SYS / LIVE REGIONS / 03 AMEA  ·  EU  ·  AMER —
KAAZI SOLUTIONS  /  AI INFRASTRUCTURE & PLATFORM

Compute for AI. From the rack to the agent.

KAazi operates GPU clusters across three regions — and the platform, models, and agents that run on top of them. Built for teams shipping AI to production.

Request Compute → Deployment notes →
RACK · 01 AMEA 20 16 12 08 04 MGMT · SW K KAAZI · GPU 01 · H100 SXM 8× 62°C · OK K KAAZI · GPU 02 · H100 SXM 8× 64°C · OK K KAAZI · GPU 03 · H200 SXM 8× 68°C · OK PDU · 32A LOAD 18.4 kW / 32A · LINE OK
Compute H100 · H200 · GB200
Fabric NDR InfiniBand · 400 Gbps
Orchestration Kubernetes · Slurm
Footprint Multi-region · Tier-III
01 / Nodes

The infrastructure we operate.

Bare-metal GPU clusters tuned for training and inference, networked with RDMA fabric and storage built for the throughput AI actually needs.

01.1 — Compute

GPU Clusters

NVIDIA H100 and H200 SXM nodes, GB200 NVL72 on roadmap. Tuned per workload — training pods or low-latency inference fleets.

01.2 — Fabric

Interconnect

NDR InfiniBand at 400 Gbps with RoCEv2 fallback. Non-blocking topology for collectives across hundreds of nodes.

01.3 — Storage

Parallel I/O

NVMe-oF parallel pools, Lustre for hot datasets, S3-compatible object for cold. Tiered automatically by access pattern.

02 / Regions

Where the GPUs live.

Three macro regions, each with primary and secondary points-of-presence. Workloads pin to a region; the control plane spans all three.

AMEA
Lagos  ·  Johannesburg

Africa-anchored compute, designed for teams shipping AI into emerging markets and serving users on the continent.

Sovereignty NG  ·  ZA data residency  /  controlled egress
Latency < 35 ms intra-region  /  ~90 ms to EU
Clusters H100 SXM  ·  H200 (rolling)
Fabric NDR InfiniBand  ·  400 Gbps
EU
London  ·  Frankfurt

European-anchored compute with GDPR-aligned residency and low-latency reach into UK and continental Europe.

Sovereignty UK  ·  EU residency  /  GDPR-aligned
Latency < 25 ms intra-region  /  ~80 ms to AMER East
Clusters H100 SXM  ·  H200
Fabric NDR InfiniBand  ·  400 Gbps
AMER
Ashburn  ·  São Paulo

North & South American compute, anchored in the Ashburn corridor with LATAM reach for production inference.

Sovereignty US  ·  BR residency  /  controlled cross-border
Latency < 20 ms intra-region  /  ~80 ms to EU
Clusters H100 SXM  ·  H200  ·  GB200 (Q4)
Fabric NDR InfiniBand  ·  400 Gbps
03 / Platform

What runs on top.

A managed platform layer for training, inference, and agent runtimes. Bring your model — or pick from open-weight families we keep ready.

03.1 — Serving

Managed Inference

vLLM and TensorRT-LLM backends, autoscaling endpoints, batching, speculative decoding. Open and closed models.

03.2 — Training

Training Orchestration

Slurm + Kubernetes (Volcano) job queues. Multi-node DDP, FSDP, checkpoint streaming, automatic restart on node loss.

03.3 — Agents

Agent Runtime

Multi-agent coordination, tool use, persistent memory, retrieval. Observability built in — every call traced end-to-end.

03.4 — Models

Model Registry

Versioned models, fine-tune pipelines, evaluation harnesses. Promote checkpoints from experiment to prod with audit trail.

04 / Services

What we build for you.

Beyond infrastructure: the team that designs the model, the agent, and the system that ships them. End-to-end engagements, scoped to outcome.

04.1 — Fine-Tuning

Custom Models

Domain LoRA or full fine-tune on open-weight bases (Llama, Mistral, Qwen). Evaluation harness, hosted or self-served.

04.2 — Agents

Agent Engineering

Bespoke agent systems engineered for your operations. Multi-step planning, tool use, structured outputs, real-world action.

04.3 — Software

AI-Native Apps

Full-stack products where AI is the architecture, not a bolt-on. Shipped to production, on our infra or yours.

04.4 — Advisory

Strategy & Architecture

Where AI gives leverage, what to build, what to buy, what to skip. For teams without an in-house frontier AI lead.

05 / Why

A different shape of AI infrastructure company.

