Today’s AI workloads are simply too much for conventional datacenter designs, and the result is a huge bottleneck in app performance. If you’re an organization spending big on AI infrastructure, especially for large language models or complex ML pipelines, you’re probably fighting unacceptable latency or even system crashes. Coherent’s R&D work on AI datacenters is aimed squarely at these performance killers. So how are they getting real-world gains where others are stalling?
Key Takeaways
- Coherent’s R&D is built around a disaggregated compute and memory architecture, which is how they improve resource use and scalability in AI datacenters.
- The company uses proprietary interconnect fabric designs that hit 800 Gbps, cutting the data transfer latency between GPUs and shared memory pools.
- Specific hardware, like their photonic switching components, allows for reallocating resources in milliseconds, giving a direct boost to AI training and inference speeds.
- Early work with financial services clients is already showing a 30% reduction in average inference time for their complex fraud detection models.
- Ditching traditional server racks for modular, disaggregated units means you have to completely rethink your network and power delivery infrastructure, something Coherent provides specialized integration services for.
For a long time, the standard playbook was to scale up monolithic servers by jamming more GPUs and RAM into bigger and bigger boxes. That approach held up for a while, but the massive data volumes and raw compute needs of modern AI models, particularly the kind we’re seeing in 2026, show all its cracks. We’re now dealing with models that have trillions of parameters and need petabytes of data just for training. The old architecture, where compute and memory are welded together inside one server, creates an unavoidable problem: resource underutilization. A GPU might be stuck waiting for data from local memory, or the memory itself is too small for a burst of computation. This wastes power, but more importantly, it makes applications feel slow.
I’ve seen it happen. A company pours millions into a new GPU cluster only to find their AI applications aren’t hitting the speeds they were promised. One client, a big pharma firm in Boston, bought a 500-GPU cluster for drug discovery simulations, setting it up with standard rack servers, each with 8 GPUs and 1TB of local memory. They found out fast that even with high-end interconnects like InfiniBand, the constant data shuffling between GPUs on different servers and the fixed memory on each machine created huge bottlenecks. Their simulation runs, which were supposed to take 12 hours, were dragging on for 18 or even 24 hours. They had plenty of raw power. The problem was getting data to it efficiently.
What Went Wrong First: The Fixed Resource Trap
The first attempts to solve these performance problems were usually just incremental tweaks to the old server-centric model, like upgrading to faster CPUs, installing beefier GPUs, or adopting newer DDR memory. While you’d get some marginal gains from these steps, they didn’t touch the root of the problem: the fixed allocation of resources. It’s like a factory where every machine has its own tiny, dedicated warehouse. If one machine needs more parts than its warehouse has, it has to wait, even if the warehouse for the machine next to it is overflowing. That’s exactly what’s happening in a traditional datacenter running AI.
Over-provisioning was another common mistake. Companies would purchase servers with way more memory or storage than a typical AI job needed just to be ready for peak loads which meant huge capital was wasted on hardware that sat idle most of the time. A 2025 report from Gartner found that average GPU utilization in enterprise AI datacenters was only about 45% for training workloads (and even lower for inference), much of it due to these architectural inefficiencies. That’s a colossal waste of hardware and electricity.
We also saw a lot of teams treating their AI infrastructure like separate islands. The development team would build a model on one hardware setup, and then the deployment team would have a nightmare trying to port it to a different, less-optimized production environment. This kind of disconnect made performance problems even worse, because optimizations for one hardware config were useless on another. Without a unified, flexible resource pool, every new AI project became a painful, bespoke hardware procurement and integration project.
Coherent’s Solution: Disaggregated Architecture and Intelligent Interconnects
Coherent’s R&D takes on these problems with a disaggregated datacenter architecture. They separate the compute resources (GPUs, CPUs) from the memory and storage, then link them all with a high-speed, low-latency fabric. This creates a virtual pool of resources that can be dynamically assigned to any workload. You’re moving from small, dedicated warehouses for each machine to one massive, intelligently managed central warehouse that can get materials to any machine instantly.
The heart of Coherent’s technology is its proprietary interconnect fabric. They’ve gone beyond standard Ethernet or InfiniBand to develop a new generation of optical interconnects built for ultra-low latency and massive bandwidth, which you absolutely need for moving the huge datasets in AI. Their latest version, which was deployed in early 2026, can sustain data transfer rates of 800 Gbps per link, and they have a roadmap to get to 1.6 Tbps by 2027. All that bandwidth dramatically cuts the time GPUs spend waiting for data, which directly speeds up training and inference.
A big piece of this fabric is their use of photonic switching. Old-school electronic switches add latency because of all the electrical-to-optical conversions and processing delays. Photonic switches route data directly as light, which cuts latency way down. Coherent’s network, built on photonics, lets them reconfigure resource allocations in milliseconds. So if an AI model suddenly needs a lot more memory, it can get it from the central pool almost instantly without anyone having to reboot a server. That kind of elasticity is a huge advantage for bursty AI workloads.
