Axiom-X - Enterprise AI Built for Speed, Control, and Efficiency

From copilots and search to real-time decision-making, organizations need predictable latency, cost-effective scale, and the freedom to deploy on their own terms. Axiom-X is built for exactly that.

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Why Axiom-X

Modern AI inference infrastructure is shaped by several interacting constraints, not a single bottleneck. Performance depends on the balance between compute, memory bandwidth, and interconnect throughput. When any one of these falls behind, overall efficiency drops, even if the other components are highly capable. As AI workloads scale and become more distributed, maintaining this balance only becomes more critical.

Meeting these challenges calls for a system-level approach to infrastructure design. Rather than optimizing components in isolation, modern architectures focus on coordinating compute resources, memory subsystems, and network fabrics. When these elements are aligned, systems can deliver higher throughput, make better use of resources, and scale AI workloads reliably across diverse environments.

Your AI. Your Data. Your Infrastructure.

Building the future of AI on sovereignty, domesticity, and enterprise strength

Deploy Entirely On-Premises
Deploy Entirely On-Premises

Inside your data centre, under your governance

Open standards architecture
Open standards architecture

Leveraging Ultra Ethernet, RISC-V cores, and other open standards to ensure broad ecosystem compatibility and easy integration with existing infrastructure.

Data Sovereignty, Guaranteed
Data Sovereignty, Guaranteed

No model weights or traffic ever leave your environment.

Future-Proof by Design
Future-Proof by Design

No lock-in to proprietary ecosystems or vendor roadmaps

Inference at Scale, Without Compromise

Inference isn't "one model, one server." It's always-on, multi-step services operating under tight latency and cost constraints. Axiom-X is designed to deliver on the metrics that matter most in production.

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Predictable QoS (Consistency at Scale)

Not all inference is equal—SLA matters. Axiom-X is built to deliver stable tail latency and consistent throughput under bursty, multi-user traffic.

Memory-bandwidth optimized (Feeds the model, not the bottleneck)

Built for memory-hungry models, with high bandwidth and efficient data movement to keep pipelines fed and token throughput high.

Ultra scalable

Designed for spiky, multi-tenant services like LLM APIs and recommendation systems—scales from single-node installs to large fleets

Cost efficient

Lowers cost per inference by maximizing useful work and minimizing wasted cycles across real production patterns.

Power efficient

Performance per watt is the new benchmark—power-aware design reduces operating costs and carbon footprint without sacrificing throughput.

Scalable Architecture

Right-sized for every deployment — R&D lab to hyperscale data centre

Scalable Architecture
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PCIe form factor · plug-and-play AI acceleration for existing data centre and edge servers

Axiom-XG — Low-Cost Inference Platform

  • PCIe cards and servers for AI inference acceleration
  • Compatible with third-party servers
  • Serving small R&D centres and offline processing
Axiom-XG — Low-Cost Inference Platform
Disaggregated LLM inference · ~10× throughput at a fraction of GPU cluster cost

Axiom-XS — Ultra-Fast Inference at Scale

  • Disaggregated LLM inference platform
  • Accelerators with GDDR memory for attention KV cache management
  • Chip with ultra-fast memory for feed-forward layers
  • ~10x single-user performance
  • ~10× reduction in token price at scale
  • Complements existing GPU deployments, extending LLM inference capacity without replacing current infrastructure
Axiom-XS  — Ultra-Fast Inference at Scale

Next-AI Symphony — Orchestration Platform

    Full-system orchestration

    Manages compute, networking, memory, and routing as one unified system.

    OpenAI-compatible API

    Drop-in replacement, zero application changes

    Disaggregated Serving

    Distributes LLM sub-graphs across the right hardware for peak throughput.

    Dynamic Load Balancing

    Maximises utilisation across heterogeneous nodes

    Multi-Vendor Scheduling

    Schedule Evollabs and third-party GPUs as one unified cluster.

    Built-In Observability

    Per-request visibility into latency, throughput, and cost.

    KV Cache management

    Intelligently routes between GPU and host memory tiers.

Ready to Transform Your AI?

Join the waitlist for early access to Axiom-X and be among the first to experience the next generation of AI computing.