Optimizing Machine Learning Inference at Scale

We optimize real-time machine learning inference workloads for multiple applications in cloud or enterprise data centers and in edge applications. Our products, expertise and IP ensure all available compute resources are optimized to achieve the lowest deterministic latency and superior throughput, cost and energy.


For financial companies wishing to use ML to make automated trading-related decisions faster than their competitors, VOLLO provides that competitive advantage. Audited performance results for the STAC Research ML inference benchmark confirm that VOLLO has the lowest latency as well as high throughput density and energy-efficiency.

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Recommendation Systems

Recommendation models power the recommendation systems behind search, adverts & personalized content.  Performance of these models is often constrained by system memory.  We can eliminate this constraint, increasing compute density by up to 10x on existing infrastructure.

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Solutions for a Wide Range of ML Applications

To meet the huge increase in demand for AI, technologies must scale efficiently in order to meet strict latency and performance requirements while keeping the total cost of ownership and total power consumption low. Our low latency, high throughput solutions ensure efficient implementation of ML inference at scale.

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