What is ResonTech?
ResonTech is the ML & AI Operating System — a unified platform that abstracts away every layer of GPU infrastructure complexity and delivers a single, coherent execution environment for both model training and inference serving.
Where traditional cloud providers rent you raw virtual machines, and GPU marketplaces hand you a machine and an SSH key, ResonTech delivers a kernel — a full orchestration layer that sits between your code and the underlying hardware, handling everything that has nothing to do with your model: driver installation, job scheduling, distributed coordination, fault recovery, autoscaling, data management, and cost optimization.
The operating system metaphor is deliberate. Just as Linux abstracts hardware from applications, ResonTech abstracts GPU infrastructure from ML workloads. Your training scripts and inference code run on the ResonTech kernel mostly without modification. The kernel decides where, how, and on what hardware they execute.
| The kernel manages | You manage |
|---|---|
| Cluster provisioning and multi-node topology optimization | Your datasets |
| Distributed training coordination (DDP, FSDP, DeepSpeed, Federated Learning) | Your training scripts and model code |
| Real-time worker health monitoring and automatic fault recovery | Your configurations and hyperparameters |
| Checkpoint management and resume-from-failure logic | |
| Inference endpoint lifecycle, autoscaling, and load balancing | |
| S3 bucket (Garage) with presigned-URL shard distribution across workers | |
| Usage-based cost metering and idle resource reclamation |
The Problem We Solve
The GPU compute crisis in ML is not a hardware problem. It is an orchestration problem. Raw GPU capacity exists everywhere — from hyperscalers, from regional data centers, from idle gaming rigs, from on-premise enterprise clusters. But unlocking that compute for real ML workloads requires solving six deeply entangled infrastructure problems simultaneously:
| Problem | What happens without ResonTech |
|---|---|
| No Unified Kernel | Teams manage multiple clusters manually. Jobs compete for resources with no central coordination or visibility. |
| No Filesystem Abstraction | Data transfer overhead kills velocity. Moving datasets between environments is manual, slow, and error-prone. |
| No Process Isolation | Training jobs compete for resources on shared hardware. One runaway job starves everything else. |
| No Driver Layer | CUDA configurations break between environments. NCCL configuration for multi-node is an art form few master. |
| No Autoscaler | Idle GPUs cost money while jobs queue. Over-provisioning is the only safe bet — and it is expensive. |
| No Fault Tolerance | OOM crash = full restart, no checkpointing. A node fails at hour 47 of a 48-hour run. You start over. |
Most teams waste 40–70% of their GPU budget on idle instances, over-provisioned hardware, and inefficient training. ResonTech eliminates all six problems from the same platform.
How ResonTech Compares
| Capability | Cloud VM (AWS/GCP) | Lambda Labs | ResonTech |
|---|---|---|---|
| Raw GPU access | Yes | Yes | Yes |
| Driver management | You | You | Kernel |
| Job scheduling | You | You | Kernel |
| Multi-node distribution | You (complex) | You (complex) | Kernel |
| Fault recovery | You (restart) | You (restart) | Kernel (auto-resume) |
| Inference serving | Separate stack | Not included | Same platform |
| Pay for active compute only | No (hourly minimums) | No (hourly minimums) | Yes |
| Idle cost | Yes | Yes | Zero |
| Data sovereignty | Cloud-controlled | Cloud-controlled | Your control |
| Air-gap deployment | No | No | Yes (Private Cluster) |
How It Works — Training
Four actions. Everything else is the kernel.
| Step | What you do | What the kernel does |
|---|---|---|
| UPLOAD | Upload training data and scripts to your S3 bucket via the Files page or rclone. | Indexes your workspace, validates file paths, generates presigned URLs for workers. |
| WRITE | Write your model code. PyTorch, TensorFlow, HuggingFace — any framework, as-is. | Parses your job config, calculates GPU memory requirements, queues against available workers. |
| EXEC | Submit via the platform dashboard or Python SDK. | Selects optimal GPU nodes, provisions a Kubernetes cluster, configures distributed execution (NCCL/FSDP/FL), starts your job. |
| RETURN | Download results from your workspace. | Writes model checkpoints and logs back to your S3 bucket. On any node failure, auto-resumes from the last checkpoint. |
How It Works — Inference
Cold start under 10 seconds. Scale to zero when idle. OpenAI-compatible API for LLM serving.
| Step | What you do | What the kernel does |
|---|---|---|
| LOAD | Point at a HuggingFace repo or workspace checkpoint path. | Fetches the model, warms weights on the selected GPU node. |
| BIND | Set GPU type, concurrency limit, and autoscale min/max replicas. | Creates a Kubernetes Deployment with HPA, load balancer, and health checks. |
| LISTEN | Your endpoint URL is live. | Routes REST requests, serves /v1/completions and /v1/chat/completions for LLMs. Cold start < 10 seconds from zero replicas. |
| SCALE | No action needed. | HPA monitors RPS and GPU utilization. Spins replicas up and tears them down to zero when idle. Billing stops when idle. |
Three Cluster Types
ResonTech exposes three distinct GPU execution environments. Each serves a different workload profile, with a consistent API surface across all three.
| Pool | Model | SLA | Best for |
|---|---|---|---|
| Shared Pool | Multi-tenant, ephemeral, pay-as-you-go | Best effort | Experiments, prototyping, one-off training runs, burst workloads |
| Managed Cluster | Dedicated reserved nodes, isolated kernel, persistent state | 99.9% node availability | Production training pipelines, continuous retraining, inference at scale |
| Private Cluster | Your hardware, ResonTech orchestration, air-gapped capable | Enterprise SLA | Regulated data, HIPAA/SOC2/GDPR, existing GPU fleets, zero data egress |
FAQ
How is this different from AWS, GCP, or Lambda Labs?
Cloud providers rent you raw VMs — you still manage CUDA drivers, networking, checkpointing, and job scheduling yourself. Lambda Labs gives you a machine and an SSH key. ResonTech gives you a kernel: a full orchestration layer that handles everything between your code and the hardware. Submit a job, get results. No DevOps in between.
Do I need to rewrite my training code?
No. ResonTech is framework-agnostic. If your code runs on PyTorch, JAX, or TensorFlow today, it runs on ResonTech. Multi-GPU distribution uses standard NCCL / FSDP / DeepSpeed — no custom operators or proprietary APIs required. For federated learning, you wrap your training loop in a single function (fl_train_model) and return updated weights.
What GPU types are available on the Shared Pool?
The shared pool includes H100 SXM5, A100 80GB, A40, RTX 4090, and RTX 3090 nodes depending on availability and queue priority. Managed Clusters can be configured with specific GPU types — H100 NVLink, A100 PCIe, L40S — reserved exclusively for your workloads.
Where does my training data live?
Your data lives exclusively in your private S3 bucket, provisioned and isolated per account. Workers receive time-limited presigned URLs for their assigned data shards — raw data never traverses the ResonTech control plane. Only model weight updates (gradients) travel across nodes during federated training.
What happens if a node crashes mid-training?
The Coordinator detects the missing heartbeat within seconds, selects a replacement node, and the FLARE server manages partial aggregation. The replacement node loads the last successful checkpoint and resumes — zero compute lost.
What's Next
| If you want to… | Go to |
|---|---|
| Understand the full solution architecture | Solution |
| Browse cluster types and infrastructure detail | Infrastructure |
| See who uses ResonTech and how | Use Cases |
| Manage files and your S3 workspace | Files & Storage |
| Walk through the Submit wizard step by step | Submit Wizard |
| Write your model for federated learning | FL Integration Guide |
| Structure and upload your dataset shards | Dataset Format & Sharding |