ZFLOW AI offers an auto‑optimization engine that simulates compute, memory, and network behavior on any GPU, CPU, or interconnect configuration to automatically generate optimal deployment strategies for AI workloads. By ingesting model graphs in PyTorch, ONNX, or HuggingFace formats, it produces verified runtime configurations—including parallelism, KV‑cache offloading, and batch sizing—for serving frameworks like vLLM and SGLang, eliminating manual tuning and improving performance and cost efficiency across heterogeneous clusters.
Funding
Funding not disclosed
Founders
Product
Problem
AI infrastructure teams must manually tune parallelization, memory allocation, and runtime parameters across heterogeneous GPU, CPU, and interconnect configurations, a process that is time‑consuming, error‑prone, and does not scale with the growing complexity of modern LLM and multimodal workloads.
Solution
Zflow AI provides an auto‑optimization engine that automatically discovers optimal deployment strategies for AI workloads without requiring trial runs on physical hardware. By ingesting model graphs in PyTorch, ONNX, or HuggingFace formats, the platform simulates compute, memory, and network behavior on any target hardware configuration—including NVIDIA, AMD, and emerging AI chips. The simulation‑driven pipeline evaluates a vast space of parallelism, KV‑cache offloading, and runtime settings, then generates a verified, ready‑to‑deploy runtime configuration (e.g., tensor and pipeline parallelism, memory tiering, batch size) for serving frameworks such as vLLM or SGLang. This eliminates manual tuning, reduces deployment time, and improves throughput, latency, and cost efficiency across single‑node and multi‑node clusters.
Target Audience
Primary customers are ML‑ops engineers, AI infrastructure teams, and cloud service providers who need to deploy large language models and multimodal workloads efficiently across heterogeneous GPU clusters.
Features
- Hardware‑neutral simulation that models compute, memory subsystem, and interconnect performance for any GPU, CPU, memory, and network combination
- Automatic generation of optimal parallelization strategies (tensor, pipeline, expert) and memory placement (HBM vs DDR, KV‑cache offload)
- Integrated model compilation that produces workload‑specific execution plans compatible with vLLM and SGLang serving runtimes
- Cost and energy modeling to predict utilization, memory usage, and deployment expenses before hardware allocation
- Search engine that explores the full configuration space and selects the best strategy based on throughput, latency, and resource constraints
- Delivered as a ready‑to‑run runtime configuration, eliminating the need for separate manual configuration steps
- Supports heterogeneous clusters spanning NVIDIA, AMD, and custom AI accelerators