# Research-grade AI. Production-grade infrastructure. The platform research teams build on when the model has to run on real hardware. Constrained, sovereign, or air-gapped. Same model. Your environment. Program names and logos are property of their respective owners. ## Shrink the model. Or keep it. The conventional path makes the model smaller until it fits the hardware. We take the opposite path. Keep the model. Make the hardware reach further. ### Shrink the model Distil, quantise, prune. The model that ships is not the model that was trained. ### Keep the model. Tier the memory. We orchestrate model weights across whatever memory your system has available. The model that ships is the model that was trained. ## A systems problem. Years in the making. Memory management, kernel selection, hardware abstraction, deployment, fleet telemetry, security hardening. All of it has to work together, on every device, in every environment. ### The duct-tape stack Patched inference servers, custom CUDA kernels, bash scripts for deployment. Works on one machine until the hardware changes. ### One integrated platform Memory tiering, engine selection, fleet control, air-gapped deploy, audit, identity. Tested across Jetson, x86, GPU, CPU-only. ## Memory hierarchy.In production. Runtime probes the hardware, picks the engine, and tiers the model across whatever memory is available. GPU, system RAM, and disk. p95 latency stays predictable. The model that was trained is the model that serves. Same Runtime on a Jetson at a ground station, a research workstation, and a server rack in a SCIF. Llama-3-70B-Q4_K_M VRAM (Tier 1) 16.8 / 24 GB RAM (Tier 2) 42.3 / 64 GB SSD Cache (Tier 3) 128 / 512 GB Serving Throughput 7.8 tok/s Latency 118 ms OOM Events 0 Uptime 0s ## Three layers. One platform. ### Hub The control plane. Manage models, nodes, and deployments across every site. Audit, RBAC, telemetry, version control. ### Runtime The execution layer. Tiered memory across VRAM, RAM, NVMe. Models that should not fit run stable. GPU, CPU, or mixed. ### Deploy Single Helm chart. Air-gapped install from media. No external dependencies. Same artifact from lab to ground station to facility. ## Every node. One pane. Canary deploys, rollback on failure, hot-swap models, rotate credentials. Same model promoted across the lab, the field, and the facility. Audit trails into your SIEM. Identity through your IdP. Telemetry stays local, syncs when connectivity does. 4 3 99.9% 0 Active Deployments ground-station-08 VRAM 16.8/24 tok/s 7.8 Uptime 22d ops-center-03 VRAM 5.2/16 tok/s 24.1 Uptime 8d rig-platform-11 VRAM 6.1/8 tok/s 18.6 Uptime 45d datacenter-sg-02 VRAM -- tok/s -- Uptime 0s Activity ## Three ways in. We work with research partners in a few different shapes. Each one starts the same way: a real environment and a real problem. ### Hands-on access We give your team direct access to the platform in a real environment. You run the models, we sit alongside as you go. ### Co-funded research We co-apply on research programs where you bring the academic side and we bring the industry side. National and international funds. ### Embedded partnership Longer-term research engagements where we deploy alongside your program. Shape and terms scoped to the relationship. ## The technical shape. ### Memory orchestration Weight tiering across VRAM, RAM, and NVMe. p95 latency stays predictable under load. ### Model support Open-weight transformers, custom fine-tunes, multimodal. vLLM, Triton, llama.cpp serving paths. ### Validated hardware Jetson Orin and Orin NX, industrial edge PCs, x86 with or without GPUs, CPU-only environments. ### Air-gapped install Single Helm chart. Offline model registry. Signed bundles. Zero external dependencies. ### Audit and identity Audit trails into your SIEM. Identity through your IdP. RBAC at model and node level. ### Sovereign deployment Data stays on premises. Models stay on premises. No phone-home. No outbound calls. ## Real environments. Real constraints. ### Ground station Vision-language model on a Jetson Orin. Imagery processed at the edge. Same artifact promotes to a server rack. ### Sovereign facility LLM on classified hardware. Air-gapped install from media. No phone-home, no licence server, no outbound calls. ### University lab Multi-model benchmarking on shared, mixed-generation GPUs. Auto-detect, auto-tier, version-tracked results. ## Publishable work. Not just a deployment. Latency, throughput, and memory data across hardware. Reproducible and citable. Memory-tiered inference, edge deployment, sovereign AI. Your research, our experimental foundation. Deployment evidence, validation data, and industry letters for funding applications. ## Build with us. We work with a small number of research partners at a time. Tell us about the environment, the hardware, and the model you are trying to run.