AlayaDB: The Data Foundation for Efficient and Effective Long-context
  LLM Inference
AlayaDB is a cutting-edge vector database system natively architected for
efficient and effective long-context inference for Large Language Models (LLMs)
at AlayaDB AI. Specifically, it decouples the KV cache and attention
computation from the LLM inference systems, and encapsulates them into a novel
vector database system. For the Model as a Service providers (MaaS), AlayaDB
consumes fewer hardware resources and offers higher generation quality for
various workloads with different kinds of Service Level Objectives (SLOs), when
comparing with the existing alternative solutions (e.g., KV cache
disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that
it abstracts the attention computation and cache management for LLM inference
into a query processing procedure, and optimizes the performance via a native
query optimizer. In this work, we demonstrate the effectiveness of AlayaDB via
(i) three use cases from our industry partners, and (ii) extensive experimental
results on LLM inference benchmarks.
67ff772061373fdf16ce1d38
Yangshen Deng
claimed_verified
2025-04-16T11:57:58.302Z
66486ba1640bc89c93bcc8a2
/avatars/07e07bd768339495e55b7b17f2f8bc59.svg
Yangshen Deng
user
YangshenDeng
67ff772061373fdf16ce1d39
Zhengxin You
67ff772061373fdf16ce1d3a
Long Xiang
claimed_verified
2025-04-17T11:10:34.914Z
647dd5a6becb41a272925b5e
/avatars/9ec9a3357ac43dcbe350fb3b72f3dbc4.svg
Long Xiang
user
BenjaminXIANG
67ff772061373fdf16ce1d3b
Qilong Li
admin_assigned
2025-04-17T08:16:16.665Z
66d6be0bddf54fd90923c727
/avatars/7bb82c8c339db944d79d47b3b9b35aa8.svg
Li
user
Qilong00
67ff772061373fdf16ce1d3c
Peiqi Yuan
admin_assigned
2025-04-17T08:16:22.829Z
66efe50667c4ce2c9024cd45
/avatars/7b7f650953c371f08a5beecc500b6a43.svg
peiqiyuan
user
YuanPeiqi
67ff772061373fdf16ce1d3d
Zhaoyang Hong
67ff772061373fdf16ce1d3e
Yitao Zheng
admin_assigned
2025-04-17T08:16:36.808Z
66d6c339b61dd11022907252
/avatars/d2ed3cc003e94b2e5204ce0f8a481dcf.svg
Yitao Zheng
user
FeTieTer
67ff772061373fdf16ce1d3f
Wanting Li
67ff772061373fdf16ce1d40
Runzhong Li
admin_assigned
2025-04-17T08:17:02.031Z
671a7bce2b10d343bab18637
/avatars/af1c10a59236d953b42e67d3955eecc4.svg
runzhong
user
runzhongli
67ff772061373fdf16ce1d41
Haotian Liu
admin_assigned
2025-04-17T08:17:25.728Z
63898b61ec1f539adc0f4da2
https://cdn-avatars.huggingface.co/v1/production/uploads/1674248167280-63898b61ec1f539adc0f4da2.jpeg
Haotian Liu
user
liuhaotian
67ff772061373fdf16ce1d42
Kyriakos Mouratidis
67ff772061373fdf16ce1d43
Man Lung Yiu
67ff772061373fdf16ce1d44
Huan Li
67ff772061373fdf16ce1d45
Qiaomu Shen
67ff772061373fdf16ce1d46
Rui Mao
67ff772061373fdf16ce1d47
Bo Tang
admin_assigned
2025-04-17T08:18:01.460Z
66b02a2642c34e7a212133c0
/avatars/737a69b095e8c427ecd08f870b173635.svg
Bo Tang
user
BO1022
AlayaDB: The Data Foundation for Efficient and Effective Long-context
  LLM Inference
AlayaDB is a cutting-edge vector database system natively architected for
efficient and effective long-context inference for Large Language Models (LLMs)
at AlayaDB AI. Specifically, it decouples the KV cache and attention
computation from the LLM inference systems, and encapsulates them into a novel
vector database system. For the Model as a Service providers (MaaS), AlayaDB
consumes fewer hardware resources and offers higher generation quality for
various workloads with different kinds of Service Level Objectives (SLOs), when
comparing with the existing alternative solutions (e.g., KV cache
disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that
it abstracts the attention computation and cache management for LLM inference
into a query processing procedure, and optimizes the performance via a native
query optimizer. In this work, we demonstrate the effectiveness of AlayaDB via
(i) three use cases from our industry partners, and (ii) extensive experimental
results on LLM inference benchmarks.
AlayaDB is a vector database system that decouples KV cache and attention computation from LLMs, optimizing inference for higher quality and reduced hardware usage.
KV cache attention computation vector database Model as a Service (MaaS) Service Level Objectives (SLOs) query processing procedure native query optimizer Large Language Models (LLMs)