In previous posts, we explored BYOA deployments, AI job scheduling, and cost control and insights. In this blog, we focus on the performance accelerators and scalability layers Omnistrate offers out of the box—purpose-built for AI workloads that demand fast startup, efficient resource usage, and intelligent scaling.

Let’s dive into four features that can supercharge your AI deployment strategy.

🧠 GPU with CUDA Support

Modern AI models—especially those used in deep learning, training, or inference—rely heavily on GPUs (Graphics Processing Units) for their massive parallel compute capabilities.

But it's not just about having GPUs—it's about making them programmable, and that’s where CUDA comes in.

CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform and API that enables developers to harness the full power of NVIDIA GPUs. It allows you to write highly optimized compute kernels for training and inference at scale.

What Omnistrate does:

Omnistrate automatically provisions and manages GPU-backed infrastructure with CUDA pre-installed and validated—so your models can hit the ground running without manual configuration.

⚡ Turbocharged Model Startup with SOCI

Model startup time can be a major bottleneck—especially when using large container images. Waiting for full image downloads before container launch slows productivity and consumes unnecessary bandwidth.

SOCI (Seekable OCI) is an open-source technology (developed by AWS) that speeds up container startup by allowing containers to start running before the entire image is downloaded. It uses a lazy-loading mechanism so only the necessary parts of an image are fetched on demand.

What Omnistrate does:

Omnistrate supports SOCI-enabled container runtimes, enabling near-instant model startup times for inference and training jobs. This helps dramatically reduce cold start latency for your GPU-powered workloads.

🧩 MIG Support for Resource-Efficient GPU Usage

AI teams often over-provision full GPUs for workloads that don’t need the entire card—which leads to underutilization and wasted spend.

MIG (Multi-Instance GPU) is an NVIDIA technology that allows a single A100 or H100 GPU to be split into multiple isolated instances. Each instance acts like a dedicated GPU, enabling better GPU utilization across workloads.

What Omnistrate does:

Omnistrate supports MIG configurations out of the box, letting you define instance sizes that match the needs of each tenant or job. This gives you fine-grained control over GPU allocation—ideal for multi-tenant AI services.

⚙️ Intelligent Autoscaling with KEDA

As your AI workloads vary—whether it's real-time inference or batch training—you need infrastructure that scales elastically with demand.

KEDA (Kubernetes-based Event-Driven Autoscaling) is an open-source project that extends Kubernetes autoscaling. It lets you scale containers based on event sources like message queues, HTTP requests, or custom metrics—not just CPU and memory.

What Omnistrate does:

We integrate deeply with KEDA to allow event-driven scaling for inference pipelines, job queues, and background workers. It’s ideal for AI services that need to scale up during spikes and scale down to zero when idle—saving cost without compromising performance.

🔄 All Together: Smarter AI Infrastructure

These features—CUDA, SOCI, MIG, and KEDA—form a powerful toolkit for anyone deploying high-performance AI infrastructure:

• CUDA: GPU compute at scale
• SOCI: Instant container startup
• MIG: GPU resource partitioning
• KEDA: Event-driven autoscaling

With Omnistrate, you get all of this without having to wire it together manually. Whether you're running in your own account, your customer’s cloud, or a hybrid of both—Omnistrate abstracts the complexity, so your teams can focus on AI and application, not infrastructure.

💡 Ready to accelerate your AI workloads?

Let us show you how easy it is to build from zero to production with the power of Omnistrate. 👉 Schedule a demo or visit docs to learn more.