

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:32 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:00 UTC · Last verified 2026-10-25

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