So, I was looking at the infrastructure spending reports from this morning, and it is official. We have fully crossed the rubicon. As of this month, March twenty-twenty-six, more than half of every dollar spent on AI is now going toward just keeping the lights on. We are finally in the deployment era, where running the models is more expensive than training them.
Corn Poppleberry here, and you are hitting on the fundamental shift of the year. For so long, everyone was obsessed with the training phase, the massive clusters of H-one-hundreds grinding away for months. But now that these models are actually being used in production at scale, the focus has shifted to the efficiency of the inference itself. It is no longer about who has the biggest cluster for training; it is about who can serve the most tokens per second for the lowest cost.
Which brings us to today's prompt from Daniel. He wants us to dig into the technical reality of the AI runtime. It is that critical software layer sitting between the hardware and the model weights. And honestly, it is the piece of the puzzle that determines whether your application feels like a snappy conversation or a slow crawl through mud. It is the unsung hero of the AI stack.
It is the most overlooked part of the entire stack for people outside the engineering room. When you download a model from Hugging Face, you are getting the weights, which are basically just a massive, static file of numbers. They are the brain in a jar. But that brain cannot think until you have an engine to process those numbers. That is the runtime. It is the active software environment that loads those weights into memory, manages the GPU kernels, and orchestrates the mathematical operations required to turn your text into tokens.
I think a lot of people assume that if you have the weights and you have a beefy GPU, the rest is just magic. But you are saying the runtime is actually the nervous system that makes the brain functional. Without it, those weights are just dead weight.
Think of the weights like a musical score. It is all there on the paper—the notes, the rhythm, the structure. But the score does not make a sound. You need the orchestra, the instruments, and the conductor to actually turn that data into music. The runtime is the entire performance. And the reason we are seeing this massive spike in inference spending, over fifty-five percent of all AI infrastructure spend according to the latest Unified AI Hub reports, is because we are realizing that the runtime is where the performance battle is won or lost. If your conductor is slow or your violinists are out of sync, it does not matter how good the score is.
So let us get into the weeds then. Why are there so many of these things? If I am running a model locally, I am probably using something like Ollama. But if I am looking at a cloud deployment, I am hearing about v-L-L-M or T-G-I. Why can I not just use the same engine everywhere? Is it just a matter of preference, or is there a deeper architectural reason?
It comes down to what you are optimizing for. If you are running locally on your laptop, your biggest constraints are ease of use and hardware limitations. You might only have sixteen or thirty-two gigabytes of V-RAM. Runtimes like Ollama are built on top of the llama dot c-p-p library, which is a masterpiece of engineering for consumer hardware. It uses a format called G-G-U-F, which is a single-file format that includes both the weights and the metadata. It is designed to be portable and simple.
And G-G-U-F is the one that lets you do the offloading trick, right? Where you can shove some of the model onto your system memory if your GPU is too small? I have used that to run seventy-billion parameter models on a machine that definitely should not have been able to handle them.
That is the core advantage. It allows for flexible offloading between the C-P-U and the G-P-U. It is designed for the single user. It wants to get that model running with one command, even if you are on a Mac or a Windows machine with a mid-range card. But that convenience comes at a cost. You are usually optimizing for latency on a single stream of text. You want the words to appear quickly for you, the one person using it. You are not worried about ten thousand other people trying to use your laptop at the same time.
Right, but that does not work if you are a company trying to serve ten thousand users at once. You cannot just run ten thousand instances of Ollama. That would be like trying to run a city's power grid with ten thousand individual camping generators.
You would go bankrupt in a week. That is where production-grade runtimes like v-L-L-M come in. Developed at U-C Berkeley, v-L-L-M is built for high concurrency. Its claim to fame is something called PagedAttention. In traditional runtimes, the K-V cache, which is basically the short-term memory the model uses to remember the beginning of your sentence while it writes the end, is very fragmented and wasteful. It is like having a bunch of half-empty notebooks scattered across your desk.
I imagine that waste adds up when you have hundreds of users hitting the system simultaneously. If every user is taking up more memory than they need, you run out of space fast.
