AIC Targets AI's Memory Bottleneck with New NVIDIA-Powered Storage

CITY OF INDUSTRY, Calif. – January 06, 2026 – As the artificial intelligence race intensifies, the hardware running it is undergoing a radical transformation. Today, server and storage solutions provider AIC Inc. announced a significant entry into this next-generation landscape: the F2032-G6, a 2U storage system designed to tackle one of the most pressing challenges in AI inference—the memory bottleneck.

The new system, part of AIC’s expanding NVIDIA BlueField-accelerated portfolio, is more than just a box of flash drives. It represents a new architectural approach to AI infrastructure, aiming to unify compute, networking, and storage to accelerate the complex reasoning and inference tasks demanded by today's massive AI models.

Addressing the AI Memory Wall

The rise of generative AI and large language models (LLMs) has created an insatiable demand for computational power, primarily supplied by GPUs. However, a critical bottleneck has emerged that raw processing power alone cannot solve: the Key-Value (KV) cache. This cache stores the contextual history of an AI conversation or reasoning process, and for modern LLMs, it can swell to hundreds of gigabytes or even terabytes.

This enormous memory footprint creates a "memory wall," where the context required for real-time inference exceeds the capacity of expensive, on-chip GPU memory (VRAM). This forces developers into inefficient workarounds, slows down response times (increasing "time to first token"), and ultimately limits the scale and complexity of AI applications.

The AIC F2032-G6 is purpose-built to dismantle this wall. By creating a high-speed, DPU-accelerated external storage tier specifically for KV cache context, it offloads the memory burden from the GPUs. This allows the GPUs to focus exclusively on computation, dramatically improving efficiency and enabling the deployment of far larger and more sophisticated AI agents. This architectural shift is considered crucial for entering what many in the industry are calling the "agentic era," where AI performs complex, multi-step tasks.

A New Class of AI-Native Infrastructure

At the heart of the F2032-G6 are two to four NVIDIA BlueField-4 Data Processing Units (DPUs). The BlueField-4 is a powerhouse, engineered as the operational core for the "AI factories" of the future. Each DPU delivers up to 800Gb/s of throughput and integrates a 64-core Grace CPU, offloading networking, storage, and security tasks that would otherwise consume valuable host CPU and GPU cycles.

This processing power is orchestrated by the NVIDIA DOCA (Data Center Infrastructure On-a-Chip Architecture) software framework. DOCA microservices enable the F2032-G6 to function as part of the NVIDIA Inference Context Memory Storage Platform, intelligently managing KV cache placement and sharing across multiple AI nodes at the hardware level.

This smart offloading is paired with extreme storage performance and density. The 2U chassis supports up to 32 E3.S/L NVMe drives—the highest-performing category of flash storage. This allows for staggering capacity, enabling up to 8 petabytes (PB) of all-flash storage in a single compact system. The platform’s dual-active node architecture and dual-port design ensure high availability and link redundancy, providing the full resiliency required for mission-critical enterprise workloads.

"As data demands continue to grow at unprecedented rates, AIC is fully committed to the NVIDIA BlueField platform as a storage controller," said Michael Liang, President and CEO of AIC. "The F2032-G6 delivers resilient, high-performance, and scalable flash storage that empowers critical workloads with unmatched speed, reliability, and efficiency."

Strategic Alliance for the Future Data Center

The launch of the F2032-G6 highlights a crucial industry trend: deep collaboration between hardware specialists and AI leaders to build synergistic ecosystems. The tight integration of AIC's storage hardware with NVIDIA's BlueField DPUs and DOCA software is a prime example of the co-design necessary to optimize modern data centers for AI.

This partnership is not exclusive, but rather part of a broad movement. NVIDIA is working with a wide range of server and storage leaders, including Dell, HPE, and Supermicro, to integrate BlueField-4 into their next-generation platforms. AIC's swift alignment and specialized focus on a JBOF (Just a Bunch of Flash) form factor for KV cache offloading positions it as a key enabler within this burgeoning ecosystem.

"The increasing complexity of AI inference and reasoning workloads requires a fundamental architectural shift to unify compute, network, and storage for unprecedented efficiency and scale," said Yael Shenhav, VP of Networking at NVIDIA. "NVIDIA is collaborating with AIC to bring accelerated, AI-native data storage platforms, powered by the BlueField-4 DPU, to scale AI inference in the agentic era."

The Economic Calculus of Smart Storage

For the enterprises and cloud operators that will deploy these systems, the value proposition extends beyond raw performance. While such cutting-edge technology commands a premium, the focus is on a lower total cost of ownership (TCO) and a higher return on investment for expensive AI assets.

By alleviating the KV cache bottleneck, the F2032-G6 allows for greater utilization of costly GPU clusters, preventing them from sitting idle while waiting for data. Furthermore, NVIDIA claims that BlueField-4-powered platforms can offer up to five times greater power efficiency for memory-bound workloads compared to traditional approaches. In an era of escalating data center energy costs, such efficiency gains are a critical financial consideration.

The system's incredible density—up to 8 PB in a 2U rack space—also translates directly to operational savings in physical footprint, cooling, and management overhead. For hyperscalers and large enterprises planning AI infrastructure at scale, these efficiencies are not just beneficial; they are essential for sustainable growth. With BlueField-4 platforms expected to become more widely available in the second half of 2026, solutions like the F2032-G6 are poised to become foundational building blocks for the next wave of AI services.