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Cornelis Networks designs high-performance data-center networks that connect servers and storage to move large data quickly. Its CN5000 Omni-Path family provides high-speed interconnects that enable demanding workloads, including cognitive AI, in data centers. The company sells hardware and offers professional services and technical support to implement and optimize deployments, differentiating itself with a services-oriented approach and extensive global deployments. Its goal is to provide reliable, scalable infrastructure that accelerates data transfer for data-driven workloads and supports customers' missions.
Industries
Data & Analytics
Hardware
Industrial & Manufacturing
Company Size
201-500
Company Stage
Debt Financing
Total Funding
$130.8M
Headquarters
Radnor Township, Pennsylvania
Founded
2019
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Cornelis and NEC Expand Collaboration to Advance AI and HPC Infrastructure in Japan. August 20, 2026 Press play to listen to this content WAYNE, Pa. and TOKYO, Aug. 20, 2026 - Cornelis and NEC Corporation have announced an expanded collaboration that builds on the companies' successful work together in Europe and will help organizations across Japan and the broader Asia-Pacific region deploy next-generation AI and high-performance computing (HPC) infrastructure using Cornelis high-performance networking solutions. The expanded collaboration builds on NEC's successful evaluation of the Cornelis CN5000 Omni-Path platform and the companies' existing collaboration in Europe, creating a foundation to support enterprise AI, manufacturing, research, and high-performance computing deployments across Japan. As part of the collaboration, NEC also plans to participate in early evaluation activities for the next-generation Cornelis CN6000 platform, helping customers prepare for increasingly complex AI and HPC workloads. "AI infrastructure is placing unprecedented demands on network performance, scalability, and efficiency," said Lisa Spelman, CEO of Cornelis. "NEC's extensive experience delivering enterprise and HPC infrastructure throughout Japan makes them an outstanding partner as we continue giving customers more choice and bringing advanced networking technologies to new markets. Together, we're helping customers build AI and HPC environments that deliver greater performance today while preparing for the next generation of large-scale computing." The collaboration reflects growing adoption of open, high-performance networking alternatives as AI and HPC deployments continue to scale. Following a comprehensive technical evaluation of the CN5000 platform, NEC and Cornelis will continue collaborating to support customer opportunities by leveraging NEC's long-standing relationships with enterprise, manufacturing, academic, and research organizations throughout Japan. NEC's evaluation included extensive MPI communication testing across multiple workloads and communication patterns, validating the capabilities of the CN5000 platform while also identifying optimization techniques that further improve application performance. The joint evaluation demonstrated the value of close engineering collaboration to help customers maximize networking efficiency across large-scale HPC environments. The companies also plan to extend their collaboration to the upcoming Cornelis CN6000 platform, allowing NEC to evaluate next-generation networking technologies as customers prepare for larger AI training, inference, and HPC deployments. "Organizations across Japan are preparing for increasingly complex AI and HPC environments that require higher performance, scalability, and efficiency," said Takeshi Hirose, Senior Director of Compute Department, NEC Corporation. "Our collaboration with Cornelis broadens the high-performance networking technologies available to our customers while allowing us to evaluate next-generation capabilities that support future computing requirements." The collaboration further strengthens Cornelis' growing global ecosystem of infrastructure partners, enabling customers to deploy high-performance networking through trusted server and infrastructure providers while benefiting from continued innovation across the Cornelis product portfolio. As AI clusters continue to scale, Cornelis and NEC will continue working together to evaluate new networking technologies, optimize customer deployments, and support future AI and HPC infrastructure across Japan and beyond. About Cornelis Cornelis delivers high-performance, scale-out networking solutions that accelerate AI and HPC workloads. Built on the powerful Omni-Path architecture, Cornelis technology enables lossless, congestion-free networking that reduces training time, improves inference, and maximizes compute utilization. From foundation model training to complex climate modeling and real-time analytics, Cornelis' solutions power the most demanding workloads across commercial, academic, government and cloud environments. With a focus on performance, scalability, and efficiency, Cornelis helps organizations achieve faster insights and greater return on infrastructure investments. Learn more at cornelis.com. About NEC Corporation The NEC Group leverages technology to create social value and promote a more sustainable world where everyone has the chance to reach their full potential. NEC Corporation was established in 1899. Today, the NEC Group's approximately 110,000 employees utilize world-leading AI, security, and communications technologies to solve the most pressing needs of customers and society. For more information, please visit https://www.nec.com. Deep Origin this month announced that its AI drug discovery framework delivered nearly a 31%... AI models are getting better at a rapid pace. They are now able to reason,... The tech industry is fixated on one main metric, the raw number of GPUs accumulated... Scientists running large simulations can end up with terabytes of data that then has to... Jensen Huang believes NVIDIA's chips are becoming much more than expensive pieces of hardware. As... As hybrid cognition deepens, the boundary between biological and artificial embodiment will begin to blur...