Most providers stop at the rack. We own the layer above too — and we use it ourselves.

— 01

Full stack.

Rack, platform, models, agents — one team, one accountability. No finger-pointing between infra vendor and AI vendor when something breaks.

— 02

Multi-region by default.

Three regions live, more on the way. Latency-routed inference, data-sovereign training, failover that doesn't require a re-architecture.

— 03

Production-grade.

Built for SLOs, not demos. Observable, debuggable, durable. We page on what matters — and we share the runbook.

— 04

Open-weight friendly.

No vendor lock-in to a single model family. Run Llama, Mistral, Qwen, or your own fine-tune — same platform, same orchestration.

06 / Scale

Deployment 01 — a multi-region cluster, orchestrated by agents.

A large training and inference deployment spanning three regions, with an autonomous control-plane that routes workloads, recovers from node loss, and rebalances under shifting load.

03 / 03
Regions Active
99.97%
Control-Plane Uptime
400 Gb
Per-Node Fabric
0 → 1
Self-Recovery
01 / Constraint

Multi-region routing at scale.

Traditional orchestrators assumed single-AZ topology. As cluster size grew across regions, scheduling latency and cascading failures became the bottleneck — not compute.

02 / Approach

Agent-led control plane.

Coordinated agents observe traffic, learn workload patterns, predict load, and route between regions in real time. The control plane is itself an AI system running on the cluster.

03 / Outcome

Autonomous, durable, observable.

Self-heals on node loss without operator action. Continuously re-balances. Every decision is traceable — and the model behind it is on the registry, like any other.

07 / Stack

The full stack, in plain sight.

No mystery box. Here is what runs underneath everything we ship.

Compute
NVIDIA H100 SXM H200 GB200 NVL72 A100 (legacy)
Networking
NDR InfiniBand 400 Gbps RoCEv2 RDMA-everywhere SR-IOV
Storage
NVMe-oF Lustre WekaFS S3-compatible object
Orchestration
Kubernetes Volcano Slurm custom operators
Inference
vLLM TensorRT-LLM SGLang Triton
Training
PyTorch FSDP DeepSpeed Megatron-LM HF Accelerate
Models
Llama Mistral Qwen DeepSeek custom fine-tunes
Observability
Prometheus Grafana OpenTelemetry Loki custom telemetry
08 / Team

Operators, not slideware.

A small team with the rare overlap of deep AI infrastructure experience and frontier model engineering.

Ayobami Awoyinfa
CEO

Ayobami Awoyinfa

12+ years in data centers, networking, and AI infrastructure program operations. Sets direction across the rack-to-agent stack. Building the AI infrastructure layer Africa and the world needs — GPU clusters, models, agents, full stack.

TO
CFO

Timothy Oke

Architects the financial backbone — capital strategy, runway, and the unit economics of compute.

AA
CTO

Afeez Adeyemo

Leads engineering — infrastructure, platform, and the model layer. Owns the technical bets KAazi makes.

09 / FAQ

Questions worth asking up front.

What workloads run well on KAazi?

Frontier model training, large-scale fine-tuning, low-latency inference, agent runtimes, and AI-native applications. Anywhere you need NVIDIA GPUs at non-toy scale with RDMA fabric beneath them.

Do you operate your own data centers, or use partner facilities?

Hybrid. We operate inside Tier-III colocation facilities with our own rack designs, networking, and orchestration stack. The hardware and software above the floor tiles are ours.

Can we bring our own model weights?

Yes. Open-weight or proprietary. We deploy what you ship, keep it isolated, and never train on your weights or your data.

How do you handle data sovereignty across regions?

Workloads pin to the region you choose. Cross-region replication is explicit, controlled by you, and auditable. Each region runs an independent control plane that can operate standalone.

What's the engagement model — hourly, reserved, dedicated?

All three. On-demand for spiky workloads, reserved capacity for predictable production, dedicated clusters for teams that need isolation and tuned topology. Talk to us about scope and we'll structure it.

How does KAazi compare to AWS, GCP, or CoreWeave?

The hyperscalers give you compute. CoreWeave-class providers give you GPU-focused compute. We give you compute plus the platform and the AI team — so a single accountable provider can take you from procurement to production model.

10 / Contact

Tell us what you're shipping.

Capacity request, custom build, or just an early conversation — same form, same inbox.

Whether you need 8 GPUs for a fine-tune or a reserved multi-region cluster for production inference, start here. We reply within one business day.

Sales sales@kaazidevs.com
Operating Regions AMEA  ·  EU  ·  AMER
Response < 1 business day