Hailo-8 and Hailo-10H are built for different jobs, not different tiers of the same job. Hailo-8 is a CNN-optimized vision accelerator with fully integrated on-chip memory, rated at 26 TOPS INT8, and it remains the stronger choice for object detection, classification, and other computer-vision workloads. Hailo-10H is Hailo’s second-generation, generative-AI-focused chip with a direct DDR interface for running LLMs and VLMs, rated at 40 TOPS INT4 — but its INT8 rating is actually lower than Hailo-8’s, so it is not simply a faster version of the same chip.
That last point trips up a lot of buyers, because the two chips get compared purely on TOPS when the real decision is about architecture: does your product need to classify what a camera sees, or does it need to generate language or run a multimodal model? That question, not the spec sheet, is what should decide between them.
Quick answer
- Choose Hailo-8 if your workload is computer vision — object detection, classification, segmentation — especially at high frame rates, multiple camera streams, or in a fanless, cost-sensitive design. Its on-chip memory architecture keeps system cost and power down for exactly this kind of workload.
- Choose Hailo-10H if your product needs to run an LLM, VLM, or other generative AI model on-device — local chatbots, multimodal assistants, natural-language device control. It’s the only one of the two chips architected for models too large to fit in on-chip memory.
- Don’t choose based on TOPS alone. Hailo-10H’s 40 TOPS figure is an INT4 number; on an INT8 basis it’s rated around 20 TOPS — lower than Hailo-8’s 26 TOPS. The two numbers aren’t measuring the same thing.
Spec-for-spec comparison
| Hailo-8 | Hailo-10H | |
|---|---|---|
| Target workload | CNN-based computer vision | Generative AI (LLM, VLM, diffusion) |
| Peak AI performance | 26 TOPS (INT8) | 40 TOPS (INT4) / ~20 TOPS (INT8) |
| Neural core generation | First-generation Hailo architecture | Second-generation architecture, transformer support |
| Memory architecture | Fully integrated on-chip memory | Direct DDR interface, scales to external memory |
| Typical power | ~2.5W | ~2.5–5W depending on workload |
| Form factors | M.2, Board-to-Board (B2B), Mini PCIe | M.2 |
| Quantization support | INT8 | INT4, INT8, FP16 |
| Supported frameworks | TensorFlow, PyTorch, ONNX, Keras, TFLite | Growing LLM/VLM model support via Hailo’s GenAI stack |
| Grade options | Industrial (-40°C to 85°C), automotive variants available | Industrial and automotive grades available |
| Typical LLM performance | Not designed for this workload | ~10 tokens/sec on 1.5–2B parameter models at ~2.5W |
Architecture: on-chip memory vs. direct DDR — the real difference
This is the distinction that actually matters, more than any single TOPS figure. Hailo-8 integrates all the memory a CNN model needs directly on the die, which is what makes it so power-efficient and compact for vision workloads — there’s no external DRAM to add cost, board complexity, or supply-chain risk. Vision models (object detectors, classifiers) are typically small enough to fit entirely in that on-chip memory.
Generative AI models are a different scale of problem. Even a modest 1.5–2B parameter LLM is far too large for on-chip memory alone, and language models depend heavily on memory bandwidth for token-by-token generation. Hailo-10H’s direct DDR interface exists specifically to solve that — it lets the chip scale beyond on-die memory to run models Hailo-8’s architecture was never built to handle. This is why Hailo describes Hailo-10H as a new architecture generation rather than an incremental upgrade: the memory subsystem, not just the compute core, had to change to support generative AI.
TOPS: why “40 beats 26” is the wrong read
Hailo-10H’s headline 40 TOPS figure is measured at INT4 precision. Hailo-8’s 26 TOPS figure is measured at INT8. These are not directly comparable numbers — lower-precision math produces higher raw TOPS counts on the same silicon. On an INT8-equivalent basis, Hailo-10H is closer to 20 TOPS, which is actually below Hailo-8’s 26 TOPS rating.
That doesn’t make Hailo-10H the weaker chip — it makes it a different one. Its architecture trades raw INT8 vision throughput for the memory bandwidth and quantization flexibility (INT4/INT8/FP16) that generative AI models need. Running a CNN detection model on Hailo-10H won’t outperform Hailo-8, and running an LLM on Hailo-8 generally isn’t possible in any practical sense, since the model won’t fit in its on-chip memory architecture. Match the chip to the workload rather than the TOPS number on the datasheet.
Power and thermal design
Both chips are efficient by AI-accelerator standards, and both are available in industrial and automotive-grade variants for extended temperature ranges. Hailo-8 typically runs around 2.5W regardless of workload, since CNN inference is a relatively steady compute pattern. Hailo-10H’s power draw is more workload-dependent — LLM inference on small models can run at roughly 2.5W, while larger models or higher-throughput generation may push closer to 5W. Both remain well within fanless, compact-enclosure territory, which is a large part of why Hailo accelerators show up so often in industrial edge boxes and cameras.
