Sizing, deployment and inference optimization on infrastructure you control.
Some data can't leave. Some teams don't want it to. And some companies simply want to own their AI: models trained on their data, running on infrastructure nobody can take away. We design, deploy and optimize local AI, from a single workstation to a cluster, at the speed and budget required for your use case.
Compact box
Workstation
Cluster
Large cluster
Most clusters we look at run far below their ceiling. Engine tuning, batching, load balancing and other optimization techniques offer substantial improvements. Maximizing the hardware you own is usually cheaper than the hardware you're about to buy.
We start from your objectives for latency, throughput and GPU utilization, and we tailor the inference engine to your use case and your infrastructure. We get the best out of the existing engines, tuning and benchmarking them on your hardware, and we monitor how they perform once in production. And when the best of what exists isn't enough, we develop custom optimizations of our own.
Internal benchmarks. 35B class MoE, one RTX PRO 6000 (NVIDIA).
A local AI workstation designed by Hypermind for small teams of 2 to 30 people. Run high-performing models, AI agents and development tools entirely on-prem. No cloud dependency, no data leaving your walls.
If you're interested, let's have a chat to see how this technology can fit your needs.
If you're an AI native company building agents for a specific vertical, and your customers want your product on-prem, we're your deployment partner. You keep the product and the relationship. We handle sizing, hardware, deployment and inference optimization so your agents run inside their walls the way they run in your cloud.
Private AIOps, delivered as one system.
2501.ai builds autonomous agents for IT operations. Agents that monitor, diagnose and remediate incidents without human intervention. We provide the infrastructure to run them privately: GPU servers specced for sustained agentic inference, an inference stack tuned for that hardware, and the agents pre-installed before the rack ships.
The rack arrives at the client's data center and connects to their environment. No external API calls, no cloud dependency, no data leaving the premises.
We run the Local Inference Meetup community. The objective is to gather people working on hardware, models and end users to foster experience sharing and promote local deployments. Active in Paris and London, with more cities coming soon.
We will send you the invitation to the next Local Inference Meetup.