Build AI without the lock-in: How Ori AI Fabric powers a true multi-vendor ecosystem

Most AI cloud platforms still treat hardware choice as a constraint: you pick one GPU vendor, one storage stack, one set of network interfaces, and then spend months stitching everything together. Ori AI Fabric flips that model. Optionality is one of the first principles in the way we have architected our platform.

This post addresses how Ori AI Fabric is designed for hardware heterogeneity, operating mixed fleets of compute, storage, and networking as a single system through a single control plane. That sounds like a philosophical choice, but it’s really an operational one: the best way to hit performance, cost, and availability targets is to run the right workload on the right silicon and data fabric, without rewiring your stack.

Why multi-silicon compute matters now

As AI adoption grows, the computing landscape is getting more diverse to address a wide range of use cases and workloads. NVIDIA’s Blackwell generation pushes general-purpose performance forward at rack scale by combining next-generation GPU cores, advanced memory bandwidth, and high-speed interconnects to accelerate both training and inference workloads. These next-generation GPUs also enhance NVIDIA Confidential Computing by supporting secure, encrypted model execution while maintaining near-parity performance with unencrypted workloads across all model sizes, including large language models (LLMs). AMD’s Instinct family offers massive HBM capacity and a maturing ROCm software stack. Meanwhile, specialist accelerators from Groq, SambaNova, Cerebras, and Qualcomm target specific inference or memory patterns that GPUs don’t always hit efficiently.

NVIDIA: NVIDIA’s rack-scale GB200 NVL72 connects 36 Grace CPUs and 72 Blackwell GPUs over fifth-generation NVLink so the rack behaves like a single accelerator for trillion-parameter inference. NVIDIA positions GB300 NVL72 for test-time scaling and AI reasoning with further tensor-core and interconnect upgrades.

AMD: On the memory side, AMD Instinct MI300X ships with 192 GB of HBM3 per device, while the MI325X increases that to 256 GB HBM3e, headroom that matters for big-context LLMs and large batch inference, backed by ROCm improvements and growing first-party guidance for serving engines like vLLM.

Groq: Specialized accelerators can be even more surgical. Groq’s LPU compiles models into a statically scheduled, deterministic execution plan and relies on hundreds of megabytes of on-chip SRAM for ultra-low-latency inference at the cost of distributing larger models across many chips.

SambaNova: For memory-bound or MoE workloads, SambaNova combines a reconfigurable dataflow architecture with a three-tier memory system (on-chip SRAM, HBM, and attached DDR), making frequent model/layer switching more efficient than on fixed architectures.

Qualcomm: And if your bottleneck is power and density at inference scale, Qualcomm Cloud AI 100 Ultra offers a compelling option for horizontally scaled, QPS-heavy services where performance-per-watt and performance-per-dollar drive the business case.

At the other extreme, Cerebras WSE-3 puts 900,000 cores and 44 GB of SRAM on a single wafer-scale die with an aggregate 21 PB/s on-chip memory bandwidth, and can cluster to 2,048 systems shifting the calculus for super-sized training and inference workloads.

The takeaway: heterogeneity isn’t a quirk, it’s a response to workload diversity, supply dynamics, and governance needs.

The multi-vendor AI ecosystem goes beyond compute

The fastest GPU will idle if the data plane can’t keep up. In modern AI factories, storage layout and network fabric are as determinative as FLOPs: they decide whether pretraining jobs stream terabytes per hour without stalls, whether inference hits single-digit millisecond tails, and whether multiple teams can share one cluster without noisy neighbors. This is why Ori’s multi-vendor posture extends deliberately into storage and networking.

On storage, the center of gravity is shifting from CPU-centric I/O to GPU-direct data movement. With GPUDirect-style paths and RDMA, data travels between NVMe (or NVMe-over-Fabrics) and GPU memory without bounce buffers, cutting latency and freeing host CPUs for preprocessing. Different vendors optimize different patterns: WEKA’s parallel filesystem couples a POSIX client with RDMA and first-class GPU-direct support for high-throughput training; VAST’s disaggregated, NVMe-oF architecture provides local-flash-like access at rack and pod scale, ideal for mixed training/inference and hot KV-cache reads; DDN’s A³I (Lustre) brings mature metadata behavior and validated reference designs for very large multi-node GPU clusters.

