The ML
Operating Ecosystem.
Run any model on bare GPU. No DevOps, no overhead, no waiting — just submit and get results.
Renting a GPU is easy. Running ML on it isn't.
GPU marketplaces and cloud providers solve access. Nobody solves operations — the layer between hardware and a running job.
Days lost before first run
CUDA versions, NCCL configs, driver mismatches. Every new environment means reinstalling and debugging before you can run a single batch.
GPUs idle while jobs queue
No gang scheduling means a 32-GPU job waits hours for 4 free nodes. You pay for idle hardware while your queue backs up.
Hour 47 of 48. Full restart.
A single node crash with no fault tolerance wipes your progress. Meta's Llama 3 training saw one failure every 3 hours on 16k GPUs.
40–70% of GPU budget gone
Idle instances, over-provisioned clusters, inefficient data loading. Datadog reports only 15% of provisioned GPUs are ever core-efficient.
NCCL timeout. Root cause: unknown.
Distributed failures surface as opaque errors. Finding the actual cause — bad NIC, HBM fault, slow network path — requires hardware expertise most ML teams lack.
Works at 8 GPUs. Breaks at 64.
Distributed training introduces failure modes invisible in local testing. Scaling is a re-engineering project, not a config change.
One ecosystem.
Every GPU.
One ecosystem. Every GPU.
Runs across your hardware, our network, or the shared pool. No single point of failure. No vendor lock.
One kernel. Both workloads.
Training and inference share one kernel, one platform, one dashboard. No context switching between tools.
Drivers handled. Science first.
CUDA, NCCL, networking, checkpointing — the ecosystem handles every layer so your team handles the models.
Workflow
Two paths, one platform — from raw data to trained model, or from model to live inference.
Shard Dataset
Split your dataset into .zip shards — one per GPU worker. Upload to your S3 bucket via browser, rclone, or SDK.
Bucket Pull
We host the bucket — your private Garage storage. Workers get a short-lived presigned URL at dispatch and pull shards straight from storage, bypassing the control plane.
Submit Job
Drop your scripts, pick GPU count, hit submit. We provision the cluster and start distributed training.
Parallel Execution
Workers train in parallel. Gradients sync. Checkpoints stream back to your bucket on every epoch.
Get Your Model
Final weights land in jobs/<name>/model_out/ in your bucket. Download, deploy, or keep training.
Three GPU Runtime Pools.
Choose the execution environment that fits your workload — or use all three as you scale. Same API across all three.
SHARED POOL
Multi-tenant GPU pool. Instant provisioning.
- ✓Cold start under 60 seconds
- ✓Auto-scaled across available nodes
- ✓Multi-tenant node isolation
- ✓Fair-queue job kernel
MANAGED GPU CLUSTER
Reserved nodes. Isolated kernel.
- ✓Dedicated, non-shared nodes
- ✓Isolated job kernel
- ✓Priority queue with preemption
- ✓Custom GPU configurations
PRIVATE CLUSTER
Data never egresses. Air-gap mode available.
- ✓Full data sovereignty
- ✓Air-gapped deployment available
- ✓Your hardware, our kernel
- ✓RBAC and audit logs
Every NVIDIA GPU. Zero driver work.
From H100 clusters to workstation RTX cards — the kernel auto-detects, configures CUDA, and manages every device. No driver installs. No environment debugging.
Hopper
Ampere
Ada Lovelace
Workstation
Volta / Turing
Any CUDA GPU
From 4 Days to 12 Hours
DeepLab fine-tuning · 61M parameters · 30GB dataset · identical final model quality
What are you running?
Three runtime environments. One kernel. Pick the one that fits your workload.
Run 50 experiments for the cost of 5.
Public pool, pay-as-you-go. No infrastructure overhead between runs. Your hypothesis loop goes from days to hours.
How researchers use ResonTech →Train and serve from one platform.
Managed cluster, SLA-backed. Stop running two separate stacks for training and inference. One API, one dashboard.
How production teams use ResonTech →Bring your fleet. We bring the kernel.
Your data never moves. Air-gap mode available. Full compliance, audit logs, and RBAC out of the box.
How enterprises use ResonTech →Don't take our word for it. Ask the engineers.
Real complaints from ML engineers and data scientists — posted publicly on Hacker News and Medium. The infrastructure burden isn't hypothetical. It's burning money and sanity every single day.
"By the time you wake up and notice, you've lost 8+ hours of compute. You scramble to diagnose the issue, manually restart from the last checkpoint, and hope it doesn't happen again. For training runs that take days to weeks, this constant babysitting is exhausting and expensive."
— ML Engineer · January 2026
"Teams spend months building custom operators and kernels on top of Kubernetes, essentially recreating a GPU-aware batch system from scratch. Many abandon Kubernetes entirely after burning six figures on wasted engineer time."
— GPU Scheduling: The Hidden Infrastructure Crisis · December 2025
"It's still a major pain to debug those systems, deal with node crashing, tweak the architecture and data-loading pipeline to have high GPU utilization, optimize network bottlenecks."
— Distributed Training Discussion · December 2023
Industry Survey"Most teams waste 40–70% of their GPU budget on idle instances, over-provisioned hardware, and inefficient training."
— GPU Infrastructure for ML · February 2026
What the kernel
saves you.
ZERO INFRASTRUCTURE SETUP
No servers to assemble. No CUDA drivers to install. No environment configs to debug. Your team submits a job and it runs — on hardware that was provisioned, configured, and validated before you even opened a terminal.
NO IDLE GPU BILLS
Clusters spin up when you run, disappear when you're done. Inference endpoints scale to zero between requests.
NO MORE 3AM RESTARTS
Automatic fault recovery means a crashed node doesn't wake anyone up. The job resumes from checkpoint, silently.
ENGINEERS DO ENGINEERING
ML engineers build models — not infrastructure. Reclaim 30–40% of your team's time back from DevOps.
SCALE WITHOUT A PROJECT
Need more compute for training? Add shards — no reprovisioning. Traffic spike on your inference endpoint? ResonTech scales replicas automatically, then scales back down. No engineering work, no ops ticket, no waiting.
NO PAID RERUNS
Checkpoint recovery means a mid-run failure doesn't cost you the whole run. Resume from where it stopped.
Focus on Science,
Not Infrastructure.
Training job or inference endpoint. Public pool or private cluster. One command.