GPU networking and power distribution can have a major effect on AI performance, CoreWeave executive Lukas Biewald said—but the report of his comments gives no benchmark or latency measurements to establish an orders-of-magnitude improvement. The useful takeaway is that a GPU cluster is a system: links, workload placement, power, cooling and storage all shape how effectively its processors work together.
What CoreWeave said—and what the claim establishes
In an interview at Fully Connected, reported by SiliconANGLE on October 2, 2026, CoreWeave senior vice president of AI initiatives Lukas Biewald said chip configuration could make a “massive difference,” including an “orders of magnitude difference depending on how you do the networking for the chips [and] how you do the power distribution.”
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That is an executive’s reported observation, not a published controlled benchmark. The article provides no workload, cluster topology, baseline, measurement method or latency values. It therefore does not show that a particular wiring or power change will improve latency by a specific factor, or that the same result applies to every AI workload. SiliconANGLE’s account of the interview is the source for the claim.
Why networking can affect AI response time
GPUs often need to exchange data while working on a model. The route and bandwidth available for that communication, and whether the workload is placed so GPUs can use fast links, can affect how much time is spent moving data rather than computing. A cluster’s internal GPU-to-GPU fabric is distinct from the network used to connect systems or scale across racks; both are part of the architecture, but their effects depend on the workload and design.
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CoreWeave’s current Vera Rubin product page illustrates how tightly coupled these components can be. The company describes a liquid-cooled rack with 72 NVIDIA Rubin GPUs, 36 NVIDIA Vera CPUs, ConnectX-9 SuperNICs and BlueField-4 DPUs, within a 20.7 TB unified HBM4 memory domain. CoreWeave lists 260 TB/s of NVLink 6 Switch bandwidth within the rack, alongside Quantum-X800 InfiniBand and Spectrum-X Ethernet for scaling across GPUs. These are vendor specifications, not independent validation of Biewald’s latency statement. CoreWeave’s Vera Rubin infrastructure page also says its topology-aware scheduler keeps inference workloads on the NVLink fabric.
Power, cooling and useful compute
Power distribution is not a substitute for fast GPU links, and the interview report does not isolate its effect on latency. It is one of the constraints in making a large cluster operate as intended: the facility must deliver power and remove heat while supporting the installed systems. If power or cooling limits constrain operation, the cluster may not make full use of its compute resources. The size of any effect depends on the facility and workload; the available figures do not establish a universal latency gain from changing power delivery.
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CoreWeave’s March 28, 2025 video transcript says traditional air-cooled facilities may face retrofit challenges, power constraints and inefficient resource use with modern GPU clusters. It also claims that “up to 65% of effective compute capacity embedded in gpus is lost to system inefficiencies.” That is CoreWeave’s vendor statement; the transcript supplies no independent study or methodology for the percentage. It should not be treated as a measured loss for every cluster. CoreWeave’s video and transcript provide the company’s account.
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CoreWeave compares two rack systems on its Vera Rubin page. In the company’s stated DeepSeek R1 comparison at 150 tokens per second per user, it lists 80,000 tokens per second per megawatt for GB200 NVL72 and 800,000 for Vera Rubin NVL72—a vendor-stated 10× difference. The page also lists memory bandwidth of 8 TB/s versus 22 TB/s, total GPU memory bandwidth of approximately 576 TB/s versus 1,580 TB/s, and NVLink bandwidth of 130 TB/s versus 260 TB/s, respectively.
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These are figures in a vendor comparison, not an independent test of network topology or power distribution. The systems differ in generation and specifications, and the page does not provide enough methodology in the cited material to reproduce the performance result. The comparison cannot prove that wiring or power delivery alone caused the throughput difference, or translate it into an equivalent latency change.
Why bursty inference makes infrastructure choices matter
The SiliconANGLE report also describes a business context in which capacity needs can rise and fall. LlamaIndex co-founder and CEO Jerry Liu said the company was then running about 75% inference and 25% training, processing millions of document pages daily for finance, legal and insurance customers, and did not own a GPU cluster. The report connects rented capacity with persistent but spiky demand. Those figures describe LlamaIndex at the time of the interview, not the broader AI market.
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For this kind of workload, both the amount of capacity available and how well the system handles communication and bursts can matter. The example helps explain why infrastructure operators focus on the whole stack; it does not demonstrate a particular latency benefit from a particular network or power design.
What to ask when evaluating an AI GPU cluster
There is no like-for-like provider or topology comparison in the cited material, so it does not identify a winning design. A practical evaluation should establish the conditions under which a system will serve your workload:
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- Workload and latency target: Specify the model, request pattern, concurrency and the response-time measure that matters to users.
- Interconnect and communication: Ask which GPU-to-GPU fabric is used, what bandwidth is available, and how the design scales beyond a single rack.
- Placement and scheduling: Determine how the platform keeps communicating GPUs together and what happens when capacity is shared or demand spikes.
- Power and cooling envelope: Confirm that the facility can support the cluster’s power and thermal requirements under the operating conditions you need.
- Storage and data movement: Check whether input and model data can reach the GPUs at the rate the workload requires. CoreWeave says its AI Object Storage can deliver up to 7 GB/s per GPU; that is a vendor-stated capability, not a general result for all configurations.
- Interoperability and capacity: Establish how the environment fits with existing tools and other cloud providers, and whether capacity is available when required. Biewald characterized CoreWeave as following standard Nvidia-recommended networking protocols and working alongside customers’ other clouds; this is his characterization, not an independent comparison of provider compatibility.
- Cost per useful output: Compare cost against the completed work or service level you need, rather than relying on a peak throughput figure detached from your workload.
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