Nigeria AI Scaling Hub
Reference

Glossary

Definitions of the acronyms, models, and technical terms used across the NAISH documentation.

Definitions of the acronyms, organisations, models, and technical terms used throughout this documentation.

Organisations & programme

TermDefinition
NAISHNigeria AI Scaling Hub – the national AI infrastructure initiative documented here
FMCIDEFederal Ministry of Communications, Innovation & Digital Economy – client / partner
LBSLagos Business School (Pan-Atlantic University) – primary Gates grant recipient; manages and runs the Hub
DSNData Science Nigeria – technical implementation partner
GBBGalaxy Backbone – physical hosting provider, Abuja
NCAIRNational Centre for AI and Robotics – technical sign-off authority per RFP
UduTech–Kasi Cloud ConsortiumGPU hardware vendor (Ref: NAISH/INFRA/GPU-2026-02)
DimagiOrganisation behind CommCare, the frontline data-collection platform

Hardware & infrastructure

TermDefinition
H200 SXM5NVIDIA data-centre GPU; SXM5 socket form factor with 141 GB HBM3e
HBM3eHigh-Bandwidth Memory (3rd-gen enhanced) – the on-package GPU memory
HGXNVIDIA's multi-GPU baseboard platform; the 8 GPUs sit on one HGX baseboard
NVLink / NVSwitchNVIDIA's high-bandwidth GPU-to-GPU interconnect fabric (900 GB/s full-mesh here)
SXM5 vs PCIeSXM5 uses the HGX baseboard + NVSwitch (900 GB/s); PCIe is limited to ~64 GB/s over the host bus
TDPThermal Design Power – the power / heat a component is rated to dissipate
BTU/hrBritish Thermal Units per hour – heat-rejection rate the cooling system must handle
CFMCubic Feet per Minute – airflow requirement for cooling
PDUPower Distribution Unit – rack power feed (2× 15 kW metered here)
IPMI / RedfishOut-of-band hardware management interfaces

Software & platform

TermDefinition
AGHAfrica GPU Hub – the vendor's optional orchestration layer
KubernetesContainer orchestration platform
Volcano / KueueBatch scheduling and job-queueing systems for Kubernetes
Time-slicingSplitting a single GPU across multiple workloads logically (via the NVIDIA Device Plugin)
vLLMHigh-throughput LLM inference server with continuous batching
JupyterHubMulti-user notebook environment
MLflowExperiment and model-tracking system
MinIOS3-compatible object storage, deployed on-node
KeycloakIdentity and access-management system
Prometheus / Grafana / DCGM ExporterMetrics collection, dashboards, and GPU telemetry

Models & AI terms

TermDefinition
N-ATLaS-LLM 8BOpen-source Nigerian-language large language model (agricultural advisory)
Whisper-large-v3OpenAI's automatic speech-recognition (ASR) model (ORF assessment)
TrOCR / PaddleOCROptical Character Recognition models (health-record digitisation)
VRAMVideo RAM – GPU memory a model occupies while loaded
FP16 / INT8Numeric precisions for model weights; INT8 (quantised) roughly halves VRAM vs FP16
LoRA / QLoRAParameter-efficient fine-tuning methods that reduce the compute needed to adapt LLMs
RLHF / DPOReinforcement Learning from Human Feedback / Direct Preference Optimisation – alignment methods
ASRAutomatic Speech Recognition
OCR / NLPOptical Character Recognition / Natural Language Processing

Health, data & compliance

TermDefinition
DHIS2District Health Information System 2 – standard health-data platform
EHRElectronic Health Record
NDPANigeria Data Protection Act
SOC 2 / ISO 27001Information-security compliance frameworks
AI POCUSAI Point-of-Care Ultrasound
ORFOral Reading Fluency
Self-CAIREThe digital health-coaching use case delivered via mDoc's "Kem" platform
ChatMRPTMalaria intervention allocation copilot

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