We design, train and deploy deep learning models in PyTorch, from fine-tuned language models to computer vision on the factory floor, and we make them fast and affordable to run.
Every PyTorch model learns the same way: thousands of tiny corrections. Press Train to watch the error fall. The curve is illustrative.
Released in July 2026. Numbers below are from the official release notes.
faster FlexAttention on Apple Silicon for long, sparse attention patterns
vs SDPA, best caselower peak GPU memory for large-vocabulary training with LinearCrossEntropyLoss
up toor less overhead for fully reproducible attention gradients
at long sequencescommits in one release
from 526 contributorsMost AI projects fail between the prototype and production. Our pipeline closes that gap.
Most new open models are released for PyTorch first, and the major tools for training, fine-tuning and serving are built around it. That means faster delivery and no lock-in.
PyTorch supports a wide range of chips. Bars show our rough view of tooling maturity for each, not a benchmark.
Models that solve a measurable business problem.
Training your own model is not always the answer. We help you pick the cheapest route that meets your accuracy goal.
| Option | Strengths | When we choose it |
|---|---|---|
| PyTorch | Pythonic, flexible, the default for research and modern LLMs | Custom models, LLMs, vision, most new AI work |
| TensorFlow / Keras | Mature serving and mobile tooling | Existing TF systems, some production pipelines |
| Hosted model APIs | No training needed, fastest to launch | When a general model already does the job |
| Open-weight LLMs | Run and fine-tune yourself | Private deployments, cost control |
| Databricks | Data and ML platform at scale | Large data teams on lakehouse |
Straight answers to what clients ask us most.
PyTorch is an open-source deep learning framework for building and training neural networks: language models, computer vision, speech, recommendation and forecasting. It is governed by the PyTorch Foundation under the Linux Foundation and is the default choice for most modern AI research and many production systems.
For new projects we usually choose PyTorch: it is easier to debug, has the largest model ecosystem (including Hugging Face) and is what most new models are released in. We still support and maintain TensorFlow systems where they already run well.
Released in July 2026, PyTorch 2.13 brought FlexAttention to Apple Silicon, a fused LinearCrossEntropyLoss that cuts memory for large-vocabulary training by up to about 4x, faster FSDP2 communication, deterministic attention gradients, and native loading of safetensors files.
Often not. Many problems are solved with a hosted model or an open model plus retrieval. We train or fine-tune in PyTorch when you need higher accuracy on your data, lower running costs, privacy, or offline use.
It depends on the task. Fine-tuning an existing model can work with a few hundred to a few thousand good examples; training vision models from scratch needs far more. We start with a quick data audit and a small baseline.
Wherever it makes sense: cloud GPUs on AWS, Azure or Google Cloud, your own servers, or on phones and edge devices with ExecuTorch. We optimise with torch.compile and quantisation to cut serving costs.
A proof of concept usually takes four to eight weeks; a production system with monitoring takes longer depending on data and integrations. We work in milestones with measurable accuracy targets.
It depends on data, model size and GPU needs. We give a fixed price per milestone and estimate GPU costs up front.
Tell us what you want to build, your data and integrations, and your timeline. We reply within 4 business hours with honest advice.