AI inference where you need it.

Models too big for your hardware. Remote sites with no cloud. We make it run.

[ Platform ]

One platform. Every environment.

One install. Any hardware. Any network. We show up and make it work.

Runs on any hardware

GPU, CPU, TPU, or mixed. Runtime probes the box and configures itself for what is there. A CPU-only field server, a single Jetson at a ground station, or a rack of H100s in a SCIF. Same install, same API.

Deploys to any environment

Cloud, on-prem, edge, air-gapped, or fully disconnected. Install over a clean network or an empty one. Same Runtime. Same Hub. Same API.

Stays on your side of the wire

Your data never leaves your network. Zero egress on Pro and Enterprise. No metered tokens. Prompts, responses, weights, and traces stay on the hardware you installed on, from a ground station on a disconnected network to a SCIF behind an air-gap.

Ground station at dusk with satellite dishes and radomes

[ Runtime ]

Runtime manages the model on your hardware.

Probes the box, picks the engine, tiers memory across what you have, and serves an OpenAI-compatible API. One install. Any hardware.

Tiered memory orchestration

Model weights and cache move across the memory on your machine, whether that is GPU, CPU, or disk. Large models run on hardware that would not normally hold them.

Picks the engine for you

Runtime wraps llama.cpp, vLLM, TensorRT-LLM, and whatever ships next. You pick the model. Runtime picks the engine. When a faster one arrives, you inherit it without rewriting a single line.

OpenAI-compatible API

Drop-in for the OpenAI endpoint. Point existing software at a local URL instead of api.openai.com. Embeddings, classification, extraction, retrieval, tool-calls, and yes chat. The model runs where the data is.

s88 serve --model Llama-3-70B-Q4_K_M
INITIALIZING

Llama-3-70B-Q4_K_M

GGUF Q4_K_M 70B params
Detected

Backend Selection

Auto
llama.cpp vLLM TensorRT-LLM Triton

Memory Hierarchy

PASS

VRAM (Tier 1)

16.8 / 24 GB

RAM (Tier 2)

42.3 / 64 GB

SSD Cache (Tier 3)

128 / 512 GB

Serving

localhost:8088/v1/chat/completions

Throughput

7.8 tok/s

Latency

118 ms

OOM Events

0

Uptime

0s

Sector88 Hub
Live

Nodes

4

Serving

3

Fleet Uptime

99.9%

OOM Events

0

Active Deployments

ground-station-08

Svalbard, Norway
Llama-3-70B llama.cpp
VRAM 16.8/24 tok/s 7.8 22d up

ops-center-03

Edwards AFB, CA
Mistral-7B vLLM
VRAM 5.2/16 tok/s 24.1 8d up

rig-platform-11

North Sea, Offshore
Llama-3-8B llama.cpp
VRAM 6.1/8 tok/s 18.6 45d up

datacenter-sg-02

Singapore, APAC Warming
Qwen2-72B TensorRT-LLM
VRAM -- tok/s -- 0s up

Activity

2m agoModel Llama-3-70B serving on ground-station-08
5m agoPreflight passed on datacenter-sg-02. Loading Qwen2-72B.
18m agoTier swap on rig-platform-11. 2 layers RAM → VRAM.

[ Hub ]

Hub operates the fleet from one place.

One control plane for every Runtime. Deploy, monitor, and manage across your entire fleet from a single interface. No SSH. No spreadsheets.

Live fleet view

Every node, every model, every region. GPU, memory, throughput, latency, and power, refreshed every second. Thirty days of history by default.

Deploy, hot-swap, rollback

Push a model to one machine or the whole fleet. Canary nodes, health checks, automatic rollback on failure. No SSH into individual machines.

Every action logged, nothing stored

Every deploy, rollback, and policy change logged against the user who did it. Prompt and response content is never captured. Exports for your security team, not ours.

[ Engineers ]

Engineers who come to your site.

When a site needs hands on, our forward-deployed engineers audit the hardware, install the platform, benchmark on site, and harden it for production. We embed, ship, and leave when it runs.

Audit and install

Remote or on-site review of the hardware, network, and constraints. The deployment plan is written and signed off before anything is installed.

Benchmarks on your hardware

Throughput, latency, and cost measured on your actual hardware. Exportable scorecards. Production-grade proof.

Hardened for your environment

Air-gapped, classified, regulated. Supervisor restart, secrets management, and network posture locked in for your security regime before we leave.

Sector88 Hub
ENG-2847

Svalbard Deployment

In Progress
Site: Svalbard, Norway Hardware: Jetson AGX Orin 64GB Network: Disconnected

Deployment Progress

Phase 1 of 5

Audit

Install

Benchmark

Harden

Live

Hardware Audit

Phase 1
Hardware Detection Scanning...
Memory Probe Probing...
Network Policy Checking...

[ Capabilities ]

What the platform does.

Single-node and developer use is open. Fleet operations, identity, air-gapped postures, and forward-deployed install are scoped per deployment with our team.

Talk to the team

Fig 1.1

Offline and air-gapped by default

Offline and air-gapped

Zero outbound calls. No license pings. No phone-home. Install over any medium, run on an empty network, and keep running when the satcom link drops.

Fig 1.2

Fleet control plane

Fleet control plane

Deploy, monitor, hot-swap, and roll back every node from one place. Canary to the fleet, rollback on failure. No SSH into individual machines.

Fig 1.3

Regulated and forward-deployed

Built for regulated environments

ITAR-aware. Deployable into classified facilities and sovereign postures. Engineers install and harden on site, inside your perimeter, to your security regime.

Fig 1.4

OpenAI-compatible API

OpenAI-compatible API

Drop-in for the OpenAI endpoint. Point existing software at a local URL instead of api.openai.com. Embeddings, classification, extraction, retrieval, tool-calls, and chat.

Fig 1.5

Identity and audit

Identity and audit

SAML and OIDC out of the box. Hub roles map to your IdP groups. Every action is logged. Prompt and response content is never collected.

Fig 1.6

Tiered memory orchestration

Tiered memory orchestration

Weights and cache move across the memory you have, whether that is GPU, CPU, or disk. Large models run on hardware that would not normally hold them.

Hardware Agnostic

Any GPU, any backend, any model, anywhere.

Hardware Platforms

NVIDIA CUDA
Popular
AMD ROCm
Intel Gaudi / Xeon
Google TPU
Qualcomm AI
Apple Silicon
CPU Servers

Inference Backends

PyTorch Supported
Native inference
vLLM Supported
PagedAttention optimization
llama.cpp Supported
GGUF models, CPU/GPU
TensorRT-LLM Roadmap
NVIDIA optimization
Triton Roadmap
NVIDIA inference server
Ollama Roadmap
Developer tooling

Run it on your own hardware.

Bring in our forward-deployed engineers, or install it yourself. Either way it runs on your hardware, in your network, on your terms.