DiffusionGemma 26B-A4B Instruct

When to pick this model

• Experimenting with diffusion-based text generation as an alternative to autoregressive decoding
• Vision-language tasks needing Apache 2.0 licensing
• Research or evaluation of non-autoregressive generation quality and latency characteristics
• Multilingual vision-language workloads with a 128k context

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, DiffusionGemma 26B-A4B Instruct needs a 16 GB card at Q4_K_M (15 GB). Stepping up to Q8_0 nearly doubles the footprint to 28 GB, and unquantized FP16 weights take 52 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DiffusionGemma 26B-A4B Instruct needs roughly 34 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 9 tokens/sec on entry-level GPUs, on the order of 14 tokens/sec on a mid-range card, and up to 22 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DiffusionGemma 26B-A4B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run DiffusionGemma 26B-A4B Instruct?

To run DiffusionGemma 26B-A4B Instruct locally at Q4, you need ~15 GB for Q4 weights alone. Hardware option to compare: RTX 5070 Ti 16GB (GIGABYTE Gaming OC). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Strengths

• Apache 2.0, cleared for commercial use
• Diffusion architecture offers a genuine alternative to autoregressive transformers
• Multimodal vision-language capability
• Native 128k context

Limitations

• No official Ollama tag — HuggingFace install only
• Diffusion architecture means less mature runtime and quantization tooling
• Public benchmarks are still limited

Typical workloads

In our catalog grid, DiffusionGemma 26B-A4B Instruct is filed under Vision-Language, Diffusion Generation, Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; multilingual workloads.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: DiffusionGemma · diffusion variant of Gemma 4 · 26B parameters · vision-language · 128k context

Training: Google DiffusionGemma: diffusion (non-autoregressive) variant of the Gemma 4 family, instruction-tuned. Novel architecture for text generation via denoising steps.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

# HuggingFace : google/diffusiongemma-26B-A4B-it

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does DiffusionGemma 26B-A4B Instruct need?

At the recommended Q4_K_M quantization, DiffusionGemma 26B-A4B Instruct needs about 15 GB of VRAM. Q8_0 takes 28 GB, and unquantized FP16 weights take 52 GB.

Can DiffusionGemma 26B-A4B Instruct run without a GPU?

Yes — with roughly 34 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DiffusionGemma 26B-A4B Instruct support?

DiffusionGemma 26B-A4B Instruct supports a 125k-token context window (128,000 tokens).

Can I use DiffusionGemma 26B-A4B Instruct commercially?

Yes. DiffusionGemma 26B-A4B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is DiffusionGemma 26B-A4B Instruct on consumer hardware?

Our compatibility engine estimates on the order of 14 tokens/sec on a mid-range GPU and up to 22 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DiffusionGemma 26B-A4B Instruct should I download first?

Start with Q4_K_M (15 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.