The market is fixated on token prices. It is missing the infrastructure war unfolding beneath the surface.
In late 2025, the first vLLM Conference, held alongside Ray Summit in San Francisco, revealed a consensus that had been quietly forming among AI infrastructure teams: the era of batch inference is ending. Not because of hardware, but because of traffic. Agentic workloads — multi-turn, stateful, tool-calling — are breaking the paradigm that served the industry for years.
This is not a story about AI. It is a story about capital allocation. The same dynamics that reshaped crypto infrastructure in 2020, when DeFi composability forced a rethink of liquidity pools, are now repeating in the compute layer. The question is not whether disaggregated serving will arrive. It is whether the crypto-native compute networks — Akash, Render, io.net — are positioned to capture the value or will be left holding general-purpose GPUs that no longer fit the specialized demand.
Context: The Architecture Gap
For the past two years, decentralized physical infrastructure networks (DePINs) have competed on a simple metric: raw GPU hours. Supply was commoditized. Demand came from training and batch inference. The business model was straightforward: rent out a GPU, get paid in tokens.
Agentic workloads change the equation. A single agent session can involve dozens of turns, each requiring a prompt and a generation. The model must retain context across turns — what is called the KV cache. In batch inference, that cache is ephemeral, discarded after each request. In agentic traffic, the cache must persist across multiple requests, sometimes for minutes or hours. The result is a fundamentally different compute profile: prefill (compute-intensive) and decode (memory-bandwidth-intensive) become decoupled phases that cannot be efficiently co-located on the same GPU without causing resource contention.
Multiple teams independently converged on the same solution: disaggregated serving. Intel demonstrated prefill/decode decoupling on its hardware. Prime Intellect applied the same principle to trillion-parameter MoE models. AMD’s MORI-IO connector delivered 2.5x higher goodput on 8x MI300X nodes. The pattern is clear. The infrastructure is being redesigned from the ground up for agentic traffic.
But here is the catch — the current production users at Meta, LinkedIn, Mistral, and Hugging Face are still running the old collocated architecture. The vLLM disaggregated prefill feature remains experimental. The shift has not yet happened at scale. It is a pre-mortem of a future that has not arrived, but whose arrival is structurally inevitable.
Core: The Second-Order Effects on Crypto Compute
Liquidity is the pulse; policy is the brain. In crypto compute, the liquidity is GPU time. The policy is the scheduling algorithm. Disaggregated serving injects a new vector of complexity: the network.
Agentic inference requires the KV cache to be transmitted across nodes — from the prefill cluster to the decode cluster. This transmission is not trivial. It relies on RDMA (Remote Direct Memory Access) networks. The vLLM ecosystem has already developed two connectors: NixlConnector for RDMA-based transport and MORI-IO for AMD hardware. The network bandwidth and latency become the new bottlenecks.
For crypto DePINs, this is both a threat and an opportunity. The threat: most decentralized GPU networks are built on consumer-grade infrastructure with variable latency. They are optimized for batch processing, not for low-latency cache transfers. If a decode instance needs to fetch a 128K-token KV cache from a prefill node across the world, the latency will kill the user experience. The opportunity: a well-designed decentralized network can offer geographic affinity — matching prefill and decode clusters in the same region — and potentially lower cost than centralized cloud providers that charge premium for cross-region bandwidth.
But the real opportunity lies in the middleware layer. Disaggregated serving introduces a new component: the router. The vLLM Router uses consistent hashing and sticky routing to ensure that all requests from the same session go to the same decode instance. This is a stateful scheduling problem. In crypto terms, it is a consensus problem — who decides which instance holds the session state, and how is that state replicated across failures?
I have seen this pattern before. During DeFi Summer in 2020, I built a “DeFi Liquidity Multiplier” metric that predicted cascade failures when leverage became too concentrated. The same logic applies here: the router is the new bottleneck. If it fails, sessions are lost. If it is centralized, it becomes a single point of control. A decentralized router — one that uses a DHT or a blockchain-based registry to coordinate session state across nodes — could be the next key infrastructure primitive.
Value is a consensus, not a fundamental truth. The market currently values GPU compute on a per-hour basis. Disaggregated serving will shift the value to per-session, and then to per-cache. The entity that holds the KV cache holds the user’s context. That context is valuable for personalization, for fine-tuning, for advertising. The tokenization of KV cache — where users pay for the storage and retrieval of their session state — is a logical extension. This is not science fiction. Prime Intellect is already using distributed KV cache storage. The question is who will build the economic layer on top.
Contrarian: The Decoupling Thesis That Nobody Is Talking About
The consensus narrative is that AI and crypto are converging. Everyone points to decentralized compute as the bridge. I argue the opposite: disaggregated serving will accelerate the decoupling of AI infrastructure from crypto, not strengthen it.
Here is why. The disaggregated architecture demands high-bandwidth, low-latency networking. The best infrastructure for this today is centralized cloud: AWS, GCP, Azure. They already have RDMA fabrics, regional clusters, and mature scheduling. DePIN networks, by contrast, are built on heterogeneous hardware connected by public internet. The latency jitter alone makes consistent sticky routing nearly impossible.
To compete, decentralized compute networks will need to aggregate hardware into tightly coupled clusters — essentially becoming mini-clouds. That undermines the very premise of decentralization: permissionless participation. If a node must be in a specific data center with specific network gear to qualify for agentic workloads, then the network is no longer a global compute market. It is a curated pool.
The contrarian thesis: the disaggregation trend will actually centralize AI inference into the hands of a few cloud providers, and crypto compute will be relegated to the low-margin batch inference market. The “agent era” will be a boon for AWS, not for Akash. Only if decentralized networks solve the session routing problem — and they have not yet — will the opposite hold.
Takeaway: Positioning for the Next Cycle
I have been watching infrastructure shifts for 22 years. The 2017 ICO mania taught me to audit tokenomics before narratives. The 2020 DeFi summer taught me to map second-order effects. The 2021 NFT wash-trading taught me to distrust volume metrics. The 2022 Terra collapse taught me to run pre-mortems.
This is a pre-mortem moment. The disaggregated serving trend is real. The vLLM conference evidence is strong. But the migration has not started. The capital expenditure required — more GPUs, faster networks, stateful routers — is significant. The return on that investment depends on agentic traffic growth, which is unproven at scale.
My advice: do not chase the infrastructure narrative. Wait for the signal. The signal is when a major production user — Meta, LinkedIn, or a top-tier crypto AI project — announces a public migration to disaggregated serving. Until then, this is a technically sound but commercially unvalidated thesis. Trust the math, doubt the narrative. The math says the architecture is better. The narrative says it will dominate. The gap between the two is where fortunes are made or lost.