Distributed neural simulation with MPI for Brian2 models
Stage
Not given
Startup
Founded
Not given
2026
Based in
US
GB
Pricing model
Paid
Open source
Pricing
Usage-based pay-as-you-go pricing with hourly compute charges; committed contracts available for volume discounts. Hosted option with Anyscale-managed infrastructure or Bring Your Own Cloud deployment available.
Not given
Key features
Multimodal data curation at scale
Distributed model training with elastic scaling
Batch embedding generation at scale
Multi-cloud deployment and orchestration
Cluster-backed development environments
Workload-specific observability and debugging
Production-grade managed Ray clusters
Access control and governance
GPU budget controls and cost attribution
Post-training with SkyRL and veRL support
Distributed simulation across MPI ranks
Multi-backend execution (CPU, GPU, Browser, MPI)
AtlasIR typed intermediate representation
Native forward and reverse execution for training
Retains Brian2 modelling interface
GPU execution on Apple Metal and NVIDIA CUDA
WebAssembly browser execution
CPU reference and AOT compilation
What makes it different
Built by creators of Ray, the world's most widely adopted AI compute engine
Multi-cloud deployment without code changes
Feels local but runs distributed
Unified GPU pooling across clouds and regions
Distributed execution without sacrificing Brian2's familiar modelling interface
Open-source with published arXiv paper and reproduction materials
Brian2 Atlas