01
Compare a document with its figures.
Supply a selected passage and the chart it refers to. Ask for discrepancies between the two, then return page references with the answer so the user can inspect the original evidence.
Gemma 4 26B A4B
Build document assistants.
Gemma 4 26B A4B is Google’s instruction-tuned mixture-of-experts model for text and images. Deploy it on AI Fabric to answer document questions, compare visual evidence and control how your application prepares each request.
€5 in free credits for every new customer.
Plan your deploymentAI Fabric access is opening gradually. GPU configuration is confirmed during onboarding.
Answer questions across text and images.
Combine retrieved passages with the figures they refer to. Give users an answer they can check against the source document.
02
Review a visual specification.
Combine interface captures with a written acceptance checklist. Have the assistant draft observations, with your application distinguishing a visible defect from an assumption about hidden behaviour.
03
Process multilingual project material.
Build a workflow that normalizes supplied notes into a common project brief. Preserve original quotations and document identifiers so reviewers can resolve ambiguous wording.
Model capabilities and upstream setup: Gemma 4 26B A4B publisher reference. Reviewed .
Deployment preparation
Gemma 4 MoE: GPU memory and quantization.
Active parameters describe the portion used for a token, not the amount of GPU memory needed to hold the checkpoint. Roughly 50 GB of BF16 weights, plus runtime overhead, is beyond the 16 GB and 20 GB cards; plan a tested quantized configuration.
- Verify access to the chosen Google checkpoint and use a runtime that supports this Gemma generation.
- Choose precision and a conservative text/image input budget. Keep the checkpoint, processor and template together.
- Compare answers against reviewed document examples before increasing input length or concurrent requests.
{
"checkpoint": "google/gemma-4-26B-A4B-it",
"job": "compare_document_evidence",
"inputs": [
"question",
"selected_text",
"page_images"
],
"output": [
"observations",
"page_references"
],
"validation": [
"source_present",
"claim_supported"
]
}Example workload specification for sizing and onboarding; choose the quantized checkpoint and runtime before deployment.
GPU memory & performanceWeight-only arithmetic for 25.2B parameters is about 12.6 GB at 4 bits, before quantization metadata and runtime memory. Measure the full worker with images and cache on the selected GPU.
In your applicationInclude page identifiers in every request. Save extracted observations separately from the model’s narrative answer so a reviewer can trace what it used.
AI Fabric availability & costRTX A4500 (20 GB) and RTX 2000 Ada (16 GB) are the GPU options. Access, runtime settings and billing are confirmed during onboarding. No Blade latency or throughput benchmark is published for this configuration.
Build a retrieval workflow for document questions.
Your search service selects a few relevant pages; Gemma then receives those pages and a precise question. Set a policy for missing evidence, and route unsupported answers back to document review instead of extending the prompt indefinitely.
01 / InputRetrieve pages and figures
02 / ProcessGemma answers from evidence
03 / ResultCited answer for review
Gemma 4 26B A4B
deployment FAQ.
Does A4B mean the model needs only 4B worth of memory?
No. The checkpoint includes all experts even though only a subset is active per token. Plan memory from the total weights and runtime allocations.
Does this 26B checkpoint accept audio?
The publisher lists text and image inputs for 26B A4B. Audio capabilities documented for other Gemma 4 variants should not be attributed to this checkpoint.
Should I send every document in one request?
Start with retrieval and a bounded set of pages. The publisher’s maximum context is not a capacity guarantee for a quantized worker on a 16 GB or 20 GB GPU.
How much does running Gemma 4 26B A4B on AI Fabric cost?
Estimate cost per answered document question. Include retrieval, image preprocessing and the text/image input size, then measure the effect of concurrent requests on GPU time.
The GPU billing unit and idle policy are confirmed during onboarding. Managed Models API rates do not apply to this deployment.
Checkpoint: google/gemma-4-26B-A4B-it. Access requirements and licence: Gemma 4 26B A4B publisher reference.