Enterprise Business Agentic AI workflows are not going to run on a single model and they will not continue to send their data and workflow intelligence to a third party API end point.
The next generation of business AI applications will use different models for different steps: planner, research, verifier and domain specific fine tuned models. They will also run in a private, single tenant environment.
That requires a new kind of inference server and serving setup. It’s not a single-model endpoint. It’s not a static deployment where every model needs its own expensive GPU rack. It’s an inference server built for heterogeneous, multi-model agentic workflows which runs efficiently on low cost hardware.
That is what we are building with the WoolyAI Inference Server for DGX Spark cluster.
In our latest validation, we simulated a three-model agentic workflow running on a 2× NVIDIA DGX Spark setup. The workload used generated concurrent traffic against three different models with no speculative decoding:
- DeepSeek V4 Flash 284B
- Gemma 4 26B A4B
- Nemotron 3 Nano Omni 30B A3B Reasoning
The goal was to show how a real agentic application could activate and serve different models at different stages of a workflow, while running on much lower-cost hardware than traditional data-center GPU deployments.
The results are very exciting.
The run completed 12 out of 12 requests successfully, with zero client or server errors. The first three-model cycle completed in 215 seconds, while weighted decode throughput remained stable at 69 tokens/sec. WoolyAI inference server switched and served different models very efficiently with Gemma activation at 6 seconds, Nemotron activation at 2 seconds and DeepSeek V4 Flash activation at 16 seconds(all of these can be further improved).
These numbers matter because there is a general concern about the ability to produce enough tokens fast on low cost GPU hardware. We are able to demonstrate that with an inference server built for specific hardware and purpose – multi-model inference on Nvidia DGX Spark, such concerns can be addressed..
Today, teams often have to choose between two bad options for multi model agentic apps:
- Use shared token APIs and give up control, privacy, and predictable capacity.
- Rent expensive dedicated data-center GPU systems that are often oversized for the actual workload.
WoolyAI solution opens a third path.
Run private, multi-model agentic inference on low-cost GPU infrastructure like NVIDIA DGX Spark clusters.
The report demonstrates that heterogeneous model workflows can be set up, switched, and served through the WoolyAI Inference Server while preserving strict single-model residency and keeping execution stable across the two-node Nvidia DGX setup. This setup can be scaled almost linearly to meet higher demands.
This is the direction inference needs to go. Business AI agents will not be one-model systems. They will be workflows made of multiple models, each selected for the step where it performs best. The winning inference stack will be the one that can serve those models efficiently, switch between them intelligently, and make private agentic applications affordable.
That is the future we are building:
Private, lower-cost, multi-model inference for complex business agentic workflows.
