Self-Hosted LLM Inference
Stop Paying the API Bill
AI SOLUTIONS
AI Solutions
Three lanes: the GLabs AI Stack for running and training your own models, Computer Vision & Video AI, and AI Data Centers & AI Factories.
GLabs AI Stack
Four use cases, one stack.
Each use case below runs on the GLabs AI Stack and reports through the same GenesysBench™ validation — only the hardware shape changes.
Use casesSelf-Hosted LLM InferenceFine-Tuning & Domain AdaptationAI Agents & Agentic WorkflowsFoundation Model Pre-Training
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 onvLLM and Ollama pre-installed, OpenAI-compatible endpoint out of the box.
ExploreGLabs GPU Servers
Fine-Tuning & Domain Adaptation
Your Data Never Leaves the Building
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 onHugging Face PEFT, LoRA, Axolotl, Unsloth; disk encryption on by default.
AI Agents & Agentic Workflows
Agents That Run Where You Work
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 onMCP Server pre-configured for Cursor, Windsurf and Claude; LangChain, CrewAI, AutoGen; Qdrant and ChromaDB for local vector search.
Foundation Model Pre-Training
Training Needs a Different Kind of Infrastructure
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 onMegatron-LM, DeepSpeed ZeRO-3, NCCL, SLURM; orchestrated through NVIDIA Base Command.
ExploreGLabs HPCGLabs AI Factory
Computer Vision & Video AI
Inference Where the Camera Is
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 onNVIDIA DeepStream and Metropolis; YOLO, OpenCV, MONAI; ROS 2 for robotics integration.
AI Data Centers & AI Factories
Beyond the Lane: Sovereign-Scale Infrastructure
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.
ExploreGLabs AI Factory
Tell us what you're building.
A solutions architect responds directly — no ticket queue.