Get more from your 400 Gb/s fabric: lessons from Lenovo's Cornelis CN5000 validation. July 29th, 2026 Paul Stasurak, Cornelis-Lenovo Solutions Key takeaways: * Get more value from a 400 Gb/s fabric investment: Lenovo measured 378-391 Gb/s across tested node pairs, averaging 385 Gb/s - nearly 100% of the nominal line rate. * Accelerate communication-intensive HPC and AI workloads: The validated configuration delivered node-to-node latency as low as approximately 1.1 μs on Lenovo ThinkSystem SC750 V4 servers powered by Intel Xeon 6 processors. * Reduce deployment risk and time spent tuning: Lenovo's LP2474 guide documents the tested firmware, Fabric Manager, cabling, and MPI settings, giving teams a validated starting point instead of a blank page Make every bit of your 400 Gb/s HPC network count. When you invest in a 400 Gb/s fabric, the gap between "installed" and "optimized" is measured in real money - idle CPU cores, longer job queues, and fewer HPC simulations completed per day. A fabric running at 70% of its potential quietly taxes every workload on the cluster. Getting deployment and tuning right the first time is what turns a capital purchase into sustained scientific output. This isn't theory. Lenovo's EveryScale team validated the full deployment in their HPC Benchmarking Centre on ThinkSystem SC750 V4 servers powered by Intel Xeon 6 processors - a real cluster, real cables, and real benchmarks. A fast network can still underperform. A high-performance interconnect is only as fast as its weakest configuration step. A firmware level that's slightly off, a cable on the wrong switch port, or an MPI stack left at defaults can silently cost 10-20% of your performance. And because the loss is invisible without careful benchmarking, most teams never know it's there. The last mile to an efficient HPC cluster. CN5000 is a complete 400 Gb/s end-to-end Omni-Path fabric with credit-based flow control, fine-grained adaptive routing, delivered on an end-to-end network of SuperNICs and Switches built for over 800 million messages per second. But raw capability has knobs. A dual-socket, 128-core node defaults to 208 MPI contexts - not enough to feed every core without context sharing. Cable length dictates which switch ports deliver latency-optimized links. The fabric manager needs the right port and adapter configuration to sweep the fabric cleanly. None of these are defects; they're the settings that decide whether you get spec-sheet performance or something less. From high-speed hardware to a tuned HPC system. Cornelis doesn't hand you hardware and walk away. Together with Lenovo, Cornelis Networks publish experience-based best-practice guides drawn from real deployments - pairing the open Cornelis OPX (Omni-Path Provider) software stack with documented, repeatable tuning. OPX is OpenFabrics-compliant and built on OFI/libfabric, so it runs Open MPI, MPICH, MVAPICH, and NCCL/RCCL with no application rewrites. The tunables that matter - MPI context sharing, bulk transfer service, and SDMA - are spelled out, not left to guesswork. Its goal is simple: help you squeeze every ounce of value from the network you already paid for, so your cluster is not held back by the network. What Lenovo measured on ThinkSystem servers. The proof, straight from the Lenovo benchmarking center: * Bandwidth: 378-391 Gb/s measured across all node pairs (averaging 385 Gb/s) - roughly 96% of the 400 Gb/s line rate. * Latency: as low as ~1.1 microseconds node-to-node, delivering one of the industry's lowest end-to-end latency for cluster networking * Repeatability: every firmware level, command, and tunable is documented against Lenovo's tested "best recipe." The advantages of CN5000 expand beyond what Lenovo specifically tested. CN5000 SuperNICs are engineered for 800M+ messages per second - the currency of MPI collectives and AI communication and the OPX stack (OFI/libfabric) run today's MPI and AI libraries with no application rewrites. Evaluate the deployment, not the spec sheet. When you evaluate an HPC fabric, look past the peak numbers on the spec sheet and ask what happens after the purchase order. Does the vendor publish validated reference architectures