Sure, they have huge GPU clusters, but there must be more going on - model optimizations, sharding, custom hardware, clever load balancing, etc.
What engineering tricks make this possible at such massive scale while keeping latency low?
Curious to hear insights from people who've built large-scale ML systems.
However I can share this written by my colleagues! You'll find great explanations about accelerator architectures and the considerations made to make things fast.
https://jax-ml.github.io/scaling-book/
In particular your questions are around inference which is the focus of this chapter https://jax-ml.github.io/scaling-book/inference/
Edit: Another great resource to look at is the unsloth guides. These folks are incredibly good at getting deep into various models and finding optimizations, and they're very good at writing it up. Here's the Gemma 3n guide, and you'll find others as well.
https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-...
Inference is (mostly) stateless. So unlike training where you need to have memory coherence over something like 100k machines and somehow avoid the certainty of machine failure, you just need to route mostly small amounts of data to a bunch of big machines.
I don't know what the specs of their inference machines are, but where I worked the machines research used were all 8gpu monsters. so long as your model fitted in (combined) vram, you could job was a goodun.
To scale the secret ingredient was industrial amounts of cash. Sure we had DGXs (fun fact, nvidia sent literal gold plated DGX machines) but they wernt dense, and were very expensive.
Most large companies have robust RPC, and orchestration, which means the hard part isn't routing the message, its making the model fit in the boxes you have. (thats not my area of expertise though)
I think this might just be the key insight. The key advantage of doing batched inference at a huge scale is that once you maximize parallelism and sharding, your model parameters and the memory bandwidth associated with them are essentially free (since at any given moment they're being shared among a huge amount of requests!), you "only" pay for the request-specific raw compute and the memory storage+bandwidth for the activations. And the proprietary models are now huge, highly-quantized extreme-MoE models where the former factor (model size) is huge and the latter (request-specific compute) has been correspondingly minimized - and where it hasn't, you're definitely paying "pro" pricing for it. I think this goes a long way towards explaining how inference at scale can work better than locally.
(There are "tricks" you could do locally to try and compete with this setup, such as storing model parameters on disk and accessing them via mmap, at least when doing token gen on CPU. But of course you're paying for that with increased latency, which you may or may not be okay with in that context.)
Quite the opposite. Context caching requires state (K/V cache) close to the VRAM. Streaming requires state. Constrained decoding (known as Structured Outputs) also requires state.
"we do 1970s mainframe style timesharing"
there, that was easy
If the requests were regularly spaced, and they certainly won’t be, but for the sake of argument, then 1 machine could serve 17,000 requests per day, or 120,000 per week. At that rate, you’d need about 5,600 machines to serve 700M requests. That’s a lot to me, but not to someone who owns a data center.
Yes, those 700M users will issue more than 1 query per week and they won’t be evenly spaced. However, I’d bet most of those queries will take well under 1 second to answer, and I’d also bet each machine can handle more than one at a time.
It’s a large problem, to be sure, but that seems tractable.
Not sure if you were just joking or really believe that, but for other peoples’ sake, it’s wildly wrong.
They're definitely running cluster knoppix.
:-)
This stuff is well understood in public, and where a big name has something highly custom going on? Often as not it's a liability around attachment to some legacy thing. You run this stuff at scale by having the correct institutions and processes in place that it takes to run any big non-trivial system: that's everything from procurement and SRE training to the RTL on the new TPU, and all of the stuff is interesting, but if anyone was 10x out in front of everyone else? You'd be able to tell.
Signed, Someone Who Also Did Megascale Inference for a TOP-5 For a Decade.
Doesn't OpenAI depend mostly on its relationship/partnership with Microsoft to get GPUs to inference on?
Thanks for the links, interesting book!
That is, as a research person using our GPUs and TPUs I see first hand how choices from the high level python level, through Jax, down to the TPU architecture all work together to make training and inference efficient. You can see a bit of that in the gif on the front page of the book. https://jax-ml.github.io/scaling-book/
I also see how sometimes bad choices by me can make things inefficient. Luckily for me if my code/models are running slow I can ping colleagues who are able to debug at both a depth and speed that is quite incredible.
And because were on HN I want to preemptively call out my positive bias for Google! It's a privilege to be able to see all this technology first hand, work with great people, and do my best to ship this at scale across the globe.
