Models › diffusiongemma family

diffusiongemma-26B-A4B-it

google Apache 2.0 out 5 months ago

25.8B parameters, 4B active, images and text in, text out, 256K tokens of context.

Verified 2026-10-26 07:27 UTC Rank #160 in LLMs · score 91.8 method v1 Sources 3 Downloads 30d 633k · #145 of 619 tracked

Can I run it?16GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-26
Do people use it?633kdownloads, 30 days#145 of 619 we track · top 23%+10 likes, downloads −5% this week1,312 likesHugging Face2026-10-26

Worth reading

Picked by rule from 17 tracked pieces: the lab's own post, the most shared, a field report, the latest.

17 best of 17 tracked

The 3 picks above, plus these 14. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleDiffusionGemma: 4x Faster Text Generationblog.google · 2026-06-10 · 327 HN pts · official
  2. ArticleDiffusionGemmadeepmind.google · 2026-06-11 · 3 HN pts · official
  3. CodevLLM: Jev-like mode for the DiffusionGemma modelgithub.com · 2026-09-16 · 1 HN pts
  4. ArticleDiffusionGemma model card  |  Google AI for Developersai.google.dev · undated · official
  5. PaperNeither Parallel nor Sequential: How DiffusionGemma Commits Tokensarxiv.org · 2026-06-17 · 1 HN pts
  6. ArticleDiffusionGemma: Discrete diffusion in a large language modelidlemachines.co.uk · 2026-06-11 · 3 HN pts
  7. ArticleRun Decision Models on vLLM and Red Hat AI Using DiffusionGemmadevelopers.redhat.com · 2026-09-29 · 1 HN pts
  8. ArticleGoogle's DiffusionGemma uses diffusion tech to speed text generationtheregister.com · 2026-06-12
  9. ArticleNew Google DiffusionGemma Release Model – The Technical Deep Dive - Ai Technology Newsaitechnology.news · 2026-06-12
  10. ArticleDiffusionGemma 26B-A4B: Can Diffusion Beat Autoregression? — Genαigenalphai.com · 2026-06-12
  11. ArticleDiffusionGemma 26B | Awesome Agentsawesomeagents.ai · 2026-06-10
  12. Articlediffusiongemma-26b-a4b-it Model by Google | NVIDIA NIMbuild.nvidia.com · undated
  13. Articlegoogle / diffusiongemma-26b-a4b-itdocs.api.nvidia.com · undated
  14. ArticleDiffusionGemma 26B: 1,100 tok/s Open Diffusion LLM | HokAIhokai.io · undated

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: 37 quantized builds and 33 Spaces.

  • As a service0no API provider listed
  • On own hardware37quantized builds
  • Built on4637 fine-tunes · 8 adapters · 1 merges
  • In apps33Hugging Face Spaces

Top Spaces running it: huggingface-projects/diffusiongemma-codegen ♥ 71 · davanstrien/diffusiongemma-ocr-correction ♥ 23 · victor/DiffusionGemma-free-endpoint ♥ 12 · huggingface-projects/diffusiongemma-3d-gen ♥ 8

Will it fit?

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

Fits a 16 GB card at INT4

About 16 GB at INT4, 31 GB at FP8, 62 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 16 GB of weights (estimate; KV cache and activations extra)INT416 GBFP8: about 31 GB of weights (estimate; KV cache and activations extra)FP831 GBBF16: about 62 GB of weights (estimate; KV cache and activations extra)BF1662 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 16 GB of weights (estimate; KV cache and activations extra)INT416 GBFP8: about 31 GB of weights (estimate; KV cache and activations extra)FP831 GBBF16: about 62 GB of weights (estimate; KV cache and activations extra)BF1662 GB

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

The story so far

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

Coverage peaked at launch and is still arriving

Teal is the weights (2026-06-09), amber triangles are 9 dated articles (each links to it), grey ticks are repo commits.

What people built on it

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

83 derivatives on the Hub, led by 37 quantized

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

37 quantized (Hugging Face model tree)37 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)8 adapters (Hugging Face model tree)37 quantized37 fine-tunes1 merge8 adapters
37 quantized (Hugging Face model tree)37 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)8 adapters (Hugging Face model tree)37 quantized37 fine-tunes1 merge8 adapters

