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DiffusionGemma: The Developer Guide
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This developer guide explains how to understand, serve, and customize DiffusionGemma, an experimental model built on the Gemma 4 backbone.
Made with Rinto — analyse your own content free
DiffusionGemma Milestones
DiffusionGemma introduces several milestones improving developer workflows, including faster generation, self-correction, and smaller sizes.
Compute-bound parallel generation
This feature bypasses memory-bandwidth limitations by shifting the bottleneck to compute.
Faster token generation on GPUs
DiffusionGemma delivers up to 4x faster token generation on GPUs.
NVIDIA GeForce RTX 5090 performance
The model achieves up to 700+ tokens per second on NVIDIA GeForce RTX 5090.
NVIDIA H100 performance
The model achieves 1000+ tokens per second on a single NVIDIA H100.
Bidirectional context & self-correction
DiffusionGemma uses bidirectional attention to evaluate the entire text block simultaneously during generation.
Real-time error correction enabled
This simultaneous evaluation enables real-time error correction and parallel context propagation.
Developer-friendly sizes
DiffusionGemma is designed as a Mixture of Experts (MoE) model for efficient deployment.
MoE model size
The model is a 26B Mixture of Experts (MoE) model.
Active parameters during inference
Only 3.8B parameters are activated during inference.
VRAM deployment limit
This allows quantized deployment within 18 GB VRAM limits.
DiffusionGemma Architecture
DiffusionGemma's architecture shifts the primary bottleneck from memory bandwidth to compute, improving performance for LLMs on GPUs.
Traditional LLM bottleneck
For traditional LLMs on GPUs, the primary bottleneck is memory bandwidth due to repeated model weight loading.
Bypassing memory bandwidth
DiffusionGemma bypasses this limitation by shifting the bottleneck from memory bandwidth to compute.
Parallel 256-token canvas generation
The model generates and refines a 256-token canvas in parallel.
GPU tensor core utilization
Providing the GPU with a large parallel workload utilizes tensor cores that would otherwise be idle.
Uniform State Diffusion
DiffusionGemma starts with a canvas of random placeholder tokens and iteratively refines them in parallel.
Token resolution process
Over multiple denoising passes, highly confident tokens help resolve adjacent positions, causing the entire sequence to snap into focus.
Block Autoregressive Diffusion
This mechanism enables variable length generation for sequences longer than 256 tokens.
Processing 256-token blocks
Once a 256-token block is fully denoised, the model processes and commits it to the KV cache.
Next block initialization
The model then transitions to the next block, initializing a fresh 256-token canvas conditioned on previously committed history.
Combines parallel speed and stability
This approach combines parallel block speed with the sequential stability of autoregressive models.
Sudoku Solving Showcase
The Sudoku Solver showcases DiffusionGemma's customization for strict, multivariable constrained problems using parallel denoising.
Traditional AR model struggles
Traditional autoregressive models struggle with Sudoku because they cannot evaluate future placeholders or backtrack.
Customization with Hackable Diffusion
A fine-tuning recipe and results using Hackable Diffusion, a modular JAX research toolbox, are released to demonstrate customization.
Why Sudoku is Interesting
Sudoku is interesting for DiffusionGemma due to its strict horizontal, vertical, and 9x9 grid constraints.
81-character string representation
An 81-character Sudoku string representation marks empty cells with periods.
Bidirectional Context Propagation
DiffusionGemma's denoising step allows every canvas query to attend to all positions in parallel, unlike autoregressive models.
Symmetric information flow
Information flows symmetrically across the board, resolving global dependencies in each step.
Error Correction via Re-Noising
Under Uniform State Diffusion, the model evaluates the entire board simultaneously, allowing continuous self-correction.
Replacing digits with random ones
If confidence drops, the sampler replaces digits with random ones to correct errors.
Efficient Early Stopping
Fine-tuning on Sudoku demonstrates that adapters enhance early stopping.
SFT-tuned model stabilization
The SFT-tuned model stabilizes faster than the base model.
Reduced latency and compute costs
Faster stabilization allows the engine to halt sooner, reducing latency and compute costs.
Sudoku Performance Impact
Fine-tuning DiffusionGemma significantly improves its ability to solve Sudoku puzzles.