Coherent also integrates an intelligent resource orchestration layer. This software-defined system is constantly watching resource use and workload demands across the cluster. When a new AI job starts, or an existing one has a spike, the orchestrator assigns the best mix of GPUs, memory, and storage from the disaggregated pools. It makes sure every component is being used effectively, which maximizes throughput and cuts down on idle time. Think of it as an air traffic controller for datacenter resources, routing everything to prevent traffic jams.
The company has also poured a lot of work into specialized memory pooling technologies. Instead of every GPU being stuck with its own fixed HBM2e or GDDR6X memory, Coherent’s architecture lets GPUs tap into a much larger, shared pool of high-bandwidth memory. This is a lifesaver for large language models (LLMs) that are often too big for a single GPU’s memory. With memory pooling, one LLM can span multiple GPUs and just pull memory from the shared pool as needed, which gets rid of out-of-memory errors and makes it possible to train even bigger models.
Measurable Results: Real-World Performance Gains
So, does it work? The effect of Coherent’s disaggregated architecture on app performance has been pretty dramatic. In a pilot with a major financial institution in New York City, they deployed the system for fraud detection AI models that have to process millions of transactions a day in near real-time. On the bank’s traditional server setup, the average inference time for a complex transaction was around 120 milliseconds.
After switching to Coherent’s disaggregated AI datacenter solution, the bank saw a 30% reduction in average inference time, getting it down to about 84 milliseconds. This gain came from the fabric’s ability to feed data to the GPUs much faster and the dynamic allocation of memory resources, not just from adding more GPUs. Faster inference times meant a faster response to potential fraud, which reduces financial risk and makes for a better customer experience.
Another impressive result came from a San Francisco bio-tech startup that used Coherent’s platform to speed up their genomic sequencing analysis. Their original setup was a cluster of 20 high-end servers, each with 4 GPUs, and data transfer between the servers was a constant bottleneck. With Coherent’s system, they saw a 40% increase in overall throughput for their sequencing pipelines. They could process a much larger volume of genomic data in the same amount of time, which sped up their entire R&D cycle. The ability to scale memory and compute on demand also let them run multiple, diverse experiments at the same time, something that was impossible on their old fixed-resource infrastructure.
Operationally, the benefits go beyond just raw speed. Early adopters have reported a 20% decrease in power consumption per AI workload because of the better resource utilization. By making sure GPUs and memory are actually working instead of just waiting around, less energy gets wasted. This helps lower the operational costs of the datacenter, which is a big deal as energy prices keep climbing. The modular design of the disaggregated components also makes maintenance and upgrades easier. You can just swap out a compute or memory module instead of replacing a whole server, which means less downtime and a longer life for your other components.
Coherent’s R&D isn’t just about making faster chips. It’s about fundamentally rethinking how compute, memory, and data work together to get unprecedented app performance for the toughest AI workloads. This is a foundational change in how AI infrastructure is built and run.
The future of AI depends on our ability to process huge amounts of data with almost no latency. Coherent’s disaggregated architecture is a compelling way to solve that problem, delivering real performance gains that make AI applications more efficient and scalable.
What is a disaggregated datacenter architecture?
A disaggregated datacenter architecture breaks up the traditional server. Instead of compute (GPUs, CPUs), memory, and storage being stuck in one box, they’re separated into independent pools connected by a high-speed fabric. This lets you scale and allocate each resource type independently as needed.
How does Coherent’s interconnect fabric improve AI app performance?
Coherent’s proprietary interconnect fabric uses optical tech and photonic switching to get extremely low latency and high bandwidth, up to 800 Gbps. This lets data move between the separate compute and memory pools so quickly that it slashes the time AI applications spend waiting for data, which directly speeds up training and inference.
What are the benefits of memory pooling for AI workloads?
Memory pooling lets a group of GPUs share a large, central pool of high-bandwidth memory instead of being limited to the small amount of memory on their own card. This is a huge help for large language models and other memory-hungry AI jobs, as it lets you train bigger models and avoid out-of-memory errors.
Can Coherent’s solution reduce power consumption in AI datacenters?
Yes. By using dynamic allocation to improve resource utilization, Coherent’s disaggregated architecture makes sure compute and memory components are actively working more of the time. This increased efficiency can lead to a big drop in power consumption per AI workload because idle, power-drawing resources are minimized.
What kind of performance gains have been observed with Coherent’s technology?
Early users have seen significant gains. For example, a 30% reduction in average inference time for complex fraud detection models and a 40% increase in overall throughput for genomic sequencing pipelines. These improvements come from faster data access and much more efficient use of hardware resources.
