Inference-engine deployment and optimization service for companies running their own AI model stacks
Help AI labs and companies deploy, operate, benchmark, debug, and optimize inference engines that serve language, multimodal, agent, and document-processing workloads. The value comes from improving latency, throughput, correctness, GPU utilization, and queue behavior rather than simply exposing a model API.
From AI Engineer — What Is an Inference Engine, Anyway? — Charles Frye, Modal at 04:16
Problem: A model deployment can become slow or expensive because of queueing, scheduler bottlenecks, GPU underutilization, KV-cache pressure, tokenizer problems, host overhead, correctness regressions, or inconsistent performance across replicas.
For: AI labs and companies that want to own or rethink their inference infrastructure, including teams deploying chatbots, background agents, and document processors.
Products from this video
Hugging Face Tokenizers Hugging Face Transformers Mini-SGLang Nsight Systems PyTorch PyTorch Profiler SGLang vLLM
Examples
- A museum placard application used a vision-language model to describe what a camera saw as an art piece; under increased traffic, first-token latency and inter-token latency rose because requests queued, and adding replicas reduced congestion.