Base model success rate
The base DiffusionGemma model has a ~0% success rate for solving Sudoku puzzles.
Fine-tuned success rate
Applying the simple JAX SFT recipe on a Sudoku dataset raises correctness to 80% success.
Decreased inference step count
Fine-tuning also decreases the overall inference step count.
Visual Comparison Base vs SFT
The base model is unable to solve Sudoku after 48 steps, while the fine-tuned (SFT) model solves it after 12 steps.
Block Autoregressive Denoising Steps
DiffusionGemma alternates between incremental prefill and denoising during inference to enable block autoregressive denoising.
Prefill / Incremental Prefill
This step uses causal attention to ingest the prompt context and write to the KV cache.
Initial context prefill
It runs once to prefill the initial context.
Appending finalized canvases
It then runs once per block to append each finalized 256-token canvas to the KV cache before denoising the next canvas.
Denoising
This step uses bidirectional attention to iteratively denoise the canvas.
Query token attention
Query tokens at any canvas position can attend to all other canvas tokens and the KV cache.
Global Context Awareness
The Denoiser's bidirectional attention allows every token on the canvas to attend to every other token, unlike AR models.
Effective for non-sequential problems
This makes the model more effective at solving non-sequential problems like Sudoku, where early digits respect late constraints.
Self-Correction Capability
The model can fix earlier mistakes because it iteratively refines the whole canvas.
Re-noising mechanism
If a token's confidence drops during a pass, the sampler can re-noise and replace it.
AR models lack self-correction
AR models lack this capability as they are 'stuck' with a token once generated, especially during long output sequences.
Efficient Long-Context Scaling
The 'block-autoregressive' approach allows the model to handle long sequences efficiently.
Combines diffusion speed with AR stability
It combines the parallel speed of diffusion for blocks with the proven sequential stability of AR models for long-form text.
Simplified Deployment
Using the same architecture as the Gemma 4 26B A4B model simplifies deployment for developers.
Only denoising step implementation needed
Developers only need to implement a denoising step, making integration into existing serving frameworks easier.
Serving DiffusionGemma
DiffusionGemma is integrated into vLLM to efficiently serve this experimental architecture.
vLLM integration
The integration with vLLM allows the engine to run iterative parallel denoising loops efficiently across batched request streams.
Out-of-the-box deployment
Developers can deploy DiffusionGemma out of the box using vLLM's standard OpenAI-compatible local server.
Deployment Command Example
An example command `vllm serve google/diffusiongemma-26B-A4B-it --max-model-len 262144 ...` shows how to deploy DiffusionGemma.
Getting Started Resources
Developers can access various resources to explore non-autoregressive text generation with DiffusionGemma.
Download Weights
Access the experimental model weights, released under the Apache 2.0 license, directly on Hugging Face.
Integrate & Learn
Review the Visual Guide to DiffusionGemma to understand text-based diffusion model mechanics.
Gemma documentation
Read more about DiffusionGemma in the Gemma documentation.
Inference Frameworks
Run the model efficiently using vLLM, Hugging Face Transformers, SGLang, and MLX.
Adapt & Fine-Tune
Official training recipes using Hackable Diffusion are released for rapid experimentation.
Efficient fine-tuning options
Explore efficient fine-tuning using Unsloth or NVIDIA NeMo.
Deployment Options
Instantly deploy on Google Cloud using Model Garden or via NVIDIA NIM.
Optimized Hardware Stack
The model is optimized natively across hardware from consumer RTX 4090/5090 cards to enterprise Hopper and Blackwell servers.
▸ 7 Expand
APEX
DiffusionGemma Developer Guide
This developer guide explains how to understand, serve, and customize DiffusionGemma, an experimental model built on the Gemma 4 backbone.
Made with Rinto — analyse your own content free
▸ 3 Expand
CONC
DiffusionGemma Milestones
DiffusionGemma introduces several milestones improving developer workflows, including faster generation, self-correction, and smaller sizes.
▸ 3 Expand
SUBC
Compute-bound parallel generation
This feature bypasses memory-bandwidth limitations by shifting the bottleneck to compute.
·
STAT
Faster token generation on GPUs
DiffusionGemma delivers up to 4x faster token generation on GPUs.
·
STAT
NVIDIA GeForce RTX 5090 performance
The model achieves up to 700+ tokens per second on NVIDIA GeForce RTX 5090.
