AI Inference Acceleration Solution
Breaking through inference computing bottlenecks to accelerate AI adoption across industries.
Продукти, рішення, послуги для організацій
Breaking through inference computing bottlenecks to accelerate AI adoption across industries.
AI Inference Acceleration: Powering Enterprise AI Adoption
As well-trained models make their way into the real-world adoption, inference performance has become a core factor that affects user experience and the business value of the application itself. AI inference is no longer answering questions—it's stepping into the big leagues: analyzing lengthy documents, powering complex business decisions, and turning mountains of information into actionable insights. From extracting key points from a 10,000-word paper to guiding decisions based on 100-page medical guidelines, AI faces increasing challenges. It needs to master ultra-long texts, slash latency, handle massive concurrency, and cut down on repetitive computing. These capabilities will allow AI tools to become the go-to sidekick for industry professionals and fuel the intelligent transformation of industry.
Failed Inference
Long sequence inputs exceeding the model's context window force models to truncate or perform inference in batches, meaning full inference becomes impossible.Slow Inference
As sequence length increases, the time to first token (TTFT) increases and inference throughput decreases.Expensive Inference
KV cache cannot be continuously used, resulting in a large amount of repeated computing and high per-token computing cost.Benefits
10x Longer Context Window
Up to 90% Lower TTFT
22x Higher System Throughput
Architecture
Huawei AI Inference Acceleration Solution is built on OceanStor A series storage and comes equipped with Unified Cache Manager (UCM). The solution improves inference efficiency and experience by implementing hierarchical management and scheduling of full-lifecycle KV cache, helping accelerate AI adoption across industries.
Products
OceanStor A800
Using data-control plane separation architecture and long-term memory storage, this storage system fulfills the E2E data processing needs for AI training and inference in various industry sectors, such as financial credit, investment research, healthcare, and drug development.
Дізнатися більше
OceanStor A600
OceanStor A600 provides extreme performance density, accelerates inference, and fulfills the E2E data processing needs for AI training and inference. As such, it can be widely used in industry-specific scenarios such as financial investment and research, legal documents, medical record review, and drug R&D.
Дізнатися більше
You Might Be Interested
What are the major application scenarios of the AI inference acceleration solution?
The AI inference acceleration solution is mainly used in AI application scenarios in sectors such as carriers, finance, healthcare, and public services. It is well suited for inference workloads involving summarization, Q&A, and review based on long documents. For example, it can be used to generate financial investment research reports, analyze public opinion, provide self-service medical consultations, summarize scientific research documents, analyze government case files, answer policy-related questions, analyze enterprise network configurations, and plan and optimize network.
What is KV cache?
KV cache is a technology that caches the key and value vectors of generated text during Transformer inference. It is a core optimization for autoregressive generation and makes inference dozens of times faster by eliminating repeated computing during inference. However, this requires substantial amounts of GPU memory, making GPU memory a main bottleneck for long-context inference.
What is UCM?
Unified Cache Manager (UCM) is an open-source AI inference acceleration suite developed by Huawei. UCM uses KV cache and memory management to optimize token flows in each service phase through collaboration across the inference framework, compute, and storage. This addresses AI inference challenges of long-sequence processing, high latency, and high inference costs.
Copyright © 2026 Huawei Technologies Co., Ltd. Усі права захищені.
