Solutions
·Edge & Embedded
Cloud-to-edge
in one pipeline.
Train on powerful cloud GPUs, then optimize for deployment anywhere. TensorRT, ONNX Runtime, and quantization tools (INT8, INT4) are pre-installed on every instance. Export production-ready models to Jetson, mobile, and IoT devices without setting up a single toolchain.
Why Runcrate
Train big, deploy small, iterate fast.
TensorRT optimization
Convert PyTorch and ONNX models to TensorRT engines optimized for your target device. Layer fusion, kernel auto-tuning, and precision calibration included.
ONNX Runtime export
Export to ONNX format for cross-platform deployment. Run on Jetson, Android, iOS, Windows, Linux, and web browsers with a single model artifact.
INT8 & INT4 quantization
Post-training quantization and quantization-aware training with GPTQ, AWQ, and bitsandbytes. Shrink models 4-8x with minimal accuracy loss for edge deployment.
Jetson & mobile targets
Cross-compile for NVIDIA Jetson (Orin, Xavier), Android (NNAPI, TFLite), and iOS (CoreML). Test inference benchmarks on cloud GPUs before shipping to hardware.
Model profiling & benchmarking
Profile latency, throughput, and memory usage before deploying. Compare FP32 vs FP16 vs INT8 accuracy-latency tradeoffs on the same instance.
Full optimization toolchain
TensorRT, ONNX Runtime, TorchScript, OpenVINO, TFLite, and CoreML converters all pre-installed. No dependency hell — every tool works together out of the box.
Hardware
Train and optimize
on the same cluster.
Use powerful GPUs for training, then run quantization calibration and TensorRT compilation on the same hardware. No separate optimization environment needed.
H10080 GB HBM3 · FP8 Tensor CoresTraining + TensorRT compilationA10080 GB HBM2e · INT8 Tensor CoresQuantization calibrationL40S48 GB GDDR6X · Ada LovelaceProfiling & export workflowsHow It Works
The cloud-to-edge pipeline.
Train your model in the cloud
Train on H100 or A100 GPUs with full PyTorch support. Use any architecture — vision, NLP, multimodal. Save checkpoints to persistent storage.
Optimize and quantize
Convert to ONNX, compile with TensorRT, and quantize to INT8 or INT4. Profile accuracy-latency tradeoffs. Calibrate quantization on representative data.
Export to edge targets
Download optimized models for Jetson, mobile (CoreML, TFLite), or IoT. Ship a model that runs in 5ms instead of 500ms. Come back to retrain when you need to.
From cloud GPU to edge device.
Train, optimize, quantize, and export — all on one platform. Every optimization tool pre-installed. Per-minute billing, no commitments.