·
STAT
NVIDIA H100 performance
The model achieves 1000+ tokens per second on a single NVIDIA H100.
▸ 1 Expand
SUBC
Bidirectional context & self-correction
DiffusionGemma uses bidirectional attention to evaluate the entire text block simultaneously during generation.
·
DETL
Real-time error correction enabled
This simultaneous evaluation enables real-time error correction and parallel context propagation.
▸ 3 Expand
SUBC
Developer-friendly sizes
DiffusionGemma is designed as a Mixture of Experts (MoE) model for efficient deployment.
·
STAT
MoE model size
The model is a 26B Mixture of Experts (MoE) model.
·
STAT
Active parameters during inference
Only 3.8B parameters are activated during inference.
·
DETL
VRAM deployment limit
This allows quantized deployment within 18 GB VRAM limits.
▸ 6 Expand
CONC
DiffusionGemma Architecture
DiffusionGemma's architecture shifts the primary bottleneck from memory bandwidth to compute, improving performance for LLMs on GPUs.
·
DETL
Traditional LLM bottleneck
For traditional LLMs on GPUs, the primary bottleneck is memory bandwidth due to repeated model weight loading.
·
DETL
Bypassing memory bandwidth
DiffusionGemma bypasses this limitation by shifting the bottleneck from memory bandwidth to compute.
·
DETL
Parallel 256-token canvas generation
The model generates and refines a 256-token canvas in parallel.
·
DETL
GPU tensor core utilization
Providing the GPU with a large parallel workload utilizes tensor cores that would otherwise be idle.
▸ 1 Expand
SUBC
Uniform State Diffusion
DiffusionGemma starts with a canvas of random placeholder tokens and iteratively refines them in parallel.
·
DETL
Token resolution process
Over multiple denoising passes, highly confident tokens help resolve adjacent positions, causing the entire sequence to snap into focus.
▸ 3 Expand
SUBC
Block Autoregressive Diffusion
This mechanism enables variable length generation for sequences longer than 256 tokens.
·
DETL
Processing 256-token blocks
Once a 256-token block is fully denoised, the model processes and commits it to the KV cache.
·
DETL
Next block initialization
The model then transitions to the next block, initializing a fresh 256-token canvas conditioned on previously committed history.
·
INSG
Combines parallel speed and stability
This approach combines parallel block speed with the sequential stability of autoregressive models.
▸ 3 Expand
EXMP
Sudoku Solving Showcase
The Sudoku Solver showcases DiffusionGemma's customization for strict, multivariable constrained problems using parallel denoising.
·
DETL
Traditional AR model struggles
Traditional autoregressive models struggle with Sudoku because they cannot evaluate future placeholders or backtrack.
·
DETL
Customization with Hackable Diffusion
A fine-tuning recipe and results using Hackable Diffusion, a modular JAX research toolbox, are released to demonstrate customization.
▸ 4 Expand
CONC
Why Sudoku is Interesting
Sudoku is interesting for DiffusionGemma due to its strict horizontal, vertical, and 9x9 grid constraints.
·
DETL
81-character string representation
An 81-character Sudoku string representation marks empty cells with periods.
▸ 1 Expand
SUBC
Bidirectional Context Propagation
DiffusionGemma's denoising step allows every canvas query to attend to all positions in parallel, unlike autoregressive models.
·
DETL
Symmetric information flow
Information flows symmetrically across the board, resolving global dependencies in each step.
▸ 1 Expand
SUBC
Error Correction via Re-Noising
Under Uniform State Diffusion, the model evaluates the entire board simultaneously, allowing continuous self-correction.
·
DETL
Replacing digits with random ones
If confidence drops, the sampler replaces digits with random ones to correct errors.
▸ 2 Expand
SUBC
Efficient Early Stopping
Fine-tuning on Sudoku demonstrates that adapters enhance early stopping.
·
DETL
SFT-tuned model stabilization
The SFT-tuned model stabilizes faster than the base model.
·
INSG
Reduced latency and compute costs
Faster stabilization allows the engine to halt sooner, reducing latency and compute costs.
▸ 4 Expand
CONC
Sudoku Performance Impact
Fine-tuning DiffusionGemma significantly improves its ability to solve Sudoku puzzles.
