Open-Weight LLM · Private & Custom AI
diffusiongemma-26B-A4B-it-NVFP4
Multimodal ops engine for document processing, video analysis, and structured reasoning—small active parameter footprint (3.8B) with enterprise quantization for private data handling.
DiffusionGemma 26B A4B IT is a Google DeepMind multimodal model (text, image, video → text) using a sparse MoE architecture: 25.2B total parameters but only 3.8B active per inference step. NVIDIA's NVFP4 quantization brings it down to 4-bit precision, cutting memory and disk footprint sharply. For ops teams, this means running sophisticated document understanding, video frame analysis, and structured reasoning workflows entirely in-house without streaming data to third-party APIs.
Model facts
Private deployment
Run diffusiongemma-26B-A4B-it-NVFP4 in your own environment
Self-hosted on NVIDIA Hopper (H100) or Blackwell (B100) GPUs via vLLM runtime. Architecture advantage: sparse MoE activates only 3.8B of 25.2B weights per forward pass, reducing VRAM demand vs. dense 26B models. NVFP4 quantization further cuts memory by ~75% vs. FP16. Company deploys on private infrastructure (on-prem or VPC-locked cloud), processes documents/videos entirely within their network perimeter, and retains full control over data flow and model weights. No API calls, no vendor logs. Trade-off: requires NVIDIA GPU expertise and DevOps overhead; vLLM setup, CUDA tuning, and multi-GPU orchestration are non-trivial.
Operational AI use cases
Document & Form Automation
Ingest PDFs, scanned contracts, invoices, and tax documents. Multimodal input (text + image) extracts structured data—line items, party names, compliance flags—and routes to backend systems (ERP, RPA, approval workflows). Reasoning mode enables step-by-step parsing of ambiguous layouts. No data leaves the company network.
Video Content Triage & Metadata
Process security footage, training videos, or customer support recordings (up to 60 seconds at 1 fps). Generate summaries, extract action items, detect anomalies, and populate knowledge bases. Runs locally; sensitive footage never exposed to external APIs. Function calling enables triggering alerts or ticket creation on detected events.
Internal Knowledge Agent & Q&A
Build a chatbot that indexes internal docs, SOPs, and prior support tickets. Handles multimodal queries (text + screenshot from helpdesk issues). Structured JSON output integrates with ticketing systems (Jira, ServiceNow) for auto-classification and routing. Reasoning mode supports complex troubleshooting workflows.
Custom AI
As a base for custom AI
Strong foundation for proprietary products combining document intelligence and structured output. Sparse architecture and native function calling support agentic workflows: ingest unstructured input, reason through steps, call APIs or invoke tools, emit JSON. 256K context window suits multi-document analysis. Quantization and Apache 2.0 licensing allow commercial productization without runtime licensing friction. Teams can fine-tune on domain-specific data (contracts, medical records, financial statements) using NVIDIA's ModelOpt ecosystem.
In the operating system
Where it fits
Sits in the *Knowledge & Document Intelligence* and *Agent Reasoning* layers of an ops AI stack. Inputs: unstructured docs, videos, images; outputs: structured text, JSON, function calls. Feeds into workflow automation (RPA, orchestration), internal search (retrieval-augmented generation with multimodal indexing), and agentic decision-making (reasoning mode + tool calling). Complements SQL databases and vector stores; replaces heavyweight cloud APIs for in-house document processing.
Data control & security
Private deployment ensures data—documents, videos, internal conversations—never transits external networks. Company owns the model weights (Apache 2.0 license permits self-hosting). VRAM requirements scale to infrastructure under your control; no surprise third-party access or training-data reuse. Important caveat: quantization and sparse architecture trade some accuracy for speed/efficiency (see benchmarks); model itself is not certified for HIPAA, PCI, or regulated data—compliance depends on your deployment architecture, access controls, and audit practices. Responsibility for model bias, adversarial robustness, and content filtering remains with the deploying org.
