The Secret to Faster AI Isn’t More GPUs, It’s Smarter Networking

AI is redefining what’s possible across industries including healthcare, finance, manufacturing, and retail. But with promising potential, it also brings massive infrastructure demands.

Organizations worldwide are investing in GPUs at an unprecedented scale to accelerate AI training and inference. By 2028, Gartner projects generative AI IT spend will surpass $1 trillion. Hyperion Research forecasts the overall HPC market spend to exceed $100 billion in that same time. Yet despite investing in cutting-edge accelerators, many CIOs continue to see idling GPUs, with utilization hovering at 35% or lower. This not only results in underperformance but also wasted energy and inflated costs.

While many AI projects stall, it’s not because they lack GPUs or compute power, but because the network can’t keep up, requiring a new approach to designing for AI at scale.

The Hidden Cost of Network Bottlenecks

When networks can’t feed data fast enough to keep GPUs consistently busy, organizations experience several critical impacts:

• Underutilized GPUs and CPUs due to bottlenecked data transfers: GPUs are designed for massively parallel computation, but they can only process data as fast as it’s delivered. If the network fabric can’t keep up, GPUs sit idle waiting for data instead of crunching numbers. CPUs may also stall since they’re coordinating tasks and moving data through the pipeline, resulting in low utilization despite the availability of expensive hardware.
• Inconsistent inference performance from an inefficient network: Network inefficiencies create uneven data flows, causing GPUs to fluctuate between full speed and idle states. This produces unpredictable inference performance that can cripple AI applications in production.
• Longer training cycles, delaying time-to-market: Training AI models requires moving massive datasets across servers, GPUs, and storage. Network bottlenecks throttle this process, so GPUs spend less time training and more time waiting. This directly slows product development and deployment schedules.
• Escalating power and operational costs: Even when idle, GPUs and the surrounding infrastructure still consume significant power. If GPUs are underutilized due to network inefficiencies, organizations pay for high power usage without getting proportional performance. Operational costs escalate because facilities must support peak power and cooling loads, even though compute throughput is artificially constrained.

Enterprises can continue to pour money into more GPUs, but without the right network enhancements, they’ll only compound these bottlenecks and inefficiencies.

Network as Accelerator: A Paradigm Shift

The solution requires rethinking network architecture entirely. Introducing a model that utilizes the network as an accelerator flips traditional thinking about HPC and AI performance to unlock new capabilities.

Instead of focusing primarily on adding more compute via GPUs and CPUs, the “network as the accelerator” approach treats the interconnect fabric as a performance multiplier. As a result, the network can better support high-density compute and accelerate ROI by eliminating bottlenecks, scaling to meet compute demands, and right-sizing hardware investments. By enabling greater compute without slowdowns, organizations can run bigger workloads in less space, get results faster, and avoid overspending on extra hardware.

How the ‘Network as accelerator’ Model Works

So, how does this model work so organizations can transform their network from being a passive data mover into an active enabler of compute and start realizing the benefits? It delivers four key capabilities that traditional networks lack:

• Guaranteed delivery at the hardware level: Traditional networks burden CPUs and GPUs with packet tracking, retransmission, and reordering overhead. This consumes compute cycles that could be devoted to training or inference. With a network fabric that guarantees delivery at the hardware level, these tasks are shifted away from the compute nodes, resulting in reduced CPU and GPU overhead, predictable and consistent performance, and scalability that simplifies programming and cluster orchestration.
• Intelligent dynamic routing: Conventional routing relies on fixed or suboptimal paths, which can leave parts of the network underutilized or create bottlenecks where massive data volumes flow simultaneously. Intelligent routing dynamically leverages all available paths to optimize traffic flow. It allows higher throughput with multiple active routes balancing traffic, lower latency via optimal path selection, and improved resiliency as network traffic automatically reroutes around link or node failures. This reduces idle times and keeps GPUs fully fed with data.
• Link-level auto retry: When packets are lost or corrupted, standard networks depend on the compute layer to detect and resend them, which introduces significant latency and interrupts compute flow. A fabric with built-in, link-level auto retry capabilities handles retransmissions inside the network itself. It allows near-transparent reliability as packet loss becomes invisible to compute nodes while reducing latency impact as retries happen locally at the link, not across the entire network stack. It also eliminates the need for complex application-level error handling. Auto-retry capabilities ensure uninterrupted, efficient distributed computation, which is important when scaling across thousands of GPUs.
• In-network computing: While traditional networking fabrics primarily move data, in-network computing enables the network to become a co-processor by performing certain operations directly within the fabric. NVIDIA SHARP is a prime example – it allows reductions to happen on the network switches themselves. This allows accelerated distributed operations, lowers latency because data is aggregated as it traverses the network, and increases efficiency as compute nodes are freed from performing aggregation tasks, leaving more cycles for training and simulation.

Altogether, these capabilities are what make “network-led computing” foundational for scaling next-gen AI and HPC environments. A network-centric approach delivers tangible returns that include higher GPU utilization that eliminates data starvation, faster time-to-insight that reduces training cycles and stabilizes inference performance, improved resource efficiency, and lower total cost of ownership.

Discover True Network Power

AI at scale isn’t just a compute problem – it’s a system-level engineering challenge, with networking at the center of it. Treating the network as an accelerator turns it into a force multiplier for compute, allowing HPC and AI data centers to scale in density without sacrificing performance. It delivers measurable ROI faster by extracting maximum value from existing infrastructure before investing in more silicon.

By eliminating bottlenecks, boosting utilization, and delivering predictable performance, smarter networking enables more productive AI teams, better ROI on GPU infrastructure, and faster time-to-insight, innovation, and market leadership. It allows organizations to discover what their network can truly be and harness the power of AI in new ways.