Cloud AI

Inference

Hardware

Aión

Overcoming the limits of hardware for the next generation of AI models

Intelligence at cloud scale

Cloud inference at silicon speed

100% Hardware - 100% Standalone

Full Generative AI inference pipeline

directly in hardware, from prompt to output

with no external host required.

Maximum workload and performance control

Massive aggregated throughput

to least possible latency, you strike the balance.

Minimal latency, deterministic response with a lower power consumption

Built to operate independently.
Designed to adapt.

Industry standard interfaces for optional external management.

Ready to adapt without redesign. Future proof silicon.

Ultra-Fast and Wide Interconnect

Architect your custom cloud following your model architecture.

Higher rack density and reduced cooling challenges

Self-contained architecture means a smaller BOM, lower failure rates, simplified thermal management and infrastructure overhead

Lower Power and Silicon footprint 🔗

Wider deployment margins

Operating within 10% of the mathematical limit.

Optimized to deliver maximum tokens per second with the lowest energy and silicon fabrication cost.

Massive throughput

Lowest Power

9298


tokens/sec (DeepSeek R1 Distill 1.5B)

200 Watts
@ TSMC 3nm

Peak resource efficiency

Massive parallelism

95%


bandwidth utilization

1024


user sequences


Aión

redefines efficiency and profitability
in Generative AI at scale.

Optimized architecture 🔗

Advanced 3nm process
HBM memory interfaces
64bits RISC-V CPUs

64 MB internal SRAM
Compute at TFLOPS scale

Click to see Block Diagram

Massive Throughput 🔗


Model

Aggregate Throughput

tok/s FP16

Max Speed per User

tok/s FP16

DeepSeek R1 Distill Qwen1.5B
9298.4
332.2

Meta Llama 2 7B
1223.1
133.4

Meta Llama 3.18B
2624.6
116.1

Alibaba Qwen 38B
2406.3
110.9

Alibaba Qwen 332B
796.2
28.4

DeepSeek R1 Distill Qwen14B
1474.9
60.0

DeepCoder Preview14B
1463.7
59.1

DeepSeek R1 0528 Qwen 38B
2406.3
110.9
Speed is Output tokens per second, after a prefill of 1024 input tokens.

Beating GPUs — Redefining the limits of TPUs 🔗

Nvidia H100
31
Google TPU v6e
40
RaiderChip NPU
58

Tokens / second / user (Llama 3.1 8B - 1 TB/s)

Performance approaching the physical limit — over 90% efficiency at scale.

Get Started Today!

Contact us to begin evaluating our accelerators

See firsthand how our AI solutions transform your devices

Experience the future of Generative AI acceleration with RaiderChip