EvoChip.ai AltiCoreAI Technology Delivers 40X Faster AI Inference Than Neural Networks in Controlled Benchmark

Revolutionary technology achieves order-of-magnitude performance gains on standard CPUs, challenging fundamental assumptions about AI infrastructure requirements.

EvoChip.ai, a computer architecture innovator redefining AI efficiency, has announced results from a controlled benchmark study demonstrating that its AltiCoreAI technology delivers 13 to 41 times faster inference performance than highly optimized neural network implementations running on standard CPU hardware.

The benchmark, conducted jointly with SidePath, a premier IT solutions provider, tested AltiCoreAI against multiple TensorFlow-based implementations across seven diverse public datasets on both workstation and server-class hardware. AltiCoreAI consistently outperformed the fastest neural network configuration in every dataset tested, sustaining 472 to 575 million inferences per second on server-grade CPUs compared to 21 to 54 million inferences per second for optimized neural networks.

“For years, the AI industry has operated on the assumption that you need massive computational resources and specialized hardware to run meaningful AI workloads. This benchmark demonstrates that assumption is wrong. We’ve proven that a fundamentally different mathematical approach can deliver comparable accuracy with a fraction of the computational footprint—unlocking dramatic cost savings and enabling AI deployment in environments where conventional approaches simply cannot run.” — Alain Blancquart, CEO, EvoChip.ai

Paradigm Shift in AI Economics

AltiCoreAI’s performance advantage stems from its fundamentally different architectural approach, leveraging what computers do natively—fast logical operations—while minimizing dependence on computationally intensive arithmetic. The resulting efficiency gains were substantial:

• 35 to 301 times fewer parameters
• 40 to 343 times fewer arithmetic operations per inference
• Comparable accuracy to neural network baselines across all tested workloads

“These aren’t marginal improvements—they’re structural advantages that translate directly into lower cost per decision, higher capacity per server, and broader deployment reach. For organizations where AI inference represents a major cost center, this magnitude of efficiency gain fundamentally changes the economics.”
 — Alain Blancquart, CEO, EvoChip.ai

Real-World Deployment Implications

AltiCoreAI’s compact models enable AI deployment in resource-constrained environments where conventional neural networks cannot operate—including edge devices, embedded systems, and disconnected environments.

Key Advantages

• 10–50X throughput gains translating to proportionally lower infrastructure requirements
• Reduced input requirements (as few as 5–10 variables vs. 22–31 for neural networks)
• Proportionally lower energy consumption

“The difference between requiring $30,000 hardware acceleration and running efficiently on a $50 processor isn’t just economic—it’s about where AI can exist in the world. We’re enabling AI deployment in agricultural equipment in remote regions, medical devices in resource-limited settings, and industrial sensors in disconnected facilities.” — Patrick O’Neill, Co-Founder & CTO, EvoChip.ai

Robust, Comparative Benchmark

Testing spanned seven public datasets covering credit risk, fraud detection, manufacturing quality control, and medical diagnostics. AltiCoreAI was evaluated against four neural network implementations, including C++ TensorFlow Lite with XNNPACK—a mature, production-grade inference stack. Each configuration underwent multiple independent test runs under controlled conditions.

“There was no cherry-pick of a narrow microbenchmark. The benchmark was structured for a fair, apples-to-apples comparison under consistent conditions, not a selected performance showcase.”
 — Patrick Mulvee, CEO, SidePath

Performance Results: Server-Class CPU (Intel Xeon Gold 5416S)

Target Markets

AltiCoreAI is particularly suited for high-volume structured decisioning workloads across Financial Services, Manufacturing, Healthcare, Industrial IoT, and Telecommunications.

“For organizations running high-frequency decisioning systems where inference costs compound into millions of dollars annually, a 20–40X efficiency gain represents immediate, quantifiable ROI.” — Jerry Conrad, VP Business Development, EvoChip.ai

The AltiCore Family
Built on AltiCore’s mathematical framework, the product family includes:

• AltiCoreSWP — Windows, Linux, CUDA
• AltiCoreMCU — Edge and embedded devices
• AltiCoreHDL — FPGA/ASIC
• AltiCoreGPU and AltiCoreMob — In development

This architectural consistency enables deployment of the same core technology from data center servers to battery-powered sensors.

Commercial Launch and Availability

EvoChip.ai is preparing for commercial launch in April 2026 and is simultaneously pursuing $10 million in equity funding to accelerate go-to-market initiatives.

“This benchmark validates our core thesis: that the AI industry has been optimizing the wrong thing. AltiCore demonstrates that genuine artificial intelligence doesn’t require specialized hardware, massive energy consumption, or centralized infrastructure. It can run efficiently wherever you need it—and that changes everything.” — Alain Blancquart, CEO, EvoChip.ai

More information including a complete methodology and detailed results from EvoChip.ai