All variants →

Reference

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

Benchmarks

15 rows

Benchmarks 15 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (15)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.
BenchmarkValueSetting / notesReported byDate
author-reported AIME 2026 · Reason through hard problems69.1% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported BBEH · Reason through hard problems47.6% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported Codeforces · Write code with agents1429 eloDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported GPQA Diamond · Reason through hard problems73.2% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported HLE with search11.9% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems11.0% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported LiveCodeBench · Write code with agents69.1% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MMLU-Pro · Reason through hard problems77.6% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MMMLU · Work across languages81.5% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MMMU-Pro · Understand images and video54.3% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MRCR · Read long documents32.0% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MathVision · Understand images and video70.5% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported MedXPertQA MM49.0% percentDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported OmniDocBench · Extract text from documents0.32 otherDiffusionGemma 26B A4Bmodel author2026-10-07
author-reported tau-bench · Call tools and APIs56.2% percentDiffusionGemma 26B A4Bmodel author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

This Mixture-of-Experts architecture uses discrete diffusion to generate text from text, image, and video inputs.[1][2]

  1. is mixture of experts
  2. “generates tokens using discrete diffusion”

Generated 2026-10-07 15:02 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

8 use cases

Facts

22 fields

Listed in LLMs · MoE

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesDiffusionGemmaForBlockDiffusion
Model typediffusion_gemma
Dtypebfloat16
Params by dtypeBF16: 25,823,781,228
Size bandL
MoEyes
Experts128
Context length262,144 (config.json max_position_embeddings)
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-26

Servability

vLLM · SGLang

Servability

vLLM: supported · DiffusionGemmaForBlockDiffusion · 4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60SGLang: not in registry · 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 diffusiongemma-26B-A4B-it: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)62 GB · Single high-end GPU (<80 GB)
FP8 (1 byte/param)31 GB · Single high-end GPU (<80 GB)
INT4 (0.5 bytes/param)16 GB · Single GPU (<24 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original model Family: google/diffusiongemma 83 derivatives on the Hub