and tested firmware "best recipes"? Is the software stack open and compatible with your existing MPI and AI libraries? And are the performance tunables documented and matched to your CPU SKU - since core count, sockets, and cabling all change the right settings? The answers determine how much of that 400 Gb/s you'll actually use. How to get started. Leverage the full guide, written by networking experts. Lenovo's "Installation and Best Practices for Implementing Cornelis CN5000 Omni-Path on Lenovo ThinkSystem Servers" (Lenovo Press, LP2474) walks through the entire deployment step by step. Then talk to the Cornelis team about bringing the same confidence to your own cluster.
What Cornelis Networks heard at Advancing AI 2026, and what Cornelis Networks is building with AMD. July 23rd, 2026 Rob Hays, Vice President of Business Development, Cornelis This week its team joined AMD in San Francisco for Advancing AI 2026. Cornelis Networks came with news of its own: a new reference architecture pairing the Cornelis CN6000 SuperNIC with AMD EPYCTM Venice processors and AMD InstinctTM MI400 series accelerators. It's a blueprint its customers and OEM partners can use to build AMD-based AI systems where the network keeps pace with the compute. Cornelis Networks left with a full week of conversations that confirmed why Cornelis Networks built it. Before I get to those, credit where it belongs. This was AMD's biggest Advancing AI yet, and Dr. Lisa Su and her team laid out a vision of the AI market that is bolder than what most of the industry was projecting even a year ago. Five years into its collaboration with AMD, with more than 25 validated platform configurations behind Cornelis Networks and a joint HPC Center of Excellence in Munich, weeks like this one are why the collaboration works. Cornelis Networks is building toward the same future. Inference is now the main event. The number from the keynote that stuck with me: AMD projected that 2026 is the first year the world is using more AI compute to run models than to train them, roughly 60 percent of global capacity. Token consumption has grown about 160 times in two years. AMD tied much of that growth to the rise of agentic AI, where models reason, retrieve information, and complete multi-step tasks rather than simply generate responses. Here is what that means for anyone planning infrastructure. The systems being designed today will spend most of their lives serving models, and with disaggregated inference, serving no longer happens on one machine. A request gets routed, its context gets built, and its answer gets generated, often across different systems that hand work to each other over the network. When the network slows that hand-off down, the most expensive equipment in the datacenter sits and waits. Customers experience that as higher infrastructure costs, while end users experience it as a delay or poorer application performance. One theme echoed throughout the event was that AI infrastructure can no longer be designed component by component. CPUs, GPUs, networking, memory, and software increasingly have to be treated in one integrated system. It came up in nearly every discussion its team had at the show, and it is exactly the problem its reference architecture exists to solve. Agents are changing what customers ask Cornelis Networks to build. AMD also raised its outlook for the server CPU market, from roughly $25 billion today to more than $200 billion by 2030, driven by agentic AI. They went a step further and described a new class of server built for agent workloads, dense CPU systems that run the reasoning loops, tool calls, and data retrieval that surround every model. That matched what Cornelis Networks hear from its own customers, and it is why its reference architecture treats the CPU tier as a pool of its own. Alongside GPU pools for building context and generating tokens, Cornelis Networks is defining a Venice-based pool for agent orchestration, routing, and retrieval, sized to grow in step with the others. Agents multiply the traffic between all three pools, and a 256-core Venice socket can put more of that traffic on the network than any CPU before it. Cornelis