And folks at LMSys: https://lmsys.org/blog/
Large Model Systems (LMSYS Corp.) is a 501(c)(3) non-profit focused on incubating open-source projects and research. Our mission is to make large AI models accessible to everyone by co-developing open models, datasets, systems, and evaluation tools. We conduct cutting-edge machine learning research, develop open-source software, train large language models for broad accessibility, and build distributed systems to optimize their training and inference.Sounds analogous to the 60's and 70's i.e "even small programs run so close to hardware limits". If optimization and efficiency is dead in software engineering, it's certainly alive and well in LLM development.
> llama.cpp an other inference engines auto add a <bos> - DO NOT add TWO <bos> tokens! You should ignore the <bos> when prompting the model!
That makes the want to try exactly that? Weird
I don't like how the grand parent mystifies this. This problem is just normal engineering. Any good engineer could learn how to do it.
One of my colleagues was only 25, really smart in his field and became a professor less than 10 years later. But he was incredibly naive in everyday chores. Buying groceries or filing taxes resulted in major screw-ups regularly
The Dunning-Kruger effect also applies to smart people. You don't stop when you are estimating your ability correctly. As you learn more, you gain more awareness of your ignorance and continue being conservative with your self estimates.
I ask because scaling an system that a substantially chunk of the population finds incredibly useful, including for the more efficient production of public goods (scientific research, for example) does seem like a problem that a) needs to be solved from a business point of view, and b) should be solved from a civic-minded point of view.
Very, very few problems _need_ to be solved. Feeding yourself is a problem that needs to be solved in order for you to continue living. People solve problems for different reasons. If you don't think LLMs are valuable, you can just say that.
I don't think people realize the size of these compute units.
When the AI bubble pops is when you're likely to be able to realistically run good local models. I imagine some of these $100k servers going for $3k on eBay in 10 years, and a lot of electricians being asked to install new 240v connectors in makeshift server rooms or garages.
You can pick up a DGX-1 on Ebay right now for less than $10k. 256 GB vRAM (HBM2 nonetheless), NVLink capability, 512 GB RAM, 40 CPU cores, 8 TB SSD, 100 Gbit HBAs. Equivalent non-Nvidia branded machines are around $6k.
They are heavy, noisy like you would not believe, and a single one just about maxes out a 16A 240V circuit. Which also means it produces 13 000 BTU/hr of waste heat.
Haha. I bought a 20 yro IBM server off eBay for a song. It was fun for a minute. Soon became a doorstop and I sold it as pickup-only on eBay for $20. Beast. Never again have one in my home.
How useful is this Tesla-era hardware on current workloads? If you tried to run the full DeepSeek R1 model on it at (say) 4-bit quantization, any idea what kind of TTFT and TPS figures might be expected?
Didn’t the DGX-1 come out 9 years ago?
Sure, datacenters will get rid of the hardware - but only because it's no longer commercially profitable run them, presumably because compute demands have eclipsed their abilities.
It's kind of like buying a used GeForce 980Ti in 2025. Would anyone buy them and run them besides out of nostalgia or curiosity? Just the power draw makes them uneconomical to run.
Much more likely every single H100 that exists today becomes e-waste in a few years. If you have need for H100-level compute you'd be able to buy it in the form of new hardware for way less money and consuming way less power.
For example if you actually wanted 980Ti-level compute in a desktop today you can just buy a RTX5050, which is ~50% faster, consumes half the power, and can be had for $250 brand new. Oh, and is well-supported by modern software stacks.
Unlike the investments in railways or telephone cables or roads or any other sort of architecture, this investment has a very short lifespan.
Their point was that whatever your take on AI, the present investment in data centres is a ridiculous waste and will always end up as a huge net loss compared to most other investments our societies could spend it on.
Maybe we'll invent AGI and he'll be proven wrong as they'll pay back themselves many times over, but I suspect they'll ultimately be proved right and it'll all end up as land fill.
Even if you didn't have optimizations involved in terms of job scheduling, they would just build as many warehouses as necessary filled with as many racks as necessary to serve the required user base.
