The data infrastructure semiconductor giant Marvell Technology (NASDAQ: MRVL) has announced a comprehensive portfolio of next-generation memory solutions designed to optimize Agentic AI inference. Unveiled at the FMS 2026 conference in Santa Clara, the new suite addresses critical bottlenecks in memory capacity and bandwidth that currently limit the scalability of large language models (LLMs). By decoupling memory from compute resources through disaggregated architecture, Marvell aims to enhance GPU utilization and significantly improve token throughput for hyperscale and cloud service providers.
Next-Generation Components for AI Infrastructure
The newly released portfolio spans server-level storage, rack-scale expansion, and multi-cabinet interconnects. Central to this launch is the Bravera SC6 PCIe 6.0 SSD controller, which delivers double the performance of its predecessor, the Bravera SC5. This controller is specifically engineered to offload KV Cache (Key-Value Cache) from expensive high-bandwidth memory (HBM) to high-capacity SSDs, enabling models with longer context windows to operate more efficiently.
Key features of the new hardware include:
- Bravera SC6 SSD Controller: A NAND-agnostic PCIe 6.0 solution that reduces write amplification and extends the endurance of flash memory.
- Structera X Platform: Developed in collaboration with hyperscalers, this CXL-based (Compute Express Link) solution enables rack-level memory expansion and pooling.
- Photonic Fabric: An optical interconnect technology that creates a shared-memory tier across multiple cabinets, spanning distances up to 50 meters.
Addressing the Bottlenecks of Agentic AI
As AI models transition toward agentic workflows—where AI systems perform multi-step reasoning and long-term tasks—the demand for memory has surpassed the capabilities of traditional tightly coupled architectures. Marvell’s disaggregated approach allows for heterogeneous inference, separating the compute-heavy "prefill" phase from the memory-intensive "decode" phase. This prevents hardware stalls and improves token efficiency, which is vital for maintaining the low latency required by decentralized AI applications and complex blockchain-integrated agents.
AI infrastructure is moving beyond isolated servers to systems where compute, memory, and connectivity operate seamlessly together.
Impact on Data Centers and Emerging Tech
The shift toward memory pooling is expected to have a profound impact on the total cost of ownership (TCO) for data centers. By utilizing CXL memory expansion, operators can scale memory independently of the number of GPUs, reducing redundant provisioning. For the broader technology sector, including Proof-of-Useful-Work (PoUW) blockchain networks and decentralized compute providers, these advancements offer a roadmap for scaling AI services more sustainably. Industry analysts suggest that these innovations could lead to a 2-3x increase in token throughput within existing power and space footprints.
In conclusion, Marvell's latest memory infrastructure represents a strategic pivot toward a memory-centric computing model. By solving the persistent "memory wall" challenge, these technologies provide the necessary hardware foundation for the next stage of AI evolution, ensuring that infrastructure can keep pace with the exponential growth in model complexity and data processing requirements.
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