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Intel’s Hala Point is a neuromorphic research prototype built around 1,152 Loihi 2 chips. Intel announced it in April 2024 as the world’s largest neuromorphic system, reporting capacity for up to 1.15 billion neurons. That superlative needs a date: Zhejiang University announced a system with more than two billion neurons in 2025. The figures are institution-reported and do not establish a complete independent ranking of overall system scale.
What is Intel’s Hala Point?
Hala Point is an Intel research system designed to run brain-inspired computing workloads. Intel said it was initially deployed at Sandia National Laboratories, where Sandia teams and National Nuclear Security Administration research groups could investigate neuromorphic computing. Intel describes Hala Point as a research prototype intended to advance future commercial systems, not as a product currently offered for sale.
Intel’s design uses asynchronous, event-based spiking neural networks. Rather than treating every computation as a continuously active operation, these networks process events as they occur. Intel also highlights integrated memory and computation, sparse and changing connections, and communication between neurons without routing every exchange through distant memory. The goal is to limit unnecessary computation and data movement on suitable workloads—not to make every kind of computing more efficient.
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How large is Hala Point?
Intel’s April 2024 specifications describe the system as follows:
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| Specification | Intel-reported figure |
|---|---|
| Loihi 2 processors | 1,152 |
| Neuron capacity | Up to 1.15 billion |
| Synapses | 128 billion |
| Neuromorphic processing cores | 140,544 |
| Embedded x86 processors for ancillary computation | More than 2,300 |
| Maximum power draw | 2,600 watts |
| Processor manufacturing process | Intel 4 |
| Chassis | Six rack units; Intel compares its size to a microwave oven |
These are figures Intel published for Hala Point; they describe different aspects of the system and should not be treated as interchangeable measures of capability. In particular, a neuron-capacity figure does not by itself establish biological fidelity or performance on a given task. Intel says Hala Point is not intended for neuroscience modeling. Its capacity is roughly comparable to an owl brain or a capuchin monkey cortex, an analogy about scale rather than biological equivalence. Intel’s Hala Point announcement gives the specifications and its explanation of the architecture.
What do Hala Point’s performance numbers mean?
Intel said Hala Point can support up to 20 quadrillion operations per second (20 petaops) and exceed 15 trillion 8-bit operations per second per watt (TOPS/W) when running conventional deep neural networks. Intel also reported early results as high as 15 TOPS/W for workloads using sparse connectivity and event-driven activity. These are Intel’s reported results for specified workloads, not a universal measure or a directly comparable benchmark across all AI tasks and hardware.
Intel separately says Loihi-based systems can perform inference and optimization with up to 100 times less energy and up to 50 times the speed of conventional CPU and GPU architectures. Those figures carry workload-specific qualifications in Intel’s announcement; they should not be read as general advantages for every application. The available figures do not provide a directly comparable test against the later Darwin Monkey system.
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No. Intel presented Hala Point as a research prototype, and Sandia described receiving it for research. Neither announcement establishes that Hala Point is commercially available or that customers can buy a system. Intel’s stated aim is for research on Hala Point to help advance future commercial systems.
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Loihi 2, the chip family used in Hala Point, is also positioned as research hardware. In 2021, Intel said Loihi 2 supports up to one million neurons per chip and described up to 10 times faster processing and up to 15 times greater resource density than its predecessor under Intel’s comparisons. That announcement also introduced Lava, an open-source framework for neuro-inspired applications that Intel said can run on conventional and neuromorphic architectures. Intel then described research access through the Neuromorphic Research Cloud for engaged members of its Neuromorphic Research Community. Intel’s current research overview says membership is free and open to qualified groups; check Intel’s current information for access details, since the specific 2021 access description is historical. See Intel’s Loihi 2 and Lava announcement and its neuromorphic computing overview.
Is Hala Point still the world’s largest neuromorphic computer?
Not on the published neuron-capacity figures now available. Intel called Hala Point the world’s largest when it announced the system on April 17, 2024. On August 26, 2025, Zhejiang University announced Darwin Monkey, also called Wukong, and said it had more than two billion neurons. That is a higher institution-reported neuron count than Hala Point’s 1.15 billion.
The comparison does not settle which system is larger or better across every meaningful dimension. Zhejiang University says Darwin Monkey comprises 15 blade-type servers, each with 64 Darwin-III chips, and describes simulations involving C. elegans, zebrafish, mice, and macaques. These details and neuron counts come from the institutions’ own announcements; the available information does not establish a full independent ranking or comparable performance tests. Neuron count alone cannot establish relative speed, efficiency, biological fidelity, or overall capability. Zhejiang University’s Darwin Monkey announcement describes its system and reported research.
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Sandia listed research interests spanning physics, chemistry, environmental science, device design, and climate modeling. Intel has named possible future applications including scientific and engineering problem-solving, logistics, smart-city infrastructure, large language models, and AI agents. These are research interests and potential applications, not evidence that Hala Point has delivered deployed products in those fields.
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Intel also cited Ericsson Research’s work applying Loihi 2 to telecom infrastructure optimization. That example illustrates the kind of specialized problem researchers are exploring; it does not show that neuromorphic systems are replacements for general-purpose computing. Sandia team lead Craig Vineyard put the distinction plainly: “We’re not looking at global replacements of all traditional processing. It’s more a matter of identifying the best approach to a problem.” Sandia’s account also quotes Vineyard saying, “Since a system of this scale hasn’t existed before, we’ve been developing algorithms to efficiently use it.” Both remarks appeared in Sandia LabNews on April 18, 2024.
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