Each of these NAND chips hundreds of dies of flash stacked inside, and they are hooked up to the same data line, so just 1 of them can talk at the same time, and they still achieve >1GB/s bandwidth. If you could hook them up in parallel, you could have 100s of GBs of bandwidth per chip.
>A typical 1U or 2U server can accommodate 2-4 H100 PCIe GPUs, depending on the chassis design.
>In a 42U rack with 20x 2U servers (allowing space for switches and PDU), you could fit approximately 40-80 H100 PCIe GPUs.
Supermicro will sell you a full rack loaded with servers [1] providing 13.4 TB of GPU memory.
And with 132kW of power output, you can heat an olympic-sized swimming pool by 1°C every day with that rack alone. That's almost as much power consumption as 10 mid-sized cars cruising at 50 mph.
[1] https://www.supermicro.com/en/products/system/gpu/48u/srs-gb...
From what I understand, this hardware has a high failure rate over the long term especially because of the heat they generate.
After years of “AI is a bubble, and will pop when everyone realizes they’re useless plagiarism parrots” it’s nice to move to the “AI is a bubble, and will pop when it becomes completely open and democratized” phase
Also, you CAN run local models that are as good as GPT 4 was on launch on a macbook with 24 gigs of ram.
https://artificialanalysis.ai/?models=gpt-oss-20b%2Cgemma-3-...
Conversely, you can't do the same thing as a self hosted user, you can't really bank your idle compute for a week and consume it all in a single serving, hence the much more expensive local hardware to reach the peak generation rate you need.
I assume the former has massive overhead, but maybe it is worthwhile to keep responsiveness up for everyone.
If you want a survey of intermediate level engineering tricks, this post we wrote on the Fin AI blog might be interesting. (There's probably a level of proprietary techniques OpenAI etc have again beyond these): https://fin.ai/research/think-fast-reasoning-at-3ms-a-token/
My simple explanation of how batching works: Since the bottleneck of processing LLMs is in loading the weights of the model onto the GPU to do the computing, what you can do is instead of computing each request separately, you can compute multiple at the same time, ergo batching.
Let's make a visual example, let's say you have a model with 3 sets of weights that can fit inside the GPU's cache (A, B, C) and you need to serve 2 requests (1, 2). A naive approach would be to serve them one at a time.
(Legend: LA = Load weight set A, CA1 = Compute weight set A for request 1)
LA->CA1->LB->CB1->LC->CC1->LA->CA2->LB->CB2->LC->CC2
But you could instead batch the compute parts together.
LA->CA1->CA2->LB->CB1->CB2->LC->CC1->CC2
Now if you consider that the loading is hundreds if not thousands of times slower than computing the same data, then you'll see the big different, here's a "chart" visualizing the difference of the two approaches if it was just 10 times slower. (Consider 1 letter a unit of time.)
Time spent using approach 1 (1 request at a time):
LLLLLLLLLLCLLLLLLLLLLCLLLLLLLLLLCLLLLLLLLLLCLLLLLLLLLLCLLLLLLLLLLC
Time spend using approach 2 (batching):
LLLLLLLLLLCCLLLLLLLLLLCCLLLLLLLLLLCC
The difference is even more dramatic in the real world because as I said, loading is many times slower than computing, you'd have to serve many users before you see a serious difference in speeds. I believe in the real world the restrictions is actually that serving more users requires more memory to store the activation state of the weights, so you'll end up running out of memory and you'll have to balance out how many people per GPU cluster you want to serve at the same time.
TL;DR: It's pretty expensive to get enough hardware to serve an LLM, but once you do have you can serve hundreds of users at the same time with minimal performance loss.
- Big models like GPT-4 are split across many GPUs (sharding).
- Each GPU holds some layers in VRAM.
- To process a request, weights for a layer must be loaded from VRAM into the GPU's tiny on-chip cache before doing the math.
- Loading into cache is slow, the ops are fast though.
- Without batching: load layer > compute user1 > load again > compute user2.
- With batching: load layer once > compute for all users > send to gpu 2 etc
- This makes cost per user drop massively if you have enough simultaneous users.
- But bigger batches need more GPU memory for activations, so there's a max size.
This does makes sense to me but does this sound accurate to you?
Would love to know if I'm still missing something important.
One can serve a lot if models if allowed to burn through over a billion dollars with no profit requirement. Classic, VC-style, growth-focused capitalism with an unusual, business structure.