It is a disaster for memory efficiency. PagedAttention treats the K-V cache like virtual memory in an operating system. It breaks it into small blocks that can be stored non-contiguously. This allows v-L-L-M to pack way more requests into the same amount of G-P-U memory. The throughput difference is staggering. If you look at the March twenty-twenty-six benchmarks from Particula Tech, switching from a basic runtime to v-L-L-M on the same hardware can give you up to a sixteen-fold increase in throughput.
Sixteen times? That is not just a small optimization. That is the difference between needing one G-P-U or sixteen G-P-U-s to handle the same traffic. I can see why the cloud providers are obsessed with this. But what about the weight formats? You mentioned G-G-U-F for local stuff, but what are the big boys using? Does the format itself change how the runtime behaves?
In production, you are usually looking at Safetensors, which is the Hugging Face standard. It is designed to be secure and incredibly fast to load because it uses memory mapping. But you also see specialized quantization formats like A-W-Q or G-P-T-Q. These are ways of compressing the weights—turning sixteen-bit numbers into four-bit or even two-bit numbers—so they take up less space without losing too much intelligence. Different runtimes have different levels of support for these. A runtime like v-L-L-M is highly optimized for A-W-Q because it plays well with their memory management system.
It is interesting that the same G-P-U, let us say an R-T-X forty-ninety, can support all of these. I could run Ollama for my personal coding assistant in the morning, and then spin up a v-L-L-M instance to test an A-P-I in the afternoon. It is the same silicon, but it is behaving completely differently. It is like the hardware is a chameleon.
Because the runtime is using different kernels. Think of kernels as the low-level code that tells the G-P-U exactly how to do the math. A runtime like NVIDIA's Tensor-R-T L-L-M is the extreme version of this. It is a close-to-the-metal engine that uses kernel fusion. Instead of doing step A, then step B, then step C, it fuses those operations into a single massive step that stays on the G-P-U's fast memory. It avoids the bottleneck of moving data back and forth between different parts of the chip.
That sounds like the kind of thing that is great if you are locked into NVIDIA, but maybe a headache if you want to be flexible. If I spend all my time optimizing for Tensor-R-T, am I stuck with Team Green forever?
That is the ultimate trade-off. Tensor-R-T L-L-M will give you the absolute lowest latency on a Blackwell or Hopper chip, but you are married to NVIDIA. If you want to move that model to an A-M-D chip or an Intel accelerator, you are starting from scratch. You have to re-optimize everything.
Which leads us to O-N-N-X. The Open Neural Network Exchange. Every time I hear about it, people call it the universal translator of AI. Is that still the case in twenty-twenty-six, or has the industry moved past it in favor of these hyper-optimized, hardware-specific engines?
It is still the go-to for portability. If you are a developer and you want your model to run on a wide variety of hardware without writing custom code for every single chip, O-N-N-X is your best friend. But, and this is a big but, you almost always pay a performance tax. You are choosing a common denominator. You are not going to get that sixteen-x throughput boost or the deep kernel fusion of a hardware-specific runtime. It is a choice between flexibility and peak performance.
It sounds like the classic engineering problem. You can have it fast, you can have it cheap, or you can have it compatible with everything. Pick two. If you want the speed of Tensor-R-T, you lose the compatibility of O-N-N-X. If you want the ease of Ollama, you lose the throughput of v-L-L-M.
If you are building an edge device, like a smart camera or a local robot, you might go O-N-N-X because you do not know exactly what chip will be in the final hardware. But if you are Databricks or Snowflake, you are going to squeeze every drop of performance out of the hardware you own. You are going to go as close to the metal as possible.
Speaking of Databricks, I saw they just integrated the new R-T-X PRO forty-five-hundred Blackwell chips into their serverless runtime last week. They are specifically targeting what they call agentic AI. Why does the runtime matter so much for agents compared to just a regular chatbot? I mean, a token is a token, right?
Not quite. Agents are autonomous. They are not just waiting for you to type. They are constantly thinking, planning, and reacting in the background. They might be running loops where they generate a thought, check a database, generate another thought, and then take an action. That requires a runtime that can handle continuous, low-latency reasoning without breaking the bank. If your agent takes five seconds to decide its next move because the runtime is inefficient, the user experience falls apart. It feels like talking to someone who has to look up every word in a dictionary before they speak. Databricks is trying to minimize that overhead by tightly coupling the hardware signals with the inference engine.
