Simplismart

Founded 2022

Deploy, fine-tune, and scale open-source machine learning models across multiple cloud providers

You can use Simplismart to fine-tune, deploy, and scale machine learning models across public clouds or your private infrastructure. The service provides custom CUDA kernels to run language, vision, speech, and diffusion models with low latency and high throughput. You can import models from popular cloud repositories or choose from open-source options like Llama and Whisper. It handles automated scaling in under 500 milliseconds, matches compute hardware to your traffic patterns, and monitors ongoing workloads across multiple cloud environments from a single dashboard.

What Simplismart does

Deploy an open-source model endpointImport custom weights from a cloud storage bucketConfigure autoscaling thresholds based on request latencyFine-tune a language model on specialized internal datasetsQuantize models using FP8 or AWQ precisionMonitor inference latency and server metrics across cloud providersSet instances to scale down to zero when idleAuto-scales inference instances in under 500 millisecondsRuns models inside private cloud VPCs or on-premises serversExecutes inference with custom-built CUDA kernels for lower latencySupports over 150 open-source models including Llama, Flux, and WhisperManages multi-cloud GPU deployments from a single control panelScales GPU instances to zero during periods without trafficFine-tunes text, vision, and speech models directly on the infrastructure