Networks designed its CN6000 SuperNIC to stay ahead of what that socket can drive. It matches the new bus speeds of these new PCIe Gen 6.0 Venice systems and has an industry-leading design target of 1.6 billion messages per second, with ultra-low latency. Customers should never face a choice between scaling nodes and a responsive network. Cornelis Networks built the CN6000 so they don't. Open standards are how everyone gets to build. The theme I appreciated most from the keynote was AMD's continued commitment to open platforms, in hardware standards like Ultra Ethernet and in software through ROCm. AMD is driving an open ecosystem for AI infrastructure and Cornelis is proud to be building in it alongside them. Cornelis Networks has put its own work behind that commitment for years. Cornelis is a key contributor to libfabric, the open fabric interface the Ultra Ethernet Consortium has now adopted. Applications built on MPI, OpenSHMEM, and RCCL run on its fabric without code changes. For customers, the payoff is practical, they choose their processors, their accelerators, their fabric, and their software on the merits. They keep that choice as their systems grow and their workloads evolve. Open standards are what turn a market this size into an opportunity for everyone building in it, from OEMs and enterprises to HPC centers, sovereign AI programs, and cloud providers. What comes next. Its announcement last week was the starting line, not the finish. Between now and the second half of 2026, when both the CN6000 and Venice arrive, Cornelis Networks is working with its OEM partners to validate the full architecture, and Cornelis Networks will share those results this fall at the AI Infra Summit and OCP Global Summit. Its thanks to AMD for another great week and to the customers and partners who spent time with Cornelis Networks. The market numbers on that stage were remarkable, but numbers alone do not build anything. The winners of this cycle will be the companies that help customers turn infrastructure into results, and that work goes faster when Cornelis Networks do it together.
Cornelis has unveiled a reference architecture pairing its CN6000 SuperNIC with AMD 6th Gen EPYC processors and AMD Instinct MI400 series GPUs. The platform targets disaggregated AI inference, large-scale training, and HPC simulation. The CN6000 SuperNIC supports Omni-Path, RoCEv2, and Ultra Ethernet protocols on a single adapter. It delivers 800 Gbps bandwidth through PCIe 6.0 x16 interface and targets over 1.6 billion bidirectional messages per second. Pre-production simulations show the CN6000-based network completing AllReduce collectives roughly 24% faster than standard Ethernet. Training time for a 250-billion-parameter model on a simulated 10,000 GPU cluster was cut by approximately 13%. Cornelis will share full architecture details and OEM validation results at OCP Global Summit and AI Infra Summit this autumn. The CN6000 family is expected to be generally available in Q4 2026.
Cornelis Networks has announced the acceptance of a CN5000 networking upgrade for Texas Advanced Computing Center's Stampede3 supercomputer. The deployment upgrades over 600 compute nodes, serving more than 5,000 researchers annually across weather forecasting, engineering and data analytics. Testing using the Weather Research and Forecasting model demonstrated performance improvements ranging from 53% to 71% across multiple scaling points. The upgrade enables researchers to complete simulations faster and supports increasingly complex workloads. The CN5000 system, built on Omni-Path architecture, provides lossless, congestion-free networking designed for AI and high-performance computing environments. TACC executive director Dan Stanzione said the upgrade strengthens an important production partition and ensures researchers have access to required performance for demanding workloads.
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Industries
Data & Analytics
Hardware
Industrial & Manufacturing
Company Size
201-500
Company Stage
Debt Financing
Total Funding
$130.8M
Headquarters
Radnor Township, Pennsylvania
Founded
2019
Find jobs on Simplify and start your career today