It reminds me of the announcement from F-5 and NVIDIA about their Big-I-P Next system. They are using something called Dynamo runtime signals to route requests. If I understood that correctly, the network itself is now talking to the runtime to decide which G-P-U is the most efficient for a specific request in real-time. It is like the traffic lights are talking to the car engines to optimize the flow of the whole city.
It is a forty percent increase in token throughput just by being smarter about routing. We are getting to a point where the runtime is not just a siloed piece of software; it is part of the broader infrastructure fabric. It is talking to the load balancer, it is talking to the Kubernetes orchestrator. It is providing telemetry that we never had before.
And that brings us to the big news from today, March twenty-fifth, twenty-twenty-six. The Cloud Native Computing Foundation just published the Kubernetes AI Requirements, or K-A-R version one dot thirty-five. This feels like a major milestone for standardizing how these runtimes actually live in a cluster. We have been waiting for this for a long time.
It is huge. Before today, trying to scale something like v-L-L-M across a distributed cluster was a bit of a dark art. You would run into resource deadlocks where one node thought it had enough memory but the K-V cache would spike and crash the container. K-A-R provides a standard interface for runtimes to report their actual memory pressure and throughput capacity to the Kubernetes scheduler. It is like giving the scheduler a real-time map of the city's traffic instead of just a static list of roads.
So the scheduler actually knows that v-L-L-M is doing its PagedAttention magic and can pack more work onto that node without it blowing up. It can see the "virtual memory" of the G-P-U.
It makes inference a first-class citizen in the cloud-native world. We are moving away from the era of bespoke, hand-tuned AI servers and into the era of standardized, scalable inference fleets. This is what allows companies to treat AI models like any other microservice. You can spin them up, scale them down, and move them around without worrying about the underlying hardware quirks as much.
I love that term, inference fleets. It makes it sound much more industrial, which I guess is where we are. We have gone from the hobbyist playing with llama dot c-p-p on a laptop to these massive, automated fleets. But for the listener who is trying to decide what to use today, how do they navigate this? If you are a developer sitting down to build an app, where do you start? We need a decision matrix.
I think you have to look at your user count first. If you are building something for yourself or a very small team, do not overcomplicate it. Use Ollama. Use the G-G-U-F format. The ease of setup is worth the performance trade-off because your time is more expensive than the extra milliseconds of latency. You can get a model running in sixty seconds. That is a huge win for productivity.
And if you are looking at an actual product? Something with users who expect a fast response and you do not want your cloud bill to look like a phone number?
Then you have to look at v-L-L-M or T-G-I. If you are running on NVIDIA hardware, which most of the world still is, v-L-L-M is the gold standard for high-concurrency throughput. You want those PagedAttention benefits. You want to be able to serve a hundred users on a single card instead of just five. But if you are in a high-security environment or you have a very specific latency requirement, like high-frequency trading or real-time medical imaging, then you spend the time to implement Tensor-R-T L-L-M. You go close to the metal. You pay the "complexity tax" to get the "performance rebate."
What about the O-N-N-X crowd? Is there still a strong case for that universal translator approach in this world of specialized chips?
Especially for enterprise software that needs to run on-premise for various clients. If you sell software to a bank, you do not know if they have a rack of NVIDIA cards or a bunch of Intel Gaudi accelerators. O-N-N-X gives you that insurance policy. You write the implementation once, and it runs anywhere, even if it is not the fastest version possible. It is about market reach and reducing maintenance overhead.
It is funny to think that we used to just talk about the models. We would spend hours debating Llama versus Claude versus Gemini. Now, the conversation is becoming much more like traditional software engineering. It is about memory management, kernel optimization, and network routing. The AI part is almost becoming the easy part. The "intelligence" is a given; the "execution" is the challenge.
The model weights are becoming a commodity. Everyone has a great seven-billion or seventy-billion parameter model now. The real competitive advantage is becoming the operational efficiency. How cheaply and how fast can you run that model? That is why the runtime is the new operating system of the AI age. It is the layer that manages the resources and provides the services that the "application"—the model—needs to function.
