NETINT Technologies about Unleashing the Power of GPU-Free AI

Опубликовано: 01 Январь 1970
на канале: NETINT Technologies Inc.
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One of the key factors behind Ampere's success in AI processing is their utilization of a large number of cores. In the case of the NETINT Quadra server running the Whisper model, Ampere leverages an impressive 96 cores to deliver high-performance AI capabilities.

Parallel Processing: With a large core count, Ampere can efficiently distribute AI workloads across multiple cores, enabling faster and more efficient processing.
Scalability: As AI models become increasingly complex, having a scalable solution like Ampere's multi-core architecture becomes crucial for handling demanding workloads.

To showcase the power of Ampere's GPU-free AI approach, let's take a closer look at the NETINT Quadra server demo.

Multi-Channel Support: The NETINT Quadra server, powered by Ampere, can support up to 30 simultaneous video streams, taking in audio and generating closed captions in real-time.
Multilingual Capabilities: This solution can be configured to support multiple languages, making it versatile for a wide range of applications.
High-Performance AI on CPU: By running AI workloads directly on the CPU, Ampere eliminates the need for specialized GPU hardware, simplifying deployment and reducing costs.

To further demonstrate Ampere's exceptional performance, let's examine some benchmark results comparing the Ampere Ultra Max CPU against Intel and AMD offerings.

Raw Performance Leadership: Across various AI workloads such as DLRM (recommender engines), BERT (natural language processing), Whisper (speech-to-text), and ResNet (computer vision), the Ampere Ultra Max consistently outperforms its Intel and AMD counterparts.
Cost-Effective Performance: When considering the performance per dollar spent, Ampere Ultra Max delivers outstanding value, particularly in cloud environments where costs are generally fixed.
Efficiency Advantage: Ampere's CPUs consume less power, enabling cloud providers to offer more cost-effective instances while maintaining high performance.

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