Please note that the information covered in this article is based on unverified reports that have not been officially confirmed. Therefore, definitive expressions are avoided, and the content is described as reports based on news coverage and comments from involved parties.<\/p>\n
It is reported that interest in the AI field in 2026 is shifting significantly from large-scale model training to “inference.” With the increase in model usage and the spread of agentic AI, demand for inference is surging, and major tech companies and semiconductor startups are reportedly exploring new hardware configurations.<\/p>\n
Following Nvidia’s reported $20 billion acquisition of Groq’s technology and talent, as well as Amazon’s partnership with Cerebras, a transition is reported away from traditional training-centric GPU setups toward inference-optimized hardware that prioritizes memory bandwidth and on-chip memory.<\/p>\n
According to reports, it has become clear that the computational characteristics required for AI model training and inference differ significantly. In contrast to the backpropagation process performed during training, the “Decode” phase of inference\u2014which generates tokens autoregressively one by one\u2014is said to be bottlenecked by the memory bandwidth required to quickly read model weights and the KV cache (key-value cache). As a result, it is pointed out that when running open-source LLMs on Nvidia H100 GPUs, processors spend 50 to 80% of processing time idle, waiting for data.<\/p>\n
To resolve these memory issues and computational inefficiencies, various companies are reportedly pursuing the following approaches and movements.<\/p>\n
d-Matrix is said to have adopted a configuration in its second-generation AI accelerator, “Raptor,” where computation accelerator dies are stacked directly on top of DRAM dies, reducing data movement distance to the micrometer scale. Meanwhile, Majestic Labs claims that by using proprietary copper links and memory aggregator chips to extend general-purpose DRAM connection distances to about 1 meter, it can connect up to 128 terabytes of DRAM per server rack.<\/p>\n
In addition, memory makers such as SK Hynix have begun production of “HBM4,” which doubles maximum bandwidth, and it is expected to be featured in Nvidia’s Vera Rubin GPUs scheduled for shipment in late 2026.<\/p>\n
Methods have emerged that divide the workload between the computationally intensive “Prefill” phase and the heavily memory-bandwidth-consuming “Decode” phase across separate hardware.<\/p>\n
Nvidia has reportedly presented a two-chip strategy where Vera Rubin GPU racks handle Prefill and attention calculations, while the “Groq 3 LPU,” equipped with 500 megabytes of on-chip SRAM, handles Decode processing. A rack-sized “Groq 3 LPX” system incorporating 256 LPUs is also reportedly prepared.<\/p>\n
Amazon Web Services (AWS) has also reportedly adopted a configuration that uses its proprietary training chip “Trainium” for Prefill, paired with Cerebras’s “Wafer-Scale Engine 3 (WSE-3)” for Decode processing. Cerebras’s WSE-3 is a single silicon wafer integrating over 4 trillion transistors with 44 gigabytes of built-in SRAM, capable of supporting models up to 80 billion parameters on its own. It has reportedly been deployed in the operation of OpenAI’s “GPT-5.3-Codex-Spark,” achieving output speeds exceeding 1,000 tokens per second.<\/p>\n
Optimization through software-hardware co-design is also progressing. Nvidia developed the 4-bit numeric format “NVFP4,” and reports claim that when quantizing DeepSeek-R1 from FP8 to NVFP4, performance was improved threefold while keeping score degradation on major benchmarks below 1%. Furthermore, AMD, Intel, and Qualcomm are supporting the “MXFP4” format.<\/p>\n
Additionally, startup Tensordyne has developed “Napier” hardware utilizing a logarithmic calculation system, claiming that by replacing multiplication with addition, it can achieve outputs of up to 1,300 tokens per second per user with less than one-tenth the power of equivalent Nvidia hardware.<\/p>\n
As another industry movement, transactions such as Anthropic leasing surplus computing resources from competitor SpaceXAI for over $1 billion per month have also been reported.<\/p>\n
The materials explain that the shift in the AI mainstream from training\u2014which competes on the number of model parameters\u2014to inference is driven not only by the practical application of LLMs but also by changes in how models are utilized.<\/p>\n
In particular, the emergence of reasoning models that perform chains of thought has dramatically increased the number of tokens generated per query as models perform self-reiteration. Models with high thinking effort are said to generate up to 20 times more text than traditional models. Furthermore, the expansion of agentic AI, which operates autonomously toward goals 24 hours a day, is analyzed to be driving a worldwide explosion in inference workloads.<\/p>\n
For engineers operating open-weight models in local environments or on private servers, these developments could have the following impacts:<\/p>\n
Improved Execution Efficiency via Quantum Advancements<\/strong> Limited Direct Mention of Local Hardware<\/strong> Impact on Model Distribution Forms and Licenses<\/strong>
\nThe standardization of new 4-bit quantization formats and methods like NVFP4 and MXFP4 may allow users to run larger models within local memory capacity limits while minimizing accuracy loss.<\/p>\n<\/li>\n
\nMany of the reported hardware approaches\u2014such as WSE-3, Groq 3 LPX, and Majestic Labs’ rack-scale DRAM systems\u2014are massive configurations targeted at data centers and cloud infrastructure. The materials do not clearly state whether these will be directly scaled down for personal equipment or general local environments.<\/p>\n<\/li>\n
\nSpecific information regarding the availability of existing open-weight models, future model licenses, or shifts toward API-centric delivery models is not included in the materials.<\/p>\n<\/li>\n<\/ul>\nSources<\/h2>\n