Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →On April 2, 2024, Hailo announced an additional $120 million in Series C funding and introduced Hailo-10, an accelerator designed for generative-AI inference on edge devices. The funding announcement brought Hailo’s stated cumulative funding to more than $340 million. It marked a financing and product milestone—not proof of the company’s current financial health, Hailo-10’s present availability, or a win over Nvidia.
What Hailo announced on April 2, 2024
Hailo said it had closed a $120 million extension to its Series C. The company named the Zisapel family, Gil Agmon, Delek Motors, Alfred Akirov, DCLBA, Vasuki, OurCrowd, Talcar, Comasco, Automotive Equipment and Poalim Equity among the investors. Hailo said the round took its cumulative funding above $340 million. These are figures and investor details from Hailo’s announcement.
The company framed the financing as support for growth and opportunities in its pipeline. CEO and co-founder Orr Danon said the round would help the company pursue those opportunities and plan for long-term growth. A funding announcement alone does not show how much capital remains available, how quickly it will be spent, or whether the business is profitable.
What Hailo-10 is—and what its specifications establish
Hailo introduced Hailo-10 as an accelerator for generative-AI inference on edge devices, with early applications in PCs and automotive infotainment. In this context, “edge” means running AI processing on or near the device using the accelerator, rather than sending every request to a remote data center.
#1 Best Overall
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
Hailo’s announcement listed performance of up to 40 TOPS and said Hailo-10 could run Llama 2 7B at up to 10 tokens per second while using under five watts. These are vendor-reported figures, not results from an independent, like-for-like test. The announcement also said samples were expected to ship in Q2 2024; that was a plan stated in 2024, not confirmation of current shipping status or availability.
Those figures do not, by themselves, establish how Hailo-10 compares with a particular Nvidia GPU or another accelerator. A useful comparison would need to match the model and workload, measure performance and power under the same conditions, and account for memory, the rest of the system, software support, developer effort and total cost.
Why put generative AI on a device?
Hailo’s case for edge inference is that local processing can reduce dependence on a cloud connection and avoid network-latency constraints. The company also presented edge AI as a way to address privacy concerns and energy use. These are potential benefits, not guarantees: actual latency, privacy and power outcomes depend on the application, device, network and deployment design.
Rank #2
- Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor.
- 2.5W typical power consumption
- Enabling real-time low latency and high-efficiency AI inferencing on the edge devices
- Supports TensorFlow TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- Supports Linux and Windows.
- Connectivity: An on-device workload may keep working when a cloud connection is unavailable, if the application and model are designed to run locally.
- Latency: Local inference can avoid the round trip to a remote service, but the result depends on the hardware and workload.
- Privacy: Keeping data on a device can limit what needs to be sent elsewhere; it does not automatically ensure secure handling or eliminate all data sharing.
- Energy: A low-power accelerator may suit some edge workloads, but whole-system energy use and workload efficiency matter more than a chip specification in isolation.
How Hailo’s strategy differs from Nvidia’s
Hailo is pursuing specialized accelerators for selected AI workloads in edge products. Nvidia’s competitive strength, as described in TechCrunch’s reporting, includes a much larger, more established software ecosystem. The two companies are not simply offering interchangeable chips for the same deployment: the intended workload and location matter.
Stanford professor Christos Kozyrakis told TechCrunch that accelerators can handle efficiency-critical tasks while general-purpose processors preserve programmability. He also highlighted Nvidia’s software advantage, saying the company had invested in software for its architectures for more than 15 years. For a buyer, that points to a practical trade-off: specialized hardware may be attractive for an appropriate workload, while mature tools, model support and developer familiarity can make a broader platform easier to deploy.
There is no independent Hailo-10-versus-Nvidia benchmark in the cited material, so it does not support declaring a general performance winner. A real procurement decision should compare the same model and task, memory and system requirements, software compatibility, latency, power and full deployment cost.
Rank #3
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
What the funding says about a tough market for chip startups
TechCrunch placed Hailo’s raise in the context of a difficult funding environment for chip startups. Its April 2024 report relayed figures of $881 million raised by U.S. chip companies from January through September 2023, compared with $1.79 billion in the first three quarters of 2022. The passage did not identify the original publisher of those figures, so they should be read as context reported by TechCrunch rather than as independently verified market totals here.
The same report described Mythic as having run short of cash in 2022 and Graphcore as facing mounting losses at the time. Those were dated descriptions, not statements about either company’s present status. Hailo’s ability to raise capital in 2024 showed investor support for that financing round; it does not establish that every AI-chip startup is funded adequately or that Hailo will succeed commercially.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Hailo’s customer and product context
TechCrunch described Hailo as founded in 2017 by Orr Danon and Avi Baum to design specialized AI chips for edge devices. The report identified applications including automotive, security, retail, industrial automation, medical devices and defense. It also reported Danon’s claim that Hailo had more than 300 customers in 2024; that was an executive’s claim, not an independently audited customer count.
Rank #4
- World's first USB edge AI accelerator for both classic AI and generative AI.
- UGen300 features Hailo-10H chipset delivering up to 40 TOPS (INT4) at 2.5 W (typical) and comes with 8GB LPDDR4 Memory
- Provides 150+ pre-trained models (LLM, VLM, Whisper, Vision Network, and more) via the online model zoo
- Supported host architectures: x86, ARM & Supported operating system: Windows, Linux, and Android
- Compatibility with major frameworks: TensorFlow, TensorFlow Lite, Keras, PyTorch, and ONNX
A separate hardware path appeared in TechCrunch’s coverage of a Raspberry Pi and Hailo AI extension kit. The Hailo topic page lists a June 4, 2024 story about the kit. It is development hardware, not evidence that Hailo-10 is a Raspberry Pi add-on, that the products are the same, or that the kit is currently available through a particular retailer.
What a reader should take from the announcement
For investors, the headline is a dated private-company funding milestone, not a standalone measure of business value or financial resilience. For developers or device makers, Hailo-10’s stated specifications and intended edge use offer a starting point for evaluation, but purchasing or design decisions need current availability, compatibility information and workload-specific testing.
Hailo’s stated ambition was to make high-performance AI available beyond data centers. Whether its specialized approach can compete in practice depends not just on chip performance, but also on software, developer adoption and the applications that customers choose to build. Hailo’s company overview describes its edge-device focus; current product status should be checked with the company rather than inferred from its 2024 announcement.
Free tools Windows power users keep installed
One-click scans. No signup required.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
