AI's rapid advancement is fueling demand for efficient infrastructure, as traditional processing struggles to keep pace. ScaleFlux, a leader in AI and data center storage and memory solutions, is paving the way by tackling critical bottlenecks and optimizing bandwidth to reduce latency.
MILPITAS, Calif., March 19, 2025 /PRNewswire-PRWeb/ -- Global spending on AI hardware nearly doubled in the first half of 2024, and projections indicate even greater acceleration in the coming years, with total AI infrastructure spending expected to surpass $200 billion by 2028. (1) Yet, this rapid expansion has exposed infrastructure bottlenecks, sparking debate over whether traditional architectures can continue to scale AI capability. DeepSeek's launch and the trillions in AI stock selloffs that followed underscored opportunities for greater efficiency in AI. In this context, is leading the shift toward specialized accelerator components, enhancing AI scalability. "Businesses need smarter infrastructure that boosts efficiency without compromising scalability," states JB Baker, VP of Products of the company. "Our technology eliminates bottlenecks, ensuring AI reaches its full potential."
Addressing Memory Bandwidth Limitations in the Growth of AI Models
AI models, particularly large language models (LLMs), have seen impressive growth in complexity and size. However, a significant challenge remains: memory bandwidth limitations. Over the last two years, compute power has grown by 750%, yet the supporting infrastructure—such as memory capacity and interconnect bandwidth—has not kept pace. While the performance of server hardware FLOPS has increased by a factor of 3× every two years, DRAM and interconnect bandwidth have grown at a much slower rate of just 1.6 and 1.4 times, respectively. (2)
This disparity has emerged as a dominant bottleneck for AI workloads, especially in model training and inferencing. AI applications previously scaled by distributing workloads across multiple accelerators, but this approach has not addressed memory bandwidth limitations. Even when models fit within a single chip's memory, intra-chip memory transfer speeds—from registers to global memory—have become a major performance constraint. (2)
To address these challenges, AI experts are advocating for a redesign of AI model architecture and training strategies. More efficient training algorithms, improved memory hierarchy designs, and specialized hardware accelerators that optimize memory-bound workloads are expected to play a key role in overcoming the "memory wall" that currently limits AI scalability.
DeepSeek's Impact and the Shift Toward Optimized AI Infrastructure
Amid these challenges, DeepSeek's launch reshaped AI priorities, triggering a $1 trillion stock selloff before partial recovery. (3) The event underscored the growing need to rethink AI infrastructure to support increasingly complex models.
By leveraging multi-head latent attention and simultaneous multi-word generation, DeepSeek highlighted structural limitations in existing AI model architectures, particularly in how memory bandwidth and data movement impact performance at scale. This has driven renewed focus on optimizing system-level architecture—rethinking how memory is attached and how data flows between processors, memory, and storage.
As AI scales, specialized solutions like Compute Express Link (CXL) offer optimized memory and storage connectivity. Pioneered by companies like ScaleFlux, CXL reduces compute-memory bottlenecks with low-latency expansion, improving efficiency for large AI models. By enhancing data transfer and streamlining memory access, CXL enables AI workloads to scale effectively without requiring costly infrastructure overhauls. (5)
ScaleFlux Pioneering AI Infrastructure Solutions for Scalable, Efficient Operations
is redefining AI infrastructure with its innovative approach to optimizing data transfer speeds, reducing latency, and addressing critical memory bandwidth bottlenecks. By combining its NVMe SSD solutions with advanced CXL memory modules, ScaleFlux enables businesses to scale AI operations efficiently without compromising performance or cost.
- NVMe SSD Solutions: ScaleFlux integrates write reduction technology into its SSD controllers. This improves throughput for AI workloads, enabling faster model serving and lower energy consumption, making AI operations more efficient and cost-effective.
- CXL Memory Modules: As AI models demand more memory than traditional architectures can support, ScaleFlux's CXL-based solutions expand memory capacity while maintaining low-latency access. This ensures seamless scalability without requiring extensive and expensive GPU overhauls.
By delivering purpose-built solutions that optimize data flow for AI performance and efficiency, empowers businesses to maximize hardware utilization and stay competitive in the rapidly evolving AI landscape. Its integrated strategy positions the company as a future-proof leader in AI infrastructure innovation.
The Shift Toward Specialized Hardware Solutions in AI Infrastructure
The focus on specialized hardware solutions represents a fundamental shift in how AI infrastructure is being designed and deployed. Over the past two decades, while processor performance has grown exponentially, the infrastructure required to support this growth, such as memory capacity and interconnect bandwidth, has not scaled at the same rate. This widening gap is now the key limiting factor in scaling AI cluster performance.
"These changes have catalyzed a critical reassessment of the approach to architecting infrastructure for AI," Baker concludes. "It underscores the need for efficiency-driven models that enhance performance while optimizing resource utilization. This paradigm shift is essential for sustaining the rapid growth and adoption of AI technologies."
About ScaleFlux
In an era where data reigns supreme, ScaleFlux emerges as the vanguard of enterprise storage and memory technology, poised to redefine the landscape of the data infrastructure - from cloud to AI, enterprise, and edge computing. With a commitment to innovation, ScaleFlux introduces a revolutionary approach to storage and memory that seamlessly combines hardware and software, designed to unlock unprecedented performance, efficiency, security and scalability for data-intensive applications. As the world stands on the brink of a data explosion, ScaleFlux's cutting-edge technology offers a beacon of hope, promising not just to manage the deluge but to transform it into actionable insights and value, heralding a new dawn for businesses and data centers worldwide. For more details, visit .
References
1. Ashare, Matt. "AI Hardware Spending Soared Last Year, Thanks to Cloud." CIO Dive, 19 Feb. 2025, ciodive.com/news/ai-hardware-server-spend-soars-idc/740423/. Accessed 7 Mar. 2025.
2. Gholami, Amir, et al. "AI and Memory Wall." Ai and Memory Wall, Cornell University, 1 Mar. 2024, arxiv.org/html/2403.14123v1?utm.
3. Carew, Sinead, et al. "Deepseek Sparks AI Stock Selloff; Nvidia Posts Record Market-Cap Loss." Reuters, 27 Jan. 2025,
4. "Ai in Hardware Market Report 2025 - AI in Hardware Market Opportunities and Forecast." The Business Research Company, 1 Jan. 2025
5. "NVMe Storage and CXL Memory Solutions." ScaleFlux, 20 Feb. 2025, scaleflux.com/.
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