Private AI infrastructure for your business

A complete self-hosted AI stack.

Every DiscreteStack build is a complete self-hosted AI stack for its hardware class, combining a selected open-weight model, optimized inference runtime, OpenAI- and Anthropic-compatible APIs, admission queue for multi-user serving and enterprise controls. It runs on-premises, air-gapped or on private infrastructure you control — without sending inference to a third-party AI provider.

Frontier AI on a single server.

DiscreteStack uses hardware-native compilation and patent-pending inference technology to run frontier-class open-weight AI on a single server. Each build is optimized for the specific GPU architecture underneath it, maximizing memory efficiency, throughput and serving capacity. This makes production-scale AI practical without the complexity and infrastructure footprint of a multi-node AI cluster.

Model evaluation and upgrades.

We continuously evaluate new open-weight model releases, rerunning public benchmarks to verify published results and running our own evaluations. A single benchmark run can involve hundreds of real-world tasks and cost thousands of dollars in inference. We absorb that model scouting, benchmarking and validation work, upgrading each tier only when a new model proves better for its hardware class.

Feature	Claude Opus 5 via API	DiscreteStack Max	DiscreteStack Compact
Intelligence	Latest frontier — Opus 5	Opus 4.8-class	Sonnet 4.6-class
Cost per task	1.0×	~0.33×	~0.11×
Billing	Per-token API pricing	Unlimited tokens. No per-token billing	Unlimited tokens. No per-token billing
Deployment	Anthropic-hosted cloud API	Self-hosted on infrastructure you control	Self-hosted on infrastructure you control
Data control	Processed by external provider	Private / on-premise	Private / on-premise
Aggregate speed	—	950 t/s	750 t/s
Per-stream decode	40 - 60 tps	65 tps	25 tps
Model	Opus 5	Kimi K2.7	Ornith 1.0
Hardware	Provider infrastructure	GB200 NVL4	4× RTX PRO 6000

Transparency

Open weights you can audit. Open inference stacks you can inspect. No black box between you and the technology you build your business on. Every model versioned, every component replaceable — if you can't see how it works, you can't trust it in production.

Control

Infrastructure you control — owned or leased, in jurisdictions you choose. Air-gapped when your business demands it. Rented hardware in a private cloud when it doesn't. No vendor lock-in, no dependency on a provider whose economics require your data on their platform.

Predictability

Flat-rate license per server per year. No token metering. No usage-based billing. A cost line your CFO can sign for three years without a variance clause. Your AI bill doesn't grow when your teams actually start using it.

AI for Software Engineering

• AI Pair Programming

  Write, test, debug alongside your team

• Autonomous Tasks

  Delegate entire tickets end-to-end

• 30× Code Output

  150 lines/day becomes 5,000

AI for Finance & Operations

• Natural Language Queries

  Ask any question about your data

• Automated Reports

  Weekly, monthly, and ad-hoc

• Rapid Data Entry

  Days of work in minutes

AI for Project Management

• Inbox Triage

  Prioritize, summarize, draft replies

• Task & Calendar Management

  Across Jira, Asana, email, calendar

• Hours of Repetitive Work

  Handled before your day starts

Most AI infrastructure assumes cluster-scale — dozens of GPUs, distributed across racks, requiring colocation partners you don't control. DiscreteStack fits state-of-the-art AI intelligence on a single server — through hardware-native compilation, predictive admission control, and model-hardware co-optimization that go far beyond standard prefix caching, decode scheduling, and request batching.

The result: 13× more throughput from the same silicon. Once AI runs on one machine, you choose where that machine lives — in your building, air-gapped, or on rented hardware in a private cloud. That's the engineering breakthrough that makes everything else on this page possible — the flat rate, the control, the day-one readiness.

RAG indexing

Batch inference

Report
generation

Overnight agents

Hardware-Native Builds

Every deployment is compiled and optimized for your specific GPU topology.

Intelligent Execution Runtime

A scheduling engine that maximizes GPU utilization across concurrent workloads.

Data Sovereignty

No data leaves your perimeter. Air-gapped deployment available. No exposure to the US CLOUD Act — jurisdiction follows the company, not the data centre.

Governance & Audit

Identity management, per-user audit trails, usage intelligence. Full visibility into who uses what, when, and how.

EU AI Act Ready

Full enforcement begins August 2026. When AI runs on infrastructure you control, you own the compliance posture — not your vendor.

Feature	DiscreteStack	OpenAI / Anthropic	DIY	Hyperscalers (Azure/AWS)
Model Intelligence	Frontier − 3 months	Baseline	Mixed	Baseline
Cross-system Integration		Vendor specific	Partially	Vendor specific
Predictability	Yearly Contract	Vendor Roadmap	Self-managed	Vendor Roadmap
Operational Complexity	Low	Mid	High	Mid
US CLOUD Act exposure	None (EU-incorporated)	Subject	None	Subject
EU AI Act readiness	Full (August 2026 ready)	Partial	Self-managed	Partial
Cost model	Flat-rate license	Per-token	CapEx + engineering team	Per-token