The news: Longsys, a Chinese memory and storage specialist, has released projections pointing to a sharp increase in profits for the first half of 2026. The company ties this growth to two factors: a general strengthening of the memory market and unprecedented demand generated by artificial intelligence. Behind these few lines of forecast lies a profound shift in the semiconductor industry, with direct implications for anyone working with large language models (LLMs).

The memory hunger of AI models is no secret. Training and inference for transformer-based models require massive amounts of VRAM: a single 70-billion-parameter LLM at FP16 can need over 140 GB just to load the weights. With mixture-of-experts architectures and ever-longer context windows, this requirement will only increase. High-bandwidth memory (HBM) has become the most contested component in the supply chain, with top providers—SK hynix, Samsung, Micron—already exhausting production capacity through 2026.

For those evaluating on-premise LLM deployments, the dynamics of HBM and NAND flash pricing and availability are critical. The cost of enterprise GPUs like NVIDIA's H100 or the upcoming B200 is heavily influenced by the integrated memory cost. A contraction or expansion in HBM supply can swing the TCO of an on-premise cluster significantly. Moreover, fast storage (NVMe SSDs) is essential for training data caching and serving models in production, and AI demand is distorting that market as well.

That Longsys isn't a household name doesn't diminish the relevance of its estimates. The company supplies DRAM modules, SSDs, and embedded storage solutions to OEMs and data centers worldwide, giving it early visibility into aggregate demand. The fact that it expects a profit leap already for the first half of 2026 suggests orders are already flowing, driven not only by HBM but also by the rising memory density required for AI inference at scale.

An often overlooked aspect is that AI doesn't just consume fast memory for GPUs. Retrieval-augmented generation (RAG) workloads and ongoing fine-tuning require vector databases and persistent storage that can reach tens of terabytes, all at low latencies. Longsys is a key player here too with its enterprise SSDs. The projected growth may reflect this dual track: HBM for compute, NAND for data.

For on-premise deployment, the robustness of the memory supply chain can make or break a project. If memory prices rise too much, the cloud alternative becomes more attractive, at least in the short term. However, for those with data sovereignty or strict latency requirements, on-premise remains the only path. That's why monitoring market signals like Longsys's forecast is essential.

Longsys's outlook is more than a financial data point: it's a reminder that AI infrastructure, even local, hinges on complex market dynamics. Planning today means considering not just chip performance but the entire memory value chain.