Anthropic's $1.8 billion, seven-year agreement to use the Akamai network shows the new uses of edge computing for frontier language models.
To be sure, low latency remains the paramount value, as has been the case for content providers for decades. But where traditional content delivery networks have largely been about edge storage, AI models benefit more from edge compute for inference operations.
Where traditional content delivery minimized latency by storing popular content at the edge, AI inference operations benefit by conducting inference operations at the edge.
Content providers such as Netflix or Pandora deliver mostly pre-encoded, cacheable files (videos, audio) that can be pre-positioned at edges with high hit rates.
In contrast, AI inference operations are:
highly dynamic, as
each prompt is unique
limiting the value of caching
so the value shifts more to distributed compute and intelligent routing.
But edge computing also helps with scale and demand spikes. CDNs also provide such features as:
global load balancing
auto-scaling across points of presence
burst capacity without over-provisioning central clusters
reduced data transfer (less backhaul to origin)
efficient routing
potential caching of reusable elements
lower bandwidth and egress costs
Traditional CDNs provided value by moving content bits efficiently.
For frontier AI models, the value comes from edge computation.
Lower latency is still the chief value, though.
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