Hailo vs Axelera vs Blaize: Choosing Edge Inference Silicon in 2026

Overview

Three vendors dominate the conversation when an edge deployment outgrows a CPU but cannot justify a GPU. Hailo, Axelera and Blaize are not minor variations of one another — they make genuinely different architectural bets, and each is strongest in a different deployment shape. This article compares their published specifications, explains what each design optimises for, and gives a decision frame so the choice follows the workload rather than the datasheet with the biggest number.

Hailo-10H: the low-power language-capable NPU

Hailo’s position is efficiency plus model reach. The Hailo-10H is reported at 40 TOPS at INT4 (20 TOPS INT8) while drawing around 2.5W typical, with a maximum in the region of 3W. The distinguishing feature is 8 GB of dedicated LPDDR4X and a direct DDR interface, which lets the device load larger models for language and vision-language inference rather than being confined to compact vision networks. If your edge requirement includes a small language model or a VLM — an on-device assistant, document understanding at a branch, multimodal inspection — local memory capacity matters more than headline TOPS.

Axelera Metis: throughput for machine vision

Axelera targets high-density vision. The Metis AIPU is reported at a peak 214 TOPS with INT8 weights at roughly 14.7 TOPS/W, in a 3.5W-9W envelope over a PCIe Gen3 x4 interface. That combination suits workloads where many camera streams must be processed by one node — line inspection, retail analytics, city surveillance — and where the models are convolutional vision networks that quantise cleanly to INT8. The PCIe form factor also makes it straightforward to add to an existing industrial PC rather than redesigning the endpoint.

Blaize GSP: graph-native dataflow

Blaize attacks the data-movement problem directly. Its Graph Streaming Processor starts from the observation that a neural network is a graph, and runs it as one: an on-chip hardware scheduler streams the computational graph depth-first through 16 cores, keeping intermediate data in cache rather than round-tripping to external DRAM. Reported figures are 16 TOPS within roughly a 7W envelope, with claims of up to 60x better system-level efficiency versus GPU/CPU for edge AI, 50x less memory bandwidth and ten times lower latency. The raw TOPS number is the lowest of the three, which is exactly why TOPS alone is a poor selection criterion — the architecture targets latency and system efficiency instead.

Published specifications side by side

How to choose

Match the architecture to the bottleneck. If your constraint is power or thermal budget — fanless enclosure, battery, sealed cabinet — start with the lowest-power option. If it is stream density, where one node must handle many cameras, the highest sustained INT8 throughput wins. If it is deterministic latency in a control loop, the dataflow approach that avoids DRAM round-trips deserves evaluation even at lower nominal TOPS. And if the model is a small language or vision-language model, local DRAM capacity becomes the gating spec. In every case, benchmark your own quantised model: compilation quality and achieved utilisation vary far more between toolchains than the datasheets suggest, a point we make in our edge accelerator versus GPU comparison.

What this means for Indian deployments

Two factors weigh heavily here. Power and siting: single-digit-watt devices run in factory cabinets, retail back-rooms and roadside enclosures without the cooling or conditioned power a GPU needs, which matters when endpoints number in the hundreds. Data residency: inference on the device means video and personal data need never leave the site — under the DPDP Act, where penalties reach ₹250 crore per violation, processing locally is the cleanest position for camera and customer data. The common pattern is accelerators at the endpoints and a central GPU server for retraining, keeping the whole loop in-country.

Frequently asked questions