显存除了待机都是满载跑吗：KV Cache 如何撑起推理的显存水位

TITLE: How Does KV Cache Maintain High Memory Levels for Inference: Enhancing Memory Watermarks with KV Cache

DESC: This article discusses the role of KV Cache in boosting memory levels for large model inference and analyzes its performance optimization effects.

SLUG: kv-cache-memory-level-up

Introduction

Memory watermarks are a critical factor affecting the performance of large model inference. As a novel storage acceleration technology, KV Cache can significantly enhance memory levels, thereby improving inference efficiency. This article explores the role of KV Cache in maintaining high memory levels for inference and analyzes its performance optimization effects.

Introduction to KV Cache Technology

KV Cache is a storage acceleration technology based on key-value pairs (KVPs). It stores frequently accessed data in a high-speed cache, reducing the number of accesses to main memory and disk, thereby increasing data access speed. KV Cache offers the following advantages in the inference scenario:

• Reduced Memory Pressure: By storing frequently accessed data in the cache, KV Cache effectively reduces memory usage pressure, enhancing memory levels.
• Increased Data Access Speed: KV Cache utilizes high-speed caching, with data access speeds far surpassing those of main memory and disk, thereby improving inference efficiency.
• Lower Latency: KV Cache can reduce the latency of data access, enhancing the real-time performance of inference.

Performance Optimization Effects of KV Cache

Xinyuanyi Technology's measured data shows that KV Cache has a significant effect on boosting memory levels for large model inference. The following are some key indicators:

• KV Tiered Acceleration Inference Throughput Improvement: +29–40%. Under the 480B production deployment form long context cold recovery load, concurrent 8 tiers improve by 29% (lower bound), and the optimal operating point concurrent 16 tiers improve by 40% (upper bound), with TP4×2 full machine scale improving by 35–36%【source: R2/R3 measured】.
• First Token Latency Reduction: TTFT ↓26–32%. Under the 480B·TP8 three-tier concurrent scenario, TTFT p50 decreases from 10.17–35.73s to 7.53–26.35s【source: R2 measured】.
• Acceleration Factor for Recalculations Without External Storage: 8.6–20×. The recalculating baseline TTFT p50 of 149.5s (conc16) is compared to FX100's 11.85s; throughput is 4.1 against 74.9 tok/s【source: R2 measured】.

Application Scenarios of KV Cache

KV Cache has extensive application value in the following scenarios:

• Large Model Inference: KV Cache can effectively enhance memory levels for large model inference, improving inference efficiency.
• High-Performance Computing: KV Cache can reduce data access latency in high-performance computing, improving computational efficiency.
• Artificial Intelligence Applications: KV Cache can enhance data access speed for artificial intelligence applications, improving application performance.

Conclusion

As a novel storage acceleration technology, KV Cache has a significant effect on maintaining high memory levels for large model inference. Xinyuanyi Technology's measured data indicates that KV Cache can significantly improve inference efficiency and performance. In the future, KV Cache will be applied in more fields, providing strong support for the development of artificial intelligence.

Key Points and FAQs

Q: How does KV Cache enhance memory levels? A: KV Cache enhances memory levels by storing frequently accessed data in a high-speed cache, reducing the number of accesses to main memory and disk, thereby lowering memory usage pressure and improving memory levels.

Q: What are the performance optimization effects of KV Cache? A: Xinyuanyi Technology's measured data shows that KV Cache has a significant effect on boosting memory levels for large model inference, such as a 29–40% improvement in KV tiered acceleration inference throughput and a 26–32% reduction in first token latency.

Q: What are the application scenarios of KV Cache? A: KV Cache has extensive application value in scenarios such as large model inference, high-performance computing, and artificial intelligence applications.