Get 9x more throughput from the same GPUs

The first NVIDIA-authorized Linux stack — pre-integrated, de-risked, and production-ready in minutes

The real cost of AI infrastructure isn't the initial install — it's everything that comes after. Driver conflicts during scaling. CUDA mismatches when you add new GPU types to the fleet. Framework regressions after an update that passed QA on one node but breaks on fifty others. Orchestrating hundreds of GPU nodes into a reliable, reproducible environment is where teams burn weeks and where expensive hardware sits idle.

Through our partnership with NVIDIA, Rocky Linux from CIQ (RLC) became the first distribution authorized to deliver NVIDIA's complete AI and networking software stack — CUDA Toolkit, DOCA OFED, GPU drivers, PyTorch — pre-integrated and production-ready.

It just works. Nothing to debug, nothing to change, nothing to edit. Boot and run.

In validated testing, RLC-AI delivers 9x higher token throughput for text inference compared to Ubuntu, and up to 40% higher throughput for image inference.¹

This isn't a marginal tweak — it's getting more work out of every GPU you've already paid for. For organizations spending millions on GPU infrastructure, a 9x throughput improvement translates directly to cost savings or capacity gains without buying additional hardware.

The performance gains come from a combination of AI-specific kernel-level and userspace optimizations — including memory management, I/O scheduling, CPU governor settings, and NUMA optimizations engineered for AI workload patterns — along with precompiled PyTorch tuned for NVIDIA hardware, pre-integrated, the latest upstream stable kernel (eliminating the 12-18 month lag of traditional enterprise kernels), and a fully validated stack where every component from kernel to driver to framework is built and tested to work together.

No Frankenstein builds. No version roulette. No crossing your fingers that this particular combination of kernel, driver, and CUDA won't explode.

RLC-AI, our AI-workload optimized variant of RLC, ships with everything validated together.

Same stack your first GPU gets is the same stack your thousandth GPU gets.

We benchmarked RLC-AI against a standard Rocky Linux manual setup. Clean install with one of our experienced engineers.

Manual setup: about 30-60 minutes

• Driver installation
• CUDA configuration
• Framework setup
• Model loading

RLC-AI: less than 5 minutes

• Driver/CUDA: Already there
• PyTorch: Already there
• Deploy and run inference

That's a dramatic reduction in time-to-first-inference. But installation speed is just the beginning.

For production workloads, that means:

• 9x more inference throughput
• 9x better return on your hardware investment
• Zero time spent chasing performance regressions

Get RLC in your cloud marketplace — available on AWS, Azure, GCP, and Oracle Cloud.

Why this actually eliminates risk

1. Validated compatibility. Every component (kernel, drivers, CUDA, PyTorch) is tested together. No version conflicts. No surprises at 3am.
2. Reproducible deployments. Your first GPU and your thousandth GPU get identical configurations. Same drivers. Same CUDA. Same results. Every time.
3. Pre-solved integration problems. Secure Boot handling. Kernel module signing. Driver persistence across reboots.
4. Production-ready from boot. No research phase. No testing matrix. No custom automation scripts that break with every driver release. Boot, deploy, run.

The hard problems are solved once, correctly, by the team that helped build CentOS and runs national laboratory HPC systems.

Strategic advantages beyond faster setup

One image, every configuration: Whether you're deploying H100 training clusters, A100 inference servers, or L40S development nodes, RLC-AI provides a single validated stack. No bespoke images per GPU type. No environment-specific debugging. One configuration your infrastructure team actually maintains.

Hardware future-proofing: When NVIDIA launches new GPU architectures, RLC-AI provides built-in-support, with the latest drivers and upstream stable kernel support. This means your team can take advantage of new hardware immediately, without waiting to respin images, update automation, or validate complex configurations.

Container-speed, bare-metal performance: Organizations often choose VMs or containers for faster provisioning, sacrificing GPU performance to virtualization overhead. RLC-AI eliminates that tradeoff: pre-integrated images provision bare metal in under 5 minutes, delivering full hardware performance without the wait.

Who this is built for

HPC and research organizations: Scientific computing teams running training, inference, and simulation workloads across diverse accelerators that need cutting-edge performance on a stable, secure, validated foundation.

Enterprise Linux shops: Organizations with deep RHEL/Rocky expertise and infrastructure investment who need solid GPU and accelerator support without abandoning their operational model or retraining staff.

Sovereign AI deployments: Enterprises running sensitive workloads on-premises or in controlled environments, requiring closed-model performance with enterprise-grade support and security posture.

GPU fleet operators: Organizations running mixed-generation hardware (A100s alongside H100s) who need a single validated stack that maximizes performance across their entire investment.

Production AI at scale: Teams managing 500+ AI nodes where automation maintenance, error remediation, and deployment reliability directly impact revenue and operational costs.

Time-critical deployments: Financial trading, autonomous systems, and competitive product launches where days-to-production impacts market position.

What RLC-AI delivers

RLC-AI offers a distinct combination of benefits: deployment in minutes, 2% error rate, and consistent images across AWS, Azure, GCP, and Oracle Cloud. For teams evaluating their options, these benchmarks provide a clear baseline for comparison.

The authorization from NVIDIA validates that AI infrastructure should be invisible. Teams shouldn’t spend time debugging driver compatibility, CUDA versions, or framework conflicts.

With RLC-AI, that's now a reality.

Organizations using RLC-AI can deploy new AI capabilities in minutes instead of an hour per environment, support new GPU architectures on Day 1, scale infrastructure confidently, and focus engineering talent on AI innovation instead of infrastructure maintenance. This signals that AI infrastructure is maturing from "figure it out yourself" to "enterprise-ready and validated."

Request a technical consultation to see how RLC fits your infrastructure, or deploy now from your preferred cloud marketplace.

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¹ Based on MLPerf informed benchmarks on an NVIDIA A30 with virtualized OS, and Hugging Face Hub