At the inaugural vLLM Conference, a quiet consensus emerged among the engineers and architects who build the AI backbone: the era of pure batch inference is over. Separated prefill and decode services are no longer just an experimental optimization—they are becoming the only viable architecture for handling agentic traffic. The same shift is about to fracture the decentralized compute market, and most DePIN projects are not ready.
Agentic traffic is fundamentally different from the stateless, high-throughput batch jobs that GPU networks like Render, Akash, and io.net were designed to handle. Agents maintain multi-turn conversations, pause for tool calls, and require persistent context. The traditional collocated serving architecture, where prefill and decode share the same GPU, suffers from resource contention: prefill demands compute, decode demands memory bandwidth. Disaggregated serving separates these two phases onto different GPU pools, allowing independent scaling and optimization.
Context: Why Now?
The vLLM ecosystem, which powers a significant portion of open-source LLM inference, has been the primary testbed for this pivot. At the conference, multiple teams—Intel, Prime Intellect, AMD—independently converged on the same conclusion: collocated batch inference is breaking under the weight of agentic workloads. Meta, LinkedIn, and Mistral still run collocated in production, but the experimental prefill/decode separation in vLLM v0.8+ is already showing a 2.5x goodput improvement on 8x AMD MI300X nodes using the MORI-IO connector. This is a signal, not a verdict.
For decentralized compute networks, the implications are stark. Most DePIN GPU marketplaces operate on a spot-market model where providers offer raw compute, and the network scheduler assigns tasks to available nodes. This works well for batch jobs—users submit a static prompt, the GPU runs it, and the result is returned. But agentic workloads require stateful sessions: the KV cache must persist across turns, and routing must be sticky—a user’s subsequent requests must hit the same decode instance that holds their context. Current DePIN schedulers lack session awareness. They treat every request as independent, forcing the KV cache to be rebuilt from scratch each time, destroying the latency benefits of disaggregation.
Core: The Technical Breakdown Nobody Talks About
Based on my audit experience from the 2026 AI Agent Payment Protocol case, I saw firsthand how agentic traffic breaks naive batch processing. The protocol we audited allowed AI agents to autonomously execute crypto transactions. The payment routing logic was designed for high-throughput, low-value transfers, but the agent’s incentive structure encouraged spamming requests to drain gas fees. The same principle applies here: agentic traffic is not just high-volume—it is bursty, conversational, and context-dependent. Disaggregated serving addresses this by dedicating prefill nodes to handle the initial prompt and decode nodes to generate tokens sequentially. The KV cache, which can be gigabytes for long contexts, must be transferred between nodes via high-speed RDMA networks like InfiniBand or RoCE. vLLM uses NixlConnector for this, and AMD’s MORI-IO ties into its own hardware.
But here is the hidden cost: the network becomes the bottleneck. In a collocated architecture, the KV cache stays on the same GPU—no transfer overhead. In disaggregated serving, every token generation after the first turn requires the decode instance to retrieve the full KV cache from the prefill node or a distributed store. Prime Intellect’s approach, which uses distributed KV cache storage spanning CPU memory and NVMe, adds additional latency and complexity. For DePIN projects, which rely on heterogeneous nodes connected by public internet, achieving the sub-millisecond RDMA latency required for this architecture is nearly impossible. The result? Agentic workloads will gravitate toward centralized providers with dedicated high-speed interconnects, defeating the cost advantage of decentralized networks.
Contrarian: The Conventional Wisdom is Wrong
Most crypto analysts believe that decentralized compute will naturally win on price because GPU providers are fragmented and competition drives down costs. But disaggregated serving flips this logic. The architecture demands tight coupling between nodes—low-latency KV cache transfer, sticky routing, and session persistence. This favors vertically integrated providers like AWS or specialized clusters with homogeneous hardware and high-speed networking. DePIN networks, by design, are heterogeneous and loosely coupled. They cannot guarantee that the same GPU will be available for the next turn of a conversation, nor can they enforce the network topology needed for RDMA.
Worse, the 2.5x goodput advantage of AMD’s MI300X in this architecture is a double-edged sword. It proves that hardware optimization matters, but it also means that DePIN projects relying on NVIDIA GPUs will lose competitive ground. The pivot to disaggregated serving will accelerate hardware specialization, making general-purpose GPU marketplaces less attractive for agentic workloads. The real winners are not the DePIN projects but the hardware vendors who can tailor their chips to prefill/decode separation—AMD, and potentially custom ASICs.
Takeaway: What to Watch Next
The vLLM experimental flag for separated prefill/decode will drop in the next major release. When it does, the first production migration from a major user like Meta will trigger a cascade. For DePIN investors, the key signal is not total GPU hours but session retention and network latency. If a decentralized cluster cannot keep a session alive for 10 turns without breaking, it will be obsolete for the next wave of AI agents.
Due diligence is just paranoia with a spreadsheet. Start asking: Can your DePIN project offer sticky routing? Does it support RDMA? Is the KV cache persistent? If the answer is no, the infrastructure pivot will leave it behind.