It is also worth mentioning that this connects back to what we talked about in episode fourteen-seventy-nine, about the speed of thought. As inference gets faster and cheaper, the way we interact with AI changes. We move from these long, slow prompts to these rapid-fire, agentic interactions where the AI is basically thinking in real-time alongside us. We are moving from "batch processing" our thoughts to "stream processing" them.
And you cannot do that without a runtime that can handle the pressure. If you are interested in how the infrastructure above the runtime is evolving, you should definitely check out episode eight-hundred-forty-one where we talked about AI gateways and Lite-L-L-M. That is the layer that sits on top of these runtimes to handle things like failover and load balancing. It is the traffic controller for your inference fleet.
It is a whole new stack. From the G-P-U at the bottom, through the runtime, up to the gateway, and finally to the user. It is getting complex, but it is also getting incredibly powerful. I think the takeaway for me is that if you are still just thinking about the weights, you are only seeing half the picture. The weights are the potential energy; the runtime is the kinetic energy.
It is the difference between having a blueprint for a car and actually having an engine that can turn fuel into motion. You can have the best blueprint in the world, but if your engine is seized up, you are not going anywhere.
We are finally getting to the point where the engines are becoming reliable enough for the mass market. I am curious to see if we eventually see a consolidation here. Do you think we will end up with one runtime to rule them all, or will it stay this fragmented? Will we see a "Linux of AI Runtimes"?
I think the fragmentation is a feature, not a bug. Hardware is diversifying. We have N-P-U-s in phones, specialized AI chips in the cloud, and traditional G-P-U-s. As long as the hardware stays diverse, the runtimes will have to stay diverse to squeeze the performance out of them. We might see better abstractions, like what the CNCF is doing with K-A-R, but the low-level engines will always need to be specialized. You do not use a Formula One engine in a tractor, even if they both run on fuel.
Well, I for one am glad there are people like you who enjoy reading white papers about PagedAttention so I do not have to. I will stick to making the jokes and asking the annoying questions. It is a division of labor that works for me.
Someone has to keep us grounded in the reality of the silicon, Herman. It is easy to get lost in the "magic" of AI and forget that it is all just electrons moving through gates at the end of the day.
Fair enough. I think we have given Daniel a pretty thorough breakdown of the landscape. It is a fast-moving target, especially with the news coming out of the Kubernetes world today. It feels like every week there is a new benchmark or a new standard that shifts the goalposts.
The K-A-R one dot thirty-five spec is going to change how a lot of people think about their clusters over the next few months. It is a good time to be an infrastructure nerd. We are finally getting the tools we need to build real, industrial-scale AI systems.
Is there ever a bad time to be an infrastructure nerd in your world, Corn? You seem to find excitement in the most obscure configuration files.
Not since twenty-twenty-two, that is for sure. The pace of innovation in the plumbing of AI is just as fast as the innovation in the models themselves.
Alright, let us wrap this one up. We have covered the shift to the deployment era, the difference between local and cloud runtimes, why your choice of weight format matters, and the trade-offs between flexibility and raw speed. We have looked at the new Blackwell chips, the Dynamo routing signals, and the new Kubernetes standards.
And don't forget the K-V cache. Always mind your K-V cache. It is the most expensive memory you own.
I will try to keep mine as unfragmented as possible, though my brain might disagree after all this technical talk. Thanks as always to our producer, Hilbert Flumingtop, for keeping the show running smoothly behind the scenes.
And a big thanks to Modal for providing the G-P-U credits that power our research and this show. Their serverless platform is actually a great example of how this runtime management can be abstracted away for developers. They handle the cold starts and the kernel optimizations so you can just focus on the code.
This has been My Weird Prompts. If you are enjoying the deep dives into the guts of the AI revolution, search for My Weird Prompts on Telegram to get notified the second a new episode drops. We have a lot more ground to cover as we head into the rest of twenty-twenty-six.
We will be back soon with more explorations into the weird and wonderful world of AI. There is always another layer of the stack to peel back.
See you then.
Goodbye.
