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简化大规模智能体人工智能的部署.pdf

上传人: 明**** 编号:1011947 2025-12-21 14页 1.46MB

1、Tim ZhouOct 14th,2025Simplifying the Deployment of Agentic AI at ScaleCopyright 2023 Accton Technology Corporation.All rights reserved.Inference10%Training90%2023Inference90%Training10%203010%10%InferenceInference90%90%InferenceInferenceEnterprise GenAI accelerated Retooling workflows,automate cross

2、-functional processes,contextual reasoning across departments Data privacy and data sovereignty will be critical for Enterprise Hybrid training in cloud and Inference onprem Enterprise-Grade Infrastructure will be required Power real-time decision-making without latency bottlenecksSource:Jeff Clarke

3、,COO,Dell Technologies-DTW24Source:https:/ organizations piloting Gen AI75%of organizations piloting Gen AI.Only 9%are deploying.1On-prem inference demands dynamic and flexible GPU solutionsdemands dynamic and flexible GPU solutions83%of All Data is On-PremGenAI is retooling Enterprise50%of This Dat

4、a is Generated at the Edge Enterprise demands AI to deliver tangible business outcomesTAM$100BEnterprise GenAIEnterprise GenAIAgentic AI in the Enterprise:From Chatbots to Autonomous WorkflowsBeyond Q&A:multi-agent planners with tool-use,RAG,and cross-function task orchestrationDepartmental on-prem

5、inference(HR,Finance,R&D,Ops)for latency,privacy,sovereigntyLong-context LLMs(e.g.,120K-token prompts)TB-scale KV-cache;strict TTFT/tokens-per-sec SLAsMulti-tenancy:per-tenant isolation,quotas,and policy guardrails across shared GPU poolsGovernance&audit:per-tenant usage,cost,and SLO tracking to ena

6、ble chargeback/showbackDesign goal:empower business workflows while hiding infra complexity behind templates&APIsEnterprise Reality:What We See in the FieldOn-premise inference drivers(incl Jurisdiction)On-premise inference drivers(incl Jurisdiction)Multi-tenancy RequirementsMulti-tenancy Requiremen

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根据报告的内容,全文主要内容概括如下: - **企业AI部署现状**:目前只有不到9%的企业在部署通用人工智能(Gen AI),而75%的数据存储在本地。 - **挑战与需求**:企业需要AI来产生实际业务成果,但面临数据隐私、主权、集成复杂性和长期支持等挑战。 - **解决方案**:需要一种新的架构,包括无缝集成、AI驱动的编排和适用于实际工作负载的准备。 - **关键点**: - 企业AI部署率低,仅为9%。 - 83%的数据存储在本地,50%的数据在边缘生成。 - 需要混合云训练和本地推理,以及企业级基础设施。 - 需要支持多租户、治理和审计。 - 需要简化部署,提供高性能和灵活性。
"企业AI部署难题?" "AI落地,基础设施选哪家?" "AI时代,如何降低TCO?"
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