AI Inference Acceleration Solution
Breaking through inference computing bottlenecks to accelerate AI adoption across industries.
Produkty, Rozwiązania i usługi dla przedsiębiorstw
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.
Slow Inference
As sequence length and concurrency increase, the time to first token (TTFT) increases and inference throughput decreases.Expensive Inference
Lack of key-value (KV) cache persistence leads to significant repeated computing and high per-token computing cost.
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 experience by implementing hierarchical management and scheduling of full-lifecycle KV cache, helping achieve faster and more efficient inference and accelerating AI adoption across industries.
Benefits
Up to 90% Lower TTFT
In multi-turn Q&A and industry summary and analysis scenarios, the KV hit rate of the prefix cache algorithm exceeds 90% and TTFT is greatly reduced.
2x Higher System Throughput
The prefill phase eliminates repeated computing via querying based on historical inference data. The decode phase uses intelligent association to improve the system throughput while dramatically lowering per-token costs.
Related 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.
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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.
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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.
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