Service
Train custom deep learning models with Rust and Burn — the native Rust framework that delivers PyTorch-class performance with C-level portability. No Python overhead, no runtime bloat, no vendor lock-in.
Key capabilities
Overview
Model training is the process of teaching a neural network to perform a specific task by exposing it to labelled data and iteratively adjusting its parameters to minimise prediction error. The result is a custom model purpose-built for your domain, data, and performance requirements — rather than relying on a generic off-the-shelf model.
Most teams train models using Python frameworks (PyTorch, TensorFlow) and then face a difficult deployment problem: the Python runtime, its dependency tree, and the model format all need to be packaged and served. With Burn, you train and deploy in the same language — Rust — producing a single native binary that runs anywhere without a runtime interpreter.
Your data
Train on proprietary or sensitive data — never send it to a third-party API. Full privacy from day one.
Your infrastructure
Train on your own GPUs or cloud instances. Deploy the trained model as a single static binary anywhere.
Your schedule
Fine-tune quarterly, retrain monthly, or update continuously. No per-token costs, no vendor rate limits.
Approaches
Every project is different. We match the training strategy to your data volume, performance targets, and operational constraints.
Design and train a custom neural network architecture for your specific problem. Best when you have substantial domain-specific data, unique input modalities, or hard performance/latency constraints that off-the-shelf models cannot meet.
Start from a pre-trained open-weight model (Llama, Mistral, Qwen, Gemma, Phi) and fine-tune on your domain data. LoRA, QLoRA, and full fine-tuning options. The fastest path to a specialised model with minimal data requirements.
Augment a pre-trained model with external knowledge or task-specific adapters without modifying the base model weights. Best for knowledge-intensive tasks, multi-tenant setups, or when model updates must be isolated per customer.
Our process
We take you from problem definition to production deployment — with a trained, optimised model running as a single Rust binary on your infrastructure.
Understand your problem, data, and success criteria. Evaluate whether training from scratch, fine-tuning, or a RAG approach is the right path. Define accuracy, latency, and throughput targets.
Design or select the model architecture. Clean, augment, and split your data. Set up the Burn training pipeline with data loading, augmentation, and metrics logging. Establish evaluation benchmarks.
Train the model, monitor convergence, and iterate on hyperparameters. Apply quantisation, pruning, or distillation to meet deployment constraints. Validate against your success criteria.
Package the model into a Rust binary with an inference API. Deploy on your infrastructure — cloud, on-prem, edge, or air-gapped. Integrate with your application via REST, gRPC, or message queue.
Track model performance in production, collect feedback, and retrain on new data. We help you establish a continuous training pipeline so your model stays current.
Services
End-to-end model training — from problem scoping and data preparation through architecture design, training, optimisation, and production deployment.
Design neural network architectures tailored to your data modality (text, image, audio, tabular) and performance requirements. We select the right building blocks — transformers, CNNs, state-space models, or hybrid architectures.
Build robust training pipelines in Burn: data loading and augmentation, automatic differentiation, learning rate scheduling, checkpointing, metrics logging, and experiment tracking. Production-grade pipelines that resume gracefully from failures.
Reduce model size and inference latency through post-training quantisation (PTQ) and quantisation-aware training (QAT). Run on consumer GPUs, edge devices, or CPUs with minimal accuracy loss.
Package the trained model into a single Rust binary with an HTTP API (via Actix or Axum), deploy on bare metal, container, or serverless, and integrate with your existing infrastructure. No Python runtime required.
Fine-tune open-weight models on your domain data using LoRA, QLoRA, or full fine-tuning. We handle data preparation, hyperparameter search, evaluation, and deployment of the adapted model.
Scale training across multiple GPUs and nodes for large models or massive datasets. We configure NCCL-based multi-GPU training in Burn, manage data parallelism, and optimise throughput.
Train custom forecasting models for sales, inventory, staffing, maintenance, and demand planning. Deploy as single-binary Rust models that surface predictions through C-level and operational dashboards — no Python runtime required.
Our recommendation
We recommend <strong><a href="https://burn.dev" target="_blank" rel="noopener noreferrer" class="link link-hover text-primary">Burn</a></strong> — the open-source, next-generation deep learning framework built in Rust. It is the only Rust-native framework designed from the ground up for both training and inference, without compromising on flexibility, efficiency, or portability.
Burn's backend-agnostic architecture means you write your model once and train it on any hardware — NVIDIA CUDA, AMD ROCm, Apple Metal, Vulkan, WebGPU, or plain CPU. Its CubeCL kernel system automatically fuses operations and tunes them for your specific hardware, delivering performance that often exceeds LibTorch (PyTorch's C++ backend). The same code that trains your model also serves it in production, eliminating the training-to-deployment gap that plagues Python-based workflows.
11K+
GitHub stars
8
Hardware backends
2
Licenses (MIT + Apache 2.0)
v0.20
Latest release
Comparison
Python (PyTorch / TensorFlow) remains the dominant ecosystem for ML research and experimentation. Burn is the right choice when deployment simplicity, portability, and long-term operational cost matter more than having the absolute latest research feature on day one.
Python with PyTorch remains the most widely used ML training stack. Its ecosystem is unmatched for research and experimentation, with every model architecture, paper, and tool available first. The trade-off is deployment complexity: packaging the Python runtime, its dependency tree, and model weights into a production service is a hard problem that many teams underestimate.
Burn is the open-source, next-generation deep learning framework built in Rust. It is the only Rust-native framework designed from the ground up for both training and inference, without compromising on flexibility, efficiency, or portability. Burn's backend-agnostic architecture means you write your model once and train it on any hardware — the same code also serves inference in production.
Our recommendation: Use Python during research and prototyping when you need the broadest ecosystem. Migrate to Burn for production training and deployment when you need a single binary, predictable performance, and the ability to run on any hardware without runtime dependencies. We specialise in this transition.
FAQ
Next step
Book a consultation and we'll assess your problem, recommend the right training strategy, and design a path from data to deployed model.