▶ Hover to play
“Promptapp's PyTorch engineers compiled our custom transformer with TensorRT. Our p99 latency dropped from 220ms to 42ms with zero accuracy loss.”
Dr. Aris Thorne
Head of Deep Learning, NeuroScale Labs
Build, train, and deploy production-grade deep learning architectures with the industry's most flexible framework. From custom transformers and distributed multi-GPU training to ultra-low latency inference with TensorRT and TorchServe.
A rigorous engineering pipeline ensuring deep learning models transition seamlessly from training scripts to high-throughput cloud endpoints.
We architect custom DataLoader pipelines, loss function dynamics, and deep neural network layer topologies tailored to your data.
We train and fine-tune models across multi-node GPU clusters using Distributed Data Parallel (DDP), FSDP, and mixed-precision (FP16/BF16).
We compress model weights using post-training quantization (INT8/FP8), ONNX export, and NVIDIA TensorRT compilation for maximum throughput.
We containerize models with Triton Inference Server or TorchServe, with dynamic batching, telemetry logging, and autoscaling.
Every neural network we architect is tuned for sub-millisecond execution, high concurrency, and hardware cost efficiency.
We maximize compute efficiency using custom CUDA kernels, FlashAttention-2, and Triton compilers to eliminate compute bottlenecks.
Zero-bottleneck multi-node model training utilizing DeepSpeed, PyTorch FSDP, and Ray clusters for massive multi-billion parameter architectures.
We convert dynamic computational graphs into high-performance static runtime engines via TorchScript, ONNX Runtime, and TensorRT.
You retain complete custody over all trained model weights (.pt / .pth checkpoints), training scripts, datasets, and licensing rights.
Most academic researchers struggle with cloud scale, and typical software engineers cannot optimize matrix math. We do both:
Flexible engagement tiers designed for deep tech startups, enterprise innovation labs, and specialized AI teams.
A full cross-functional team of ML researchers, PyTorch developers, and GPU cloud infrastructure engineers dedicated to your technical roadmap.
A targeted, milestone-governed sprint to architect, train, fine-tune, or accelerate a specific neural network architecture for production launch.
Senior PyTorch engineers and CUDA specialists embedded directly into your ongoing development cycles to unblock core deep learning deliverables.
End-to-end mathematical and engineering depth across the modern PyTorch and deep learning ecosystem.
Architecting and fine-tuning custom transformer backbones, bespoke attention mechanisms, and Mixture-of-Experts (MoE) models using Hugging Face and PyTorch.
YOLO object detection, Vision Transformers (ViTs), instance segmentation (Mask R-CNN), and real-time GPU-accelerated video analytics pipelines.
Modular, highly structured deep learning codebases built with PyTorch Lightning and scaled with multi-node Distributed Data Parallel (DDP) execution.
End-to-end Automatic Speech Recognition (ASR), voice cloning, neural audio separation, and speech-to-text synthesis with state-of-the-art accuracy.
Post-training quantization (INT8/FP8), Quantization-Aware Training (QAT), and cross-platform model graph export via ONNX Runtime.
NVIDIA TensorRT compilation, custom CUDA C++ operator bindings, and FlashAttention integrations for ultra-low latency model inference.
Production model microservices supporting dynamic batching, concurrent GPU workers, health telemetry, and zero-downtime rolling model updates.
GNN architectures for graph representation learning, fraud detection networks, dynamic knowledge graphs, and bio-molecular prediction.
Our operational benchmarks achieved across commercial PyTorch deployments and GPU clusters:
Real stories from engineering leaders and ML researchers who scaled their deep learning workloads with Promptapp.
“Promptapp's PyTorch engineers compiled our custom transformer with TensorRT. Our p99 latency dropped from 220ms to 42ms with zero accuracy loss.”
“We had issues scaling training across 32 A100 GPUs. Their team configured PyTorch FSDP and resolved our memory paging bottlenecks in 3 days.”
“They deployed our custom TorchVision segmentation model to Triton Inference Server with dynamic batching. Flawless production execution.”
Key questions answered about custom PyTorch architecture, multi-GPU training, and inference serving.