Networking is undergoing a similar bifurcation. Inside racks, NVLink/NVSwitch handle collective ops; across nodes, operators choose InfiniBand or Ethernet with RoCE. InfiniBand’s latest generations deliver ultra-low-tail collectives (with in-network reductions) and strong congestion isolation; AI-tuned Ethernet stacks close the gap with lossless fabrics, host offloads, and richer telemetry while preserving the tooling ops teams already know. At the server edge, high-bandwidth NICs and DPUs offload transport, encryption, storage, and even collective acceleration, stabilizing tail latency under load and returning host cycles to the application.

For platform teams, the takeaway is simple: storage back-ends and fabrics are strategic levers, not fixed constraints.

Ori AI Fabric: Multi-vendor by design, with a unified control plane

Ori AI Fabric natively integrates multiple types of hardware and mixed fleets: NVIDIA and AMD for general-purpose GPUs, Qualcomm and Groq for inference-centric accelerators; WEKA, VAST, and DDN for high-performance storage; NVIDIA Networking, Cisco, Supermicro, HPE, and Dell for servers and fabrics. What teams experience is one surface: a single control plane, one API/CLI/console, and one policy engine that place, scale, observe, and govern workloads across all of the above.

Illustration: Inference endpoints deployment with multi-silicon options

Additionally, Ori’s Bring-Your-Own-Compute (BYOC) support helps you bring your existing fleet into the same control plane that extends across hybrid and sovereign locations.

The result is tremendous operational flexibility. For example, you could pretrain on NVIDIA or AMD, turn to Qualcomm or Groq for low-latency inference, keep hot KV-cache on VAST, stream epochs off WEKA, archive to your object tier on MinIO, all without rewiring apps or duplicating pipelines.

How Ori weaves multi-vendor flexibility across the stack

Uniform cloud experience irrespective of compute hardware: Ori’s cloud services, Virtual Machines, Serverless Kubernetes, Inference Endpoints and Supercomputers all work the same way despite the diversity of compute and storage, abstracting the complexity of handling multi-silicon compute away from users.

Unified observability: Administrators can monitor and manage resources from diverse hardware vendors with a single pane of glass view, across multiple locations.

Data plane integration: Storage integrations with parallel storage providers such as WEKA, VAST, DDN as well as object-storage from MinIO and Ceph

Flexible Networking: Ori AI Fabric natively supports both InfiniBand and Ethernet/RoCE, giving operators the flexibility to choose the optimal network fabric for their workloads. InfiniBand provides ultra-low latency and in-network acceleration for large-scale training and collective operations, while Ethernet/RoCE offers scalability and ease of integration with existing data center networks. Customers can also blend these network fabrics when needed.

Model compatibility: Ori’s model catalog is designed in such a way that the model engine automatically identifies the right hardware choices based on compute and memory capabilities required for the model. Services such as Serverless Kubernetes ,virtualized GPU Instances and Supercomputers provide you a high degree of control on how you want to run your models. The combined end-to-end capabilities help you address a wide variety of use cases.

Multi-tenancy: As a platform operator, your customers and teams have diverse hardware needs, from high-memory GPUs for training to low-latency accelerators for inference. Ori AI Fabric is built for this diversity. Its multi-tenancy architecture lets each tenant run workloads on the hardware best suited to them, NVIDIA, AMD, Qualcomm, Groq, or any mix, within a single unified platform. The result is a secure, efficient, and independent experience across a shared multi-vendor infrastructure that adapts as needs evolve.

Identity, Billing and customer management: Ori AI Fabric’s hardware agnosticism also extends to the administrative side of the platform. You can bring your own identity and access management (IAM), payment provider and customer relationship management (CRM) systems to make sure your cloud platform is interoperable with the rest of your technology ecosystem.

Greater control for your AI cloud

Future-proofing without lock-in. You can adopt the best hardware per workload, swap vendors as needed, and leverage the optimal silicon roadmaps without rewriting deployment flows.

Performance where it matters. Matching workloads to silicon moves the needle: large-scale training on top-tier GPUs, ultra-fast inference on accelerators, storage with millions of IOPS or non-blocking network traffic, you get to choose a best-of-breed solution.

Better economics and supply resilience. Mixing generations and suppliers creates price leverage and mitigates lead-time shocks on the supply side. If you already own GPUs or accelerators, Ori AI Fabric makes it easier for you to aggregate capacity across vendors and make the most of your investments.

Compliance and sovereignty by design. Easier to manage requirements for compliance and sovereignty especially when navigating restrictions on hardware use at different geographies.

Ready to build without lock-in?

If you’re operating (or planning) an AI cloud, whether it is enterprise, sovereign, or public, Ori AI Fabric gives you the freedom to choose any compute, any storage, any fabric, then run it all as one system. Talk to our team to design a pilot tailored to your workload mix and requirements.