quantized: 37finetune: 37adapter: 8merge: 1

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
nvidia/diffusiongemma-26B-A4B-it-NVFP4 quantized NVFP4 14.4B L apache-2.0 nvidia 148 3.6M
RedHatAI/diffusiongemma-26B-A4B-it-NVFP4 quantized NVFP4 25.8B L apache-2.0 RedHatAI 20 1M
unsloth/diffusiongemma-26B-A4B-it-GGUF quantized GGUF — — apache-2.0 unsloth 411 623.8k
RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic quantized -FP8 25.8B L apache-2.0 RedHatAI 12 415.5k
cyankiwi/diffusiongemma-26B-A4B-it-AWQ-INT4 quantized AWQ 25.8B L apache-2.0 cyankiwi 2 387.2k
unsloth/diffusiongemma-26B-A4B-it finetune — 25.8B L apache-2.0 unsloth 7 84.2k
mlx-community/diffusiongemma-26B-A4B-it-4bit quantized -4bit 25.8B L apache-2.0 mlx-community 36 36.4k
DevQuasar/google.diffusiongemma-26B-A4B-it-GGUF quantized GGUF — — — DevQuasar 9 14.2k
mlx-community/diffusiongemma-26B-A4B-it-8bit quantized -8bit 25.8B L apache-2.0 mlx-community 14 11.6k
thesysdev/OUI-1 finetune — 25.8B L apache-2.0 thesysdev 149 4k
mlx-community/diffusiongemma-26B-A4B-it-OptiQ-4bit quantized -4bit 25.3B L apache-2.0 mlx-community 20 9.6k
Intel/diffusiongemma-26B-A4B-it-int4-AutoRound quantized int4 9.07B L — Intel 3 9.6k
aidendle94/diffusiongemma-26B-A4B-it-INT8-dynamic quantized INT8 25.8B L gemma aidendle94 4 7k
pixelkaiser/diffusiongemma-26B-A4B-it-AWQ-MLP-W4A16-G64-S32-L1024 quantized AWQ 28.6B L apache-2.0 pixelkaiser 1 6.9k
mlx-community/diffusiongemma-26B-A4B-it-mxfp4 quantized mxfp4 25.8B L apache-2.0 mlx-community 6 5.8k
FreedomAISVR/DiffusionGemma-26B-A4B-it-NVFP4-GGUF quantized NVFP4 — — apache-2.0 FreedomAISVR 7 5.5k
edwixx/diffusiongemma-26B-A4B-it-HERETIC-Uncensored finetune — 25.8B L apache-2.0 edwixx 31 4.1k
corsairnui/diffusiongemma-26b-a4b-it-strix-halo-fp16 quantized -fp16 — — apache-2.0 corsairnui 10 4.5k
mlx-community/diffusiongemma-26B-A4B-it-nvfp4 quantized nvfp4 25.8B L apache-2.0 mlx-community 6 4.5k
mlx-community/diffusiongemma-26B-A4B-it-6bit quantized — 25.8B L apache-2.0 mlx-community 4 4k
mlx-community/diffusiongemma-26B-A4B-it-bf16 finetune -bf16 25.8B L apache-2.0 mlx-community 2 3.1k
mlx-community/diffusiongemma-26B-A4B-it-mxfp8 quantized — 25.8B L apache-2.0 mlx-community 3 2.7k
FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF quantized MXFP4 — — apache-2.0 FreedomAISVR 1 2.4k
mlx-community/diffusiongemma-26B-A4B-it-5bit quantized — 25.8B L apache-2.0 mlx-community 2 2.3k
AlexAtomic/diffusiongemma-26B-A4B-it-GGUF quantized GGUF — — gemma AlexAtomic 1 2.2k
monkiey/StarSupernova quantized — 25.8B L apache-2.0 monkiey 0 2.2k
kingjones777/DiffusionGemma-26B-A4B-it-ROCmFP4-GGUF quantized GGUF — — gemma kingjones777 1 2k
ekryski/diffusiongemma-26B-A4B-it-4bit quantized -4bit 25.8B L apache-2.0 ekryski 0 2.1k
manjunathshiva/diffusiongemma-26B-A4B-it-mini-g32 finetune — 3.12B M apache-2.0 manjunathshiva 4 1.8k
GoedelMachines/diffusiongemma-26B-A4B-w4a16 finetune -w4a16 2.99B M gemma GoedelMachines 1 1.7k
DuoNeural/diffusiongemma-26B-A4B-it-abliterated finetune — 51.1B XL gemma DuoNeural 16 868
sdlangshaw/diffusiongemma-26B-A4B-it-GGUF quantized GGUF — — apache-2.0 sdlangshaw 0 1.6k
manjunathshiva/diffusiongemma-26B-A4B-it-tq3-g32 finetune — 5.03B M apache-2.0 manjunathshiva 0 1.1k
yifanyu/DiffusionGemma-26B-A4B-it-ORL finetune — 26.6B L apache-2.0 yifanyu 0 904
OsaurusAI/diffusiongemma-26B-A4B-it-MXFP8 finetune — 25.8B L gemma OsaurusAI 0 772
Hagwell/diffusiongemma-26B-A4B-it-GGUF quantized GGUF — — apache-2.0 Hagwell 1 688
OsaurusAI/diffusiongemma-26B-A4B-it-MXFP4 finetune MXFP4 25.8B L gemma OsaurusAI 0 733
interfaze-ai/diffusion-gemma-asr-small finetune — — — apache-2.0 interfaze-ai 13 0
microlandltd/Geni-S1-Ops-26B-A4B-NVFP4 quantized NVFP4 14.4B L apache-2.0 microlandltd 8 204
Infatoshi/diffusiongemma-26B-A4B-iq2xxs-4070 quantized GGUF — — apache-2.0 Infatoshi 1 495

Repository changelog

4 commits

Repository changelog 4 commits

DateCommitSHA
2026-07-15 fix: chat template — null handling, reasoning preservation, turn-tag balance, input validation (#23) f7f5b7f
2026-07-06 Add files for diffusers loading (#20) 52de6b9
2026-07-02 Update non-thinking chat template (#15) 156b5c8
2026-06-10 Preparing for release! 0f28bc4

Articles and coverage

17 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

blog.google2026-06-10officialauthorityHN front pageabout the diffusiongemma familyHN 327 pts · 88 comments920 words
Google releases DiffusionGemma, an experimental 26B Mixture of Experts model under an Apache 2.0 license that generates up to 4x faster text by processing 256 tokens in parallel. While it activates only 3.8B parameters to fit within 18GB VRAM limits, the model trades off overall output quality compared to standard Gemma 4 for speed-critical local workflows. This approach shifts the decode bottleneck from memory-bandwidth to compute, enabling 1000+ tokens per second on a single NVIDIA H100.written, reviewed, cited
  • The model iteratively refines its own output to evaluate the entire text block at once and fix mistakes in real-time.
  • DiffusionGemma's parallel decoding offers diminishing returns in high-QPS cloud serving and can result in higher serving costs.
deepmind.google2026-06-11officialauthorityabout the diffusiongemma familyHN 3 pts · 1 comments196 words
DiffusionGemma generates up to 4x faster token output, achieving over 1,000 tokens per second on a single NVIDIA H100 GPU. The 26B total Mixture of Experts model activates only 3.8B parameters and generates 256 tokens in parallel with each forward pass. Native support for NVIDIA's new NVFP4 format on Blackwell GPUs dramatically accelerates compute throughput with near-lossless accuracy.written, reviewed, cited
ai.google.devofficialauthorityabout the diffusiongemma family2,518 words
Google DeepMind released DiffusionGemma, an open-weights multimodal model based on the 26B A4B Gemma 4 architecture that generates text by iteratively denoising blocks of tokens in parallel. Using an H100 accelerator with FP8 precision, the model achieves speeds exceeding 1100 tokens per second by generating 15-20 tokens per forward pass. Best practices recommend a maximum of 48 denoising steps with an entropy bound of 0.1 to optimize performance.written, reviewed, cited
  • DiffusionGemma supports a maximum video processing duration of 60 seconds at one frame per second.
  • The model's pre-training dataset includes content from over 140 languages with a cutoff date of January 2025.
  • For OCR or document parsing tasks, users should configure the visual token budget to 560 or 1120.

