WoolyAI builds AI compute software to run more models on existing GPUs, serve private multi-model agents at lower cost, and bring modern AI software stacks to emerging accelerators.
Increase GPU (NVIDIA & AMD) utilization with VRAM swap, dynamic scheduling, priority control, and model weight deduplication across existing GPU infrastructure.
Private, lower-cost, multi-model serving for enterprise agentic workflows, using lean GPU infrastructure instead of oversized dedicated data-center GPU deployments.
Help AI chip vendors support PyTorch, vLLM, SGLang, and modern model development and execution through a portable runtime, compiler, and target-specific software stack.
Future AI applications will orchestrate planners, workers, verifiers, multimodal models, tool-use models, and fine-tuned domain models across long-running workflows. That requires infrastructure capable of running multiple models efficiently and supporting new hardware targets at low cost.
Run more workloads on existing NVIDIA/AMD GPUs with smarter memory and execution control.
Support business Agentic workflows that use multi-model processing on low-cost GPUs.
Reduce the software-stack burden for ASIC vendors that need modern framework and model support.
Support business agentic workflows that use multi-model processing on low-cost GPUs.
C4 decode for Nemotron 3 Nano Omni 30B NVFP4 with model context switch of 2.07s
C4 decode for Gemma 4 26B A4B with model context switch of 6.38s
C4 decode for DeepSeek V4 Flash with model context switch of 16.56s
On a 2× NVIDIA DGX Spark setup, WoolyAI served a simulated business-agent workflow across DeepSeek V4 Flash, Gemma 4 26B A4B, and Nemotron 3 Nano Omni with no speculative decoding via a single OpenAI-compatible endpoint.
Run more workloads on existing NVIDIA/AMD GPUs with smarter memory and execution control.
Prefill improvement in dual-model runs
Priority-0 model maintained near native single-model SLA
Large-model swap scenario on one GPU runtime
WoolyAI Runtime Software showed that multiple vLLM workloads can run concurrently with better utilization than native execution, while giving deterministic priority to the most important model.