Hardware footprint
Estimate for NVFP4 quantized model on inference: ~13–16 GB VRAM (H100 80GB, A100 40GB suitable; A10G 24GB too tight for multi-batch). FP8 full precision would be ~52 GB; FP16 ~104 GB. Throughput on H100: >1,100 tokens/sec at low batch sizes (1–4). Sparse activation (3.8B active) vs. dense alternatives saves 3–4× memory vs. a dense 26B model. Exact footprint depends on batch size, context length, and quantization backend (vLLM GPTQ, ONNX, TensorRT options pending).
Integration
vLLM runtime exposes OpenAI-compatible API (drop-in replacement for many chat apps). Native function calling and structured output (JSON) integrate cleanly with RPA platforms (UiPath, Blue Prism), BPM suites (Camunda), and ticketing systems (Jira REST API, ServiceNow). Vision token budget is configurable (70–1120 tokens/image), allowing tuning for latency vs. detail. Reasoning (thinking) mode and multimodal input require vLLM v0.6.4+ or later; requires --trust-remote-code and --enable-auto-tool-choice flags. Multi-GPU batching supported but max batch size constrained by sparse activation (large batches reduce efficiency gains).
When it's not the right fit
- —Real-time streaming use cases: 256-token parallel block generation adds latency for single-token autoregressive use; optimized for throughput, not <50ms per-token latency.
- —Xsmall footprint (<8GB VRAM): even quantized, requires Hopper-era or newer NVIDIA GPUs; no CPU-only or consumer-GPU fallback.
- —Highly regulated domains without compliance customization: model trained on web data and includes safeguards, but no certifications for HIPAA, PCI-DSS, or SOX; additional controls needed.
- —Frequent fine-tuning cycles: sparse MoE and quantization complicate QLoRA and parameter-efficient tuning; full fine-tuning requires significant VRAM and expertise.
Alternatives to consider
Meta Llama 3.2-Vision 11B / 90B
Dense, simpler to quantize; vision-capable but single-image focused. Llama 3.2-90B rivals DiffusionGemma reasoning but requires 40GB+ VRAM unquantized. Llama 3.2-11B is lighter but less capable on math/coding.
Qwen2-VL (7B / 32B)
Video-native, efficient multimodal, deployed by Alibaba teams. Smaller active parameter footprint in 7B variant; strong on OCR and video understanding. Smaller community/tooling vs. NVIDIA-optimized vLLM ecosystem.
Claude 3.5 Sonnet (API) + LocalStack for embedding-only fallback
If you forego self-hosting: Claude excels at reasoning and multimodal tasks (PDFs, images in messages). No privacy for sensitive data. Hybrid: embed with local Nomic or BGE models, reason in Claude. Adds latency and cost.
FAQ
Can I deploy this entirely on-premises without cloud APIs?
Yes. DiffusionGemma NVFP4 runs fully locally via vLLM on your NVIDIA GPUs. Download the model weights from HuggingFace, spin up vLLM on a private server, and serve on an internal network. All data stays within your infrastructure—no external API calls required. You manage CUDA, vLLM versioning, and failover.
Is commercial use permitted, and do I owe royalties?
Yes, commercial use is permitted under Apache 2.0. No royalties. You can build products and sell them. You must include a copy of the Apache 2.0 license in distribution. Also respect Google's Gemma Terms of Use and Prohibited Use Policy (no illegal activities, abuse, etc.).
How much does quantization hurt accuracy?
NVFP4 shows <1% accuracy drop on most benchmarks (GPQA Diamond: 69.4% → 68.6%, HumanEval: 94.09% → 95.00%). Math (AIME) drops ~1%, language understanding (MMLU) drops ~0.4%. Acceptable for most ops workflows (document triage, summarization, classification); test on your domain data before production.
What's the minimum GPU to run this?
NVFP4 quantized model fits in ~13–16 GB VRAM. H100 (80GB), A100 (40GB), or RTX 6000 Ada (48GB) are safe. A10G (24GB) is marginal for single-batch inference. RTX 4090 (24GB consumer) is not recommended—vLLM's production tuning targets data-center GPUs. CPU inference possible but very slow; not practical for ops.
Build a Private, Multimodal Ops AI with LLM.co
DiffusionGemma NVFP4 is production-ready for self-hosted document intelligence, video analysis, and agentic workflows. LLM.co helps integrate it into your ops stack—quantization tuning, vLLM deployment, agent orchestration, and compliance validation. Let's build your private AI system.