Analysis6

medium.com2026-09-28about the diffusiongemma familyHN 1 pts · 2 comments
arxiv.org2026-06-17authorityabout the diffusiongemma familyHN 1 pts · 0 comments425 words
Instrumentation of DiffusionGemma 26B across a 686-prompt, six-regime probe suite reveals decoding is neither parallel nor block-autoregressive but follows a partial left-to-right commit bias whose strength depends on analysis granularity. The model commits in large simultaneous batches with much within-batch order genuinely undefined, finishing in a short late burst well inside the step budget. Honest measurement requires handling trailing-EOS padding, within-regime confounding, commit non-monotonicity, and block-size sensitivity to avoid manufacturing false decoding-order results.written, reviewed, cited
idlemachines.co.uk2026-06-11about the diffusiongemma familyHN 3 pts · 1 comments1,205 words
developers.redhat.com2026-09-29about the diffusiongemma familyHN 1 pts · 0 comments1,497 words
aifoss.dev2026-06-261,941 words
awesomeagents.ai2026-06-191,811 words

Community1

github.com2026-09-16authorityabout the diffusiongemma familyHN 1 pts · 0 comments2,087 words
The PR introduces a structured generation mode for the DiffusionGemma model that shaves off approximately 20-40% off decision time with no impact to decision quality. System One bodies naming model dgemma or carrying an images array are forwarded to the image-capable DiffusionGemma service which speaks the same contract.generated, cited
All coverage tracked for this model (17) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
DiffusionGemma model card  |  Google AI for Developersai.google.dev official—2026-10-07exa
diffusiongemma-26b-a4b-it Model by Google | NVIDIA NIMbuild.nvidia.com—2026-10-07exa
google / diffusiongemma-26b-a4b-itdocs.api.nvidia.com—2026-10-07exa
DiffusionGemma 26B: 1,100 tok/s Open Diffusion LLM | HokAIhokai.io—2026-10-07exa
Run Decision Models on vLLM and Red Hat AI Using DiffusionGemmadevelopers.redhat.com2026-09-292026-10-07hn
How to build a Jev-style classifier with DiffusionGemma and vLLMmedium.com2026-09-282026-10-07hn
vLLM: Jev-like mode for the DiffusionGemma modelgithub.com2026-09-162026-10-07hn
DiffusionGemma 26B Review 2026: 4x Faster, At a Costaifoss.dev2026-06-262026-10-07exa
DiffusionGemma 26B Review: 4x Faster, Real Tradeoffs | Awesome Agentsawesomeagents.ai2026-06-192026-10-07exa
Neither Parallel nor Sequential: How DiffusionGemma Commits Tokensarxiv.org2026-06-172026-10-07hn
Google's DiffusionGemma uses diffusion tech to speed text generationtheregister.com2026-06-122026-10-07hn
New Google DiffusionGemma Release Model – The Technical Deep Dive - Ai Technology Newsaitechnology.news2026-06-122026-10-07exa
DiffusionGemma 26B-A4B: Can Diffusion Beat Autoregression? — Genαigenalphai.com2026-06-122026-10-07exa
DiffusionGemma: Discrete diffusion in a large language modelidlemachines.co.uk2026-06-112026-10-07hn
DiffusionGemmadeepmind.google official2026-06-112026-10-07hn
DiffusionGemma: 4x Faster Text Generationblog.google official2026-06-102026-10-07hn
DiffusionGemma 26B | Awesome Agentsawesomeagents.ai2026-06-102026-10-07exa

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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 15:02 UTC · Last verified 2026-10-26 07:27 UTC

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