Question 1

What is AI inference infrastructure?

Accepted Answer

AI inference infrastructure refers to the systems and compute resources used to run trained AI models in production. This includes GPUs, model serving frameworks, scaling systems, and networking designed to process real-time AI requests. Platforms like GMI Cloud provide infrastructure optimized for high-performance inference, enabling developers and companies to deploy LLMs, image models, video models, and other AI workloads reliably at scale.

Question 2

Why do companies need specialized infrastructure for AI inference?

Accepted Answer

Running AI inference at scale requires different infrastructure than traditional cloud workloads. AI models often need high-performance GPUs, optimized model serving engines, and efficient scheduling to reduce latency and cost. Dedicated inference infrastructure can provide better GPU utilization, predictable latency, and scalable deployment options compared with general-purpose cloud environments.

Question 3

What types of AI workloads can run on GMI Cloud?

Accepted Answer

GMI Cloud supports a wide range of AI workloads including large language models (LLMs), image generation, video generation, audio models, and other multimodal AI systems. Teams can deploy open-source models or custom models and run them through serverless APIs, dedicated endpoints, or GPU clusters depending on performance and scaling requirements.

Question 4

How do teams move from AI prototype to production deployment?

Accepted Answer

Moving from prototype to production typically requires infrastructure that supports reliable scaling, monitoring, and cost control. Developers often start with serverless inference APIs for experimentation and later transition to dedicated endpoints or GPU clusters for higher throughput and lower latency. Platforms like GMI Cloud allow teams to scale deployments without changing their application architecture.

Question 5

How can companies reduce the cost of large-scale AI inference?

Accepted Answer

AI inference cost can be optimized through efficient GPU utilization, batching strategies, and autoscaling infrastructure. By dynamically allocating GPU resources and scaling workloads based on traffic, teams can avoid paying for idle compute. Dedicated inference platforms also provide optimized model execution and resource scheduling to reduce overall cost compared with general-purpose cloud deployments.

MachineIntelligence

先用無伺服器 (Serveless) 推理上線，再隨需求無縫擴展.

超越 Serverless，進入真正可控的 AI 基礎架構

超越 Serverless，進入真正可控的 AI 基礎架構

GPU 租賃定價

NVIDIA H100

NVIDIA H200

NVIDIA Blackwell

規模化 AI 在 GMI Cloud 上表現更出色

以推理為核心而生

預設即為 Serverless

規模化下的穩定效能

為靈活擴展而打造

常見問題與技術支援

部署更快，推理更穩，擴展更輕鬆。

部署更快，推理更穩，擴展更輕鬆。

General|MachineMachineIntelligenceIntelligence

先用無伺服器 (Serveless) 推理上線，再隨需求無縫擴展.

超越 Serverless，進入真正可控的 AI 基礎架構

超越 Serverless，進入真正可控的 AI 基礎架構

GPU 租賃定價

NVIDIA H100

NVIDIA H200

NVIDIA Blackwell

規模化 AI 在 GMI Cloud 上表現更出色

以推理為核心而生

預設即為 Serverless

規模化下的穩定效能

為靈活擴展而打造

常見問題與技術支援

什麼是 AI 推理基礎設施？

為什麼企業需要專門的 AI 推理基礎設施？

哪些類型的 AI 工作負載可以在 GMI Cloud上運行？

團隊如何從 AI 原型走向正式環境部署？

企業如何降低大規模 AI 推理成本？

部署更快，推理更穩，擴展更輕鬆。

部署更快，推理更穩，擴展更輕鬆。

MachineIntelligence