·
STAT
Base model success rate
The base DiffusionGemma model has a ~0% success rate for solving Sudoku puzzles.
·
STAT
Fine-tuned success rate
Applying the simple JAX SFT recipe on a Sudoku dataset raises correctness to 80% success.
·
DETL
Decreased inference step count
Fine-tuning also decreases the overall inference step count.
·
EXMP
Visual Comparison Base vs SFT
The base model is unable to solve Sudoku after 48 steps, while the fine-tuned (SFT) model solves it after 12 steps.
▸ 2 Expand
CONC
Block Autoregressive Denoising Steps
DiffusionGemma alternates between incremental prefill and denoising during inference to enable block autoregressive denoising.
▸ 2 Expand
SUBC
Prefill / Incremental Prefill
This step uses causal attention to ingest the prompt context and write to the KV cache.
·
DETL
Initial context prefill
It runs once to prefill the initial context.
·
DETL
Appending finalized canvases
It then runs once per block to append each finalized 256-token canvas to the KV cache before denoising the next canvas.
▸ 5 Expand
SUBC
Denoising
This step uses bidirectional attention to iteratively denoise the canvas.
·
DETL
Query token attention
Query tokens at any canvas position can attend to all other canvas tokens and the KV cache.
▸ 1 Expand
INSG
Global Context Awareness
The Denoiser's bidirectional attention allows every token on the canvas to attend to every other token, unlike AR models.
·
JUST
Effective for non-sequential problems
This makes the model more effective at solving non-sequential problems like Sudoku, where early digits respect late constraints.
▸ 2 Expand
INSG
Self-Correction Capability
The model can fix earlier mistakes because it iteratively refines the whole canvas.
·
DETL
Re-noising mechanism
If a token's confidence drops during a pass, the sampler can re-noise and replace it.
·
JUST
AR models lack self-correction
AR models lack this capability as they are 'stuck' with a token once generated, especially during long output sequences.
▸ 1 Expand
INSG
Efficient Long-Context Scaling
The 'block-autoregressive' approach allows the model to handle long sequences efficiently.
·
JUST
Combines diffusion speed with AR stability
It combines the parallel speed of diffusion for blocks with the proven sequential stability of AR models for long-form text.
▸ 1 Expand
INSG
Simplified Deployment
Using the same architecture as the Gemma 4 26B A4B model simplifies deployment for developers.
·
JUST
Only denoising step implementation needed
Developers only need to implement a denoising step, making integration into existing serving frameworks easier.
▸ 3 Expand
CONC
Serving DiffusionGemma
DiffusionGemma is integrated into vLLM to efficiently serve this experimental architecture.
·
DETL
vLLM integration
The integration with vLLM allows the engine to run iterative parallel denoising loops efficiently across batched request streams.
·
DETL
Out-of-the-box deployment
Developers can deploy DiffusionGemma out of the box using vLLM's standard OpenAI-compatible local server.
·
DETL
Deployment Command Example
An example command `vllm serve google/diffusiongemma-26B-A4B-it --max-model-len 262144 ...` shows how to deploy DiffusionGemma.
▸ 8 Expand
CONC
Getting Started Resources
Developers can access various resources to explore non-autoregressive text generation with DiffusionGemma.
·
DETL
Download Weights
Access the experimental model weights, released under the Apache 2.0 license, directly on Hugging Face.
·
DETL
Integrate & Learn
Review the Visual Guide to DiffusionGemma to understand text-based diffusion model mechanics.
·
DETL
Gemma documentation
Read more about DiffusionGemma in the Gemma documentation.
·
DETL
Inference Frameworks
Run the model efficiently using vLLM, Hugging Face Transformers, SGLang, and MLX.
·
DETL
Adapt & Fine-Tune
Official training recipes using Hackable Diffusion are released for rapid experimentation.
·
DETL
Efficient fine-tuning options
Explore efficient fine-tuning using Unsloth or NVIDIA NeMo.
·
DETL
Deployment Options
Instantly deploy on Google Cloud using Model Garden or via NVIDIA NIM.
·
DETL
Optimized Hardware Stack
The model is optimized natively across hardware from consumer RTX 4090/5090 cards to enterprise Hopper and Blackwell servers.
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