Let’s start with the obvious: GPUs are expensive, and GPUs at massive scale are prohibitively expensive.
That's been the harsh, consistent truth of deploying LLMs, which thrive on enormous computational resources.
But recently, NVIDIA nudged the door open on a much simpler and more efficient way of doing inference at scale, something they've dubbed Dynamo.
At first glance, it sounds like yet another framework in a sea of optimization tools, but dig a bit deeper and it quickly becomes clear that this isn’t a mere tweak; it’s an actual rethink of how inference is orchestrated.
The technical narrative here revolves around three big, intertwined moves: disaggregation, smart KV cache management, and intelligent task routing. The terms are deceptively simple and commonly bandied about yet in this case are packed with nuance and complexity.
Disaggregation Explained (and Why It’s a Bigger Deal Than You Think)
Traditionally, inference workloads follow a neat trajectory: you prefill the model (building the context from input), and then decode tokens, sequentially, based on this context.
In even simpler (and more appealing) terms, imagine the inference pipeline as a busy kitchen in a crowded restaurant. The prefill (that initial context-building) and decode (token generation) phases are cramped into the same cooking station, constantly bumping elbows.
Dynamo, on the other hand, splits these phases onto separate workstations entirely, each optimized specifically for its particular tasks. The line cooks stop fighting for counter space. Everything speeds up.
The concrete impact of this split is profound," NVIDIA Dynamo Architect, Kyle Kranen says in the interview below. “Just splitting apart these two things on LLaMA-70B... yields over two times increase in the total number of tokens generated per second.” This isn’t incremental efficiency is doubling throughput simply by rearranging how work is assigned.
The nuance here isn’t trivial. Prefill and decode aren’t symmetrical tasks. Prefill involves generating key-value (KV) states, so basically building the deep context necessary for attention calculations. Decode, meanwhile, rapidly produces subsequent tokens. Each phase thrives in its own special GPU space.
In sum, disaggregation is that magic that lets you precisely calibrate resources to workload demands, targeting throughput without inflating latency. It's a subtle architectural shift with seriously large benefits.
The KV Cache: Smarter Storage, Fewer Headaches
If disaggregation is Dynamo’s backbone, the smart management of the KV cache is its brain.
In short, KV caches hold pre-crunched attention states. Each inference request, especially those with large context, creates enormous volumes of key-value pairs. Typically, re-computing these caches over and over for similar tasks is wasteful. Dynamo takes this on directly, using elegant hierarchical caching strategies to keep these precious computational nuggets intact and ready for reuse.
"You have this hierarchy of cache where the HBM (GPU memory)...is really fast...then host memory, and cold storage on disks or NVMe,” Kranen explains, which if you visualize it is a pyramid:
At the top is ultra-fast GPU memory (precious and limited); in the middle, plentiful host RAM; and at the bottom, cheaper NVMe storage. Dynamo intelligently pushes rarely-accessed KV caches downward, freeing the pricey memory and cutting down on all that recomputation.
The importance emerges most clearly with longer input sequences. When sequences are short, recomputation is manageable, even trivial. But as Kranen underscores, "As the sequence gets longer...it becomes even more advantageous to move stuff to storage.” He explains that quadratic growth in attention costs becomes linear retrieval cost, radically reshaping the economics and feasibility of long-context inference workloads.
Kranen points to the example of a sprawling codebase where the KV cache for context (source files, documentation, past interactions) might be enormous. Recomputing context states every time is madness. Dynamo’s hierarchical caching sidesteps this madness entirely, pulling precomputed states out of cheap storage exactly when needed, turning an impossible computational scenario into a routine operation.
Dynamo Also Plays Traffic Cop
And so if disaggregation and KV cache handling are Dynamo’s structure and memory, the intelligent router is its logistical mastermind.
Dynamo’s router isn't some naive dispatcher blindly pushing tasks around; it makes decisions informed by millions of real-time events.
“It tracks millions of KV events from thousands of instances…you can minimize your work because 70% of it's already done on this decode node,” says Kranen.
The router sees the entire computational map. It knows exactly where KV caches live, how loaded GPUs are, and how incoming requests match existing caches. Instead of blindly re-computing or redundantly duplicating KV caches, it directs tasks exactly to machines already primed with the needed context.