Software and toolchain
Hailo-8 has the more mature ecosystem: broad framework support (TensorFlow, PyTorch, ONNX, Keras, TFLite), years of community deployment, and a large base of documented vision use cases. Hailo-10H uses the same underlying Dataflow Compiler approach but adds Hailo’s newer GenAI software stack for converting and running LLM/VLM models, which is a younger, faster-moving toolchain with a growing but smaller base of validated models compared to Hailo-8’s vision ecosystem.
If your team is already deep in a CNN model pipeline, Hailo-8’s tooling will feel familiar. If you’re deploying an LLM at the edge for the first time, budget extra evaluation time for Hailo-10H’s GenAI toolchain regardless of which chip you ultimately choose — this is a newer category for the whole industry, not just for Hailo.
Cost and availability
Hailo-8 has been shipping in volume for longer and is available in more form factors (M.2, Board-to-Board, Mini PCIe), which generally translates to more sourcing flexibility and competitive pricing at scale. Hailo-10H is newer to market and currently ships primarily in M.2 form, with pricing that reflects both its more recent release and the added DDR memory on the module.
Decision guide by use case
| Use case | Recommended chip | Why |
|---|---|---|
| Object detection / classification camera | Hailo-8 | Purpose-built CNN architecture, mature ecosystem, lower cost |
| Multi-stream video analytics (NVR, retail, industrial) | Hailo-8 | On-chip memory keeps per-stream cost and power low |
| On-device chatbot or voice assistant | Hailo-10H | DDR interface needed to fit LLM weights beyond on-chip memory |
| Multimodal (VLM) device interaction | Hailo-10H | Second-gen architecture with transformer support |
| Cost-sensitive vision-only product | Hailo-8 | Lower cost, more form factor options, proven deployment base |
| Product roadmap adding generative AI later | Hailo-10H | Architecture built for the workload from the ground up |
| Fanless design with strict sub-3W budget | Hailo-8 | Consistent ~2.5W regardless of model complexity |
FAQ
What is the difference between Hailo-8 and Hailo-10H?
Hailo-8 is a CNN-optimized vision accelerator with fully integrated on-chip memory, rated at 26 TOPS INT8. Hailo-10H is a second-generation architecture built for generative AI, with a direct DDR interface that lets it scale to LLM and VLM model sizes Hailo-8’s on-chip memory can’t accommodate, rated at 40 TOPS INT4 (roughly 20 TOPS on an INT8-equivalent basis).
Is Hailo-10H faster than Hailo-8?
Not on a like-for-like basis. Hailo-10H’s 40 TOPS figure is measured at INT4 precision, while Hailo-8’s 26 TOPS is measured at INT8; converted to the same precision, Hailo-10H’s INT8-equivalent performance is actually lower than Hailo-8’s. Hailo-10H isn’t a faster Hailo-8 — it’s architected for a different workload (generative AI) that Hailo-8 wasn’t designed to run.
Can Hailo-8 run LLMs at the edge?
Not practically. Hailo-8’s on-chip memory architecture is built for CNN-scale vision models, not the multi-gigabyte weight sizes of even small LLMs. For on-device language or multimodal models, Hailo-10H’s direct DDR interface is the chip designed for that workload.
Is Hailo-8 still relevant after Hailo-10H?
Yes, for vision workloads. Hailo-8 remains the more mature, cost-effective, and widely deployed option for CNN-based computer vision — Hailo-10H’s architecture advantages are specific to generative AI and don’t translate into a vision-performance upgrade. Choose based on the workload, not the release date.
Which Hailo chip is better for a smart camera or NVR system?
Hailo-8, in almost all cases. Vision-only products — detection, classification, multi-stream analytics — play to Hailo-8’s strengths in on-chip memory efficiency and mature tooling. Hailo-10H only becomes the better fit if the camera or NVR system also needs to run a language or multimodal model alongside its vision pipeline.
Getting to hardware
Geniatech offers both chips as production-ready modules and as part of complete edge AI systems. The AIM-B-H8 is a Board-to-Board Hailo-8 module delivering 26 TOPS at around 2.5W, while the AIM-M-H10 is an M.2 card built on Hailo-10H for on-device LLM, VLM, and diffusion workloads. For a complete system, the APC3588-AI pairs RK3588 with an open M.2 slot that supports Hailo-8 for multi-stream vision and NVR workloads or Hailo-10H for on-device LLM and VLM inference. For a broader look at accelerator options across vision and LLM workloads, see our M.2 AI accelerator comparison.