Unified ML Inference Optimization Platform
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Automatically benchmarks, converts, and deploys models across PyTorch, TensorRT, vLLM, and ONNX runtimes for optimal inference performance.
Added May 10, 2026
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The Problem
ML engineers waste enormous time manually porting models between inference frameworks (PyTorch, TensorRT, ONNX, vLLM, SGLang) to find the best latency/cost tradeoff. Each framework has different quirks, conversion errors, and performance characteristics, and teams lack a unified way to compare them on their actual workloads.
Potential Solution
A platform that ingests a trained model and automatically converts it to all major inference runtimes (TensorRT-LLM, vLLM, ONNX Runtime, SGLang, OpenXLA), runs standardized benchmarks across hardware targets, and deploys the winning configuration. Includes regression detection, kernel-level profiling, and one-click deployment to user-managed GPU infrastructure.
Why Now?
The explosion of LLM inference frameworks (vLLM, SGLang, TensorRT-LLM all maturing in 2024-2025) combined with GPU scarcity has made inference optimization a top cost lever for AI companies, yet tooling remains fragmented and engineer-driven.
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