5x+ Faster Deep Learning at Scale

Train and Control Multiple Models Simultaneously

For All Data and Model Sizes

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Why RapidFire

Agile Experiments. Rapid Results.

Hyperparallelized

Train many model configurations at once

Real-Time Control

Kill what doesn’t work, clone what does

Fully Optimized

Dramatically faster time to accuracy, lower GPU costs

Scale Out of the Box

Across all model and data sizes

Simple Setup

No MLOps friction, launch runs from a notebook

Our Technology

What RapidFire Does

Multi-Dimensional Parallelism

RapidFire AI features a patent-pending innovative hybrid-parallel distributed execution engine that we call multi-dimensional parallelism to scale workloads automatically on a cluster, improve GPU utilization, and reduce runtimes.

A new hybrid of sharded data parallelism and task parallelism helps us scale to arbitrarily large datasets across GPUs and machines in a resource-optimal manner that minimizes communication costs.

A new hybrid of task parallelism and model parallelism generalizes intra-machine data parallelism, FSDP, and DeepSpeed-style pipelining across GPUs in an automated manner that maximizes GPU utilization.

New analogues of "multi-query optimization" interleave computation, communication, and data movement across the memory hierarchy for concurrent model configurations in a holistic and resource-aware manner to reduce runtimes and reduce communication costs by 10,000x.

Features

Agile Development Environment

Train Multiple Models Simultaneously

Train and compare multiple model configurations at once, maximizing your GPU utilization, and accelerating your development cycle.

Parallel model training with optimized resource allocation
Compare any combination of hyperparameter values, model architecture changes, and even input/output representations

Control Your Models in Real Time

Take control of your training process with real-time monitoring and control capabilities.

Stop underperforming models in place. Resources automatically reapportioned to running models.
Clone high performing models, modify any knobs (hyperparameters, architecture, etc.), and add them to the mix. RapidFire AI elastically reapportions resources across models.

Quick Launch

Start developing in minutes with our streamlined setup process.

One-click cluster launch from our UI
Instant development from your notebook environment
Seamless integration with your existing Python workflow

Integrates Into Your Stack

Seamlessly integrate with your existing tools and cloud, minimizing disruption and maximizing efficiency.

Full support for your existing tools (Jupyter, PyTorch, Hugging Face, etc.)
Runs on Kubernetes and drops into your cloud ecosystem seamlessly
Complete visibility and control over deep learning experimentation