01
Training and fine-tuning
Dense compute blocks designed for model development and domain fine-tuning, deployable incrementally rather than as one campus build.
AI training, inference, and HPC where the energy is
Physical infrastructure for AI and high-performance workloads, sited near onsite energy instead of waiting behind a utility interconnection queue.
Compute
Infrastructure for changing hardware, not one generation of it
BlackFlare does not build GPUs or foundation models. It is developing the power, thermal, and control infrastructure around modular compute blocks so that training, fine-tuning, inference, simulation, and research workloads can run at remote, energy-rich, or grid-constrained sites. The platform is intended to accommodate GPUs, inference accelerators, and HPC processors as architectures change.
02
Inference and analytics
Batch and high-volume inference, industrial analytics, image and video processing, and remote analytics close to the data.
03
Scientific and research computing
Simulation, scientific computing, and federal research workloads at locations where conventional data-center capacity is unavailable.
Tell us about the workload and the site
Different workloads have different electrical, thermal, and network characteristics. We size the infrastructure to both the compute and the energy available.