AI Inference

In the marginal cost of large-scale inference, compute procurement accounts for an extremely high share.

Model training is a one-time investment, whereas inference is a continuous cost incurred with every user interaction.

There is a significant nonlinear relationship between model capability and inference cost in the AI field, and the per-inference cost of flagship models can be tens of times that of lightweight models.

The total energy consumption of the AI inference phase has gradually surpassed that of the training phase, because inference runs continuously 24×7.

The inference cost of AI large model companies is a variable cost that grows linearly with usage rather than a fixed cost approaching zero, making their cost structure resemble that of industrial companies.

The RTX 5090 has memory capacity and compute power far exceeding ordinary gaming needs, making it a more cost-effective alternative for small- and medium-scale AI inference tasks or specific training scenarios.

The inference phase is the forward computation after model deployment, and is typically more sensitive to latency, throughput, and cost per token, with relatively lower requirements for interconnect bandwidth.

Training pursues extreme performance and interconnect bandwidth, with strong ecosystem lock-in; inference places more emphasis on unit cost, energy efficiency, and deployment flexibility.

Data centers are the physical carriers that support AI training and inference, typically characterized by extremely high power density and cooling demands.

The carbon emissions of the same AI inference request can differ by a factor of 8.5 between the Texas grid (ERCOT) and the Swedish grid.

Data generated during the inference phase includes prompts, intermediate model activations, output content, call logs, audit and compliance records, images and videos uploaded by users, and multimodal files produced by generative models, all of which land in the storage layer

Intermediate data such as checkpoints and logs generated by training can usually be cleaned up after training ends, whereas data generated by inference is directly tied to business operating state, and deleting it often means losing traceability and user experience

Training is a phased capital expenditure, with GPU clusters invested intensively over weeks to months, making the bill relatively concentrated and visible; inference is a continuous operating expenditure that fluctuates linearly or even exponentially with user request volume

Inference is the process of responding to user requests after model training is complete; the computation per request is small but the request volume is enormous, placing more emphasis on throughput, latency, and unit cost.

Training workloads emphasize large-scale parallel compute and data throughput, while inference workloads focus more on unit cost, latency, and elastic scaling.

Security researchers estimate that a botnet composed of millions of smart devices could have aggregate computing power reaching tens to hundreds of PetaFLOPS, enough to support distributed inference of medium-scale AI models.

Current high-quality conversation models still rely heavily on cloud GPU clusters, and the limitations of edge devices in compute and memory make it difficult for them to carry real-time inference of large-parameter models.

For large-scale inference services, tokens per watt throughput directly determines the operating cost per unit of computing power.

Pushing model inference capabilities down to edge nodes can significantly reduce response latency for end users.

In traditional AI inference workflows, data typically needs to reside in memory in plaintext while the GPU performs computations, creating a security blind spot for 'data in use'.