One stack, six entry points. Whether the starting problem is an API bill, a data-residency requirement, or a training run that needs its own cluster, every lane below runs on the GLabs AI Stack and reports through the same GenesysBench™ validation — only the hardware shape changes.
GLabs AI Stack
Self-Hosted LLM Inference
Stop Paying the API Bill
Every per-token invoice is a recurring cost that scales with usage, not a one-time investment. Self-hosted inference moves LLM serving onto infrastructure you own — from a single model behind an internal API to multi-tenant serving across teams — with no outbound call to a third-party endpoint and no usage meter running in the background.
Built On: vLLM and Ollama pre-installed, OpenAI-compatible endpoint out of the box.
Fine-tuning on proprietary or regulated data — contracts, patient records, internal codebases — is a different risk profile than calling a public API. Running the fine-tuning job on owned infrastructure keeps the dataset under your access controls for its entire lifecycle, not just in transit.
Built On: Hugging Face PEFT, LoRA, Axolotl, Unsloth; disk encryption on by default.
Agent frameworks and local vector-store retrieval need low-latency access to both compute and your own document stores — something a cloud-hosted agent has to work around. Building and running agents locally keeps the retrieval loop and the tool-calling loop on infrastructure you control, from a developer's desk to a small internal fleet.
Built On: MCP Server pre-configured for Cursor, Windsurf and Claude; LangChain, CrewAI, AutoGen; Qdrant and ChromaDB for local vector search.
Pre-training a foundation model from scratch is a different problem than fine-tuning or inference — it needs clustered compute with high-bandwidth interconnect between nodes, sustained for days or weeks, not a single powerful machine. This is where the stack-layer view matters as much as the chassis spec.
Built On: Megatron-LM, DeepSpeed ZeRO-3, NCCL, SLURM; orchestrated through NVIDIA Base Command.
Real-time vision and video inference — on a factory line, in a vehicle, across a camera network — is latency-sensitive in a way that rules out a round trip to a data center. Edge-class infrastructure puts the inference step physically close to the point of capture, with a path to scale that inference up into shared GPU capacity when volume grows.
Built On: NVIDIA DeepStream and Metropolis; YOLO, OpenCV, MONAI; ROS 2 for robotics integration.
Some AI programs outgrow any single lane above — they need power, cooling, hardware, software, and operations designed together as one build, not a server order. This is the end-to-end tier: sovereign and enterprise-scale AI infrastructure, delivered as a paired program with the data-centre design-and-build service.