Yet undoubtedly they are making what is declared a loss.
But is it really a loss?
If you buy an asset, is that automatically a loss? or is it an investment?
By "running at a loss" one can build a huge dataset, to stay in the running.
I think the thing to remember is that the majority of chatGPT users, even those who use it every day, are idle 99.9% of the time. Even someone who has it actively processing for an hour a day, seven days a week, is idle 96% of the time. On top of that, many are using less-intensive models. The fact that they chose to mention weekly users implies that there is a significant tail of their user distribution who don't even use it once a day.
So your question factors into a few of easier-but-still-not-trivial problems:
- Making individual hosts that can fit their models in memory and run them at acceptable toks/sec.
- Making enough of them to handle the combined demand, as measured in peak aggregate toks/sec.
- Multiplexing all the requests onto the hosts efficiently.
Of course there are nuances, but honestly, from a high level last problem does not seem so different from running a search engine. All the state is in the chat transcript, so I don't think there any particular reason reason that successive interactions on the same chat need be handled by the same server. They could just be load-balanced to whatever server is free.
We don't know, for example, when the chat says "Thinking..." whether the model is running or if it's just queued waiting for a free server.
Some of the other main tricks - compress the model to 8 bit floating point formats or even lower. This reduces the amount of data that has to stream to the compute unit, also newer GPUs can do math in 8-bit or 4-bit floating point. Mixture of expert models are another trick where for a given token, a router in the model decides which subset of the parameters are used so not all weights have to be streamed. Another one is speculative decoding, which uses a smaller model to generate many possible tokens in the future and, in parallel, checks whether some of those matched what the full model would have produced.
Add all of these up and you get efficiency! Source - was director of the inference team at Databricks
If you try to run GPT4 at home, you'll still need enough VRAM to load the entire model, which means you'll need several H100s (each one costs like $40k). But you will be under-utilizing those cards by a huge amount for personal use.
It's a bit like saying "How come Apple can make iphones for billions of people but I can't even build a single one in my garage"
I don't really understand why you're trying to connect MoE and batching here. Your stated mechanism is not only incorrect but actually the wrong way around.
The efficiency of batching comes from optimally balancing the compute and memory bandwidth, by loading a tile of parameters from the VRAM to cache, applying those weights to all the batched requests, and only then loading in the next tile.
So batching only helps when multiple queries need to access the same weights for the same token. For dense models, that's just what always happens. But for MoE, it's not the case, exactly due to the reason that not all weights are always activated. And then suddenly your batching becomes a complex scheduling problem, since not all the experts at a given layer will have the same load. Surely a solvable problem, but MoE is not the enabler for batching but making it significantly harder.
For fast inference you typically keep all experts in memory (or shard them), so VRAM still scales with the total number of experts.
Practically, that’s why home setups are wasteful: you buy a GPU for its VRAM capacity, but MoE only activates a fraction of the compute each token, and some experts/devices sit idle (because you are the only one using the model).
MoE does not make batching more efficient, but it demands larger batches to maximize compute utilization and to amortize routing. Dense models batch trivially (same weights every token). MoE batches well once the batch is large enough so each expert has work. So the point isn’t that MoE makes batching better, but that MoE needs bigger batches to reach its best utilization.
Speculative decoding uses a smaller draft model to generate tokens with much less compute and memory required. Then the main model will accept those tokens based on the probability it would have generated them. In practice this case easily result in a 3x speedup in inference.
Another trick for structured outputs that I know of is "fast forwarding" where you can skip tokens if you know they are going to be the only acceptable outputs. For example, you know that when generating JSON you need to start with `{ "<first key>": ` etc. This can also lead to a ~3x speedup in when responding in JSON.
I'm not sure it improved the speed much
In my experiences I'd seen the calls to the target model reduced to a third of what they would have been without using a draft model.
You'll still get some gains on a local model, but they won't be near what they could be theoretically if everything is properly tuned for performance.
It also depends on the type of task. I was working with pretty structured data with lots of easy to predict tokens.
A second trick is to implement something called speculative decoding. Inference has two phases. One is prompt processing and another is token generation. They actually work the same way using what is called a forward pass, except prompt processing can do them in parallel by switching from matