What is ResonTech?
ResonTech is an end-to-end ML ecosystem — a SaaS platform for running training and inference at scale. Three GPU pool types. One platform. Zero infrastructure overhead.
You write model code. Submit a job. Get your model back. Every layer in between — provisioning, distributed execution, fault recovery, artifact delivery — is handled by the platform kernel.
| Layer | What ResonTech handles |
|---|---|
| Compute provisioning | Selects optimal GPU nodes by type, locality, and current load |
| Distributed execution | Splits jobs across nodes, configures NCCL, syncs gradients |
| Data delivery | Workers pull training data and scripts from your S3 bucket at job start |
| Fault recovery | Detects failures in seconds, reschedules, resumes from checkpoint |
| Artifact return | Writes model weights and logs back to your workspace folder |
| Inference serving | Deploys endpoints with autoscaling, health checks, load balancing |
Mission
AI teams waste enormous time managing infrastructure instead of doing science — configuring Kubernetes clusters, debugging NCCL errors, recovering from spot instance preemptions, reconciling GPU billing. Hours that should go to model architecture and iteration.
The mission is to eliminate that entirely.Submit a job. Get your model back. That's it.
Training — How It Works
Training runs through four kernel-managed steps. Your code runs unchanged inside this pipeline.
| Step | What happens |
|---|---|
| 01 · UPLOAD | Upload your training data and scripts to your S3 bucket via the Files page or rclone. Workers pull from your bucket at job start. |
| 02 · WRITE | Write your training scripts. PyTorch, TensorFlow, HuggingFace Trainer — any framework runs as-is. No SDK wrapping. |
| 03 · EXEC | Submit via the platform or API. The kernel provisions nodes, configures distributed execution, and starts your job. |
| 04 · RETURN | Artifacts and logs are written back to your workspace folder. If a node fails, the job reschedules and resumes from the last checkpoint automatically. |
Inference — How It Works
Inference endpoints go live in four steps. Cold start under 10 seconds.
| Step | What happens |
|---|---|
| 01 · LOAD | Push a checkpoint from your workspace or point at a HuggingFace repo. The model is fetched and warmed on the cluster. |
| 02 · BIND | Set GPU type, concurrency limit, and autoscale rules in one config. |
| 03 · LISTEN | Your endpoint is live. REST API, OpenAI-compatible for LLMs. Cold start under 10 seconds. |
| 04 · SCALE | Replicas spin up on demand and scale to zero when idle. You pay only for active inference time. |
Three Cluster Types
Every workload has a matching pool. Choose based on your isolation requirements and commitment level.
| Pool | Model | Best for |
|---|---|---|
| Public Pool | Shared, multi-tenant, ephemeral | Experiments, prototyping, one-off training runs |
| Managed Cluster | Dedicated reserved nodes, persistent state | Production pipelines, continuous retraining, inference serving |
| Private Cluster | Your hardware, orchestrated by ResonTech | Regulated data, data residency requirements, existing GPU fleets |
See Infrastructure for a full breakdown of each pool type — architecture, data flow, and use cases.
Framework Compatibility
ResonTech runs any standard ML workload without modification. No SDK wrapping. No lock-in.
- Training frameworks: PyTorch, TensorFlow, JAX, HuggingFace Trainer, Fastai
- Distributed strategies: DDP, FSDP, DeepSpeed ZeRO, model parallelism
- Model hubs: HuggingFace Hub, local checkpoints, workspace paths
- Observability: Weights & Biases, TensorBoard, MLflow — connect your existing tooling
- Inference APIs: REST, OpenAI-compatible endpoints for LLM serving
- Runtime: CUDA 11.8+, NVIDIA GPUs from GTX 1080 Ti to H100 SXM5
train.py. The kernel handles distributed setup — NCCL configuration, rank assignment, gradient synchronization. Your script sees a standard training environment.