Think of Dynamo’s router as the best imaginable traffic controller, handling thousands of requests with nuanced precision, constantly redirecting traffic to clear computational highways. It's quietly powerful. Rather than creating bottlenecks, it consistently dissolves them, ensuring the entire distributed inference operation moves swiftly.
Real World Numbers & Gains?
So your practical question probably becomes: Does Dynamo’s technical elegance actually translate into meaningful performance improvements? Kranen’s short answer: absolutely.
"At around 300 tokens per second per user, you can generate 30 times more tokens per normalized per GPU,” he cites. 30X isn’t a modest increment. It’s a quantum leap, a practical demonstration of what thoughtful architectural optimization can achieve.
The other thing he notes is that Dynamo isn't rigidly tied to one configuration. Because disaggregation separates prefill from decode phases, it lets you flexibly scale GPU resources independently for each phase. Sudden burst in prefill-heavy workloads? Simply scale prefill GPUs up. Decode-heavy task demands? Dynamically adjust decode GPUs accordingly.
This kind of adaptive flexibility was historically elusive, forcing awkward compromises, Kranen points out, adding that Dynamo’s architectural agility means no more compromises–each task runs on precisely the GPU resources it actually needs.
Finally, Why Dynamo Matters (Beyond GPU Utilization)
Stepping back from the fine-grained technicalities, the deeper narrative here is about accessibility.
By drastically cutting the cost-per-token and latency for inference workloads, Dynamo isn't just a performance play. As Kranen argues, it unlocks applications previously blocked by computational impracticality—deep, long-context conversations, huge code retrieval scenarios, and agentic automation at industrial scales.
As inference demands explode, especially as we deploy increasingly autonomous agents generating immense token volumes, Dynamo positions itself not as a luxury, but as critical infrastructure.
This isn't just a feel-good marketing yarn about incremental improvements. Dynamo fundamentally rewrites inference mechanics, from GPU orchestration and KV caching strategy, all the way down to intelligent routing decisions. And in doing so, it positions inference to evolve from a brute-force expense into something more sophisticated and flexible.
Dynamo tells a story developers actually want to hear: It's technical yet intuitive, complex but deeply human—because ultimately, behind every efficiency and performance metric is someone building something extraordinary. And Dynamo, in its dry but quietly revolutionary way, just made that a whole lot easier
But GPU Efficiency Alone Won't Solve Your Massive-Scale Data Problem
The thing about Dynamo is that it’s fundamentally a GPU-bound creature.
Yep, it’s brilliant at rapidly shuffling caches around between memory hierarchies to make inference faster and cheaper at the hot, compute-heavy tip of the stack. But if you zoom out (say, to the scale of entire codebases, sprawling datasets, or persistent AI-driven services churning through context windows hundreds of k’s of tokens deep) you're quickly confronted by a different, deeper problem altogether.
Here, you need a data fabric that operates at massive, persistent scale: something built explicitly to handle multi-petabyte pools of cached key-value states without breaking stride or budget, something architected not merely to keep pace with GPUs but to feed them steadily and predictably at bandwidth GPUs actually notice.
Dynamo alone won't solve this. It wasn't built to.
Dynamo moves fast and close; but behind it, beneath it, at a scale where temporary caches vanish into irrelevance, you need infrastructure that doesn’t flinch when asked to serve data by the petabyte at GPU speeds…And hold onto it, too, with the kind of durable persistence that transforms fleeting data into real, lasting value.
And maybe that's the real story here. Dynamo brilliantly solves the hottest, fastest-moving problems at the GPU level, but true, transformative infrastructure requires thinking even deeper—beyond temporary caches, down to the very bones of your data architecture.
Dynamo's Open Future (and Yours)
And also importantly, Dynamo is fully open source, it’s a genuinely community-driven project with transparent roadmaps and visible codebases.
NVIDIA isn't just waving hands about this, they've put the entire tech stack into public hands, effectively inviting collaboration and community-driven improvement. In Kranen’s own words, the Dynamo team is committed.
"We want to enable as many people as possible...whatever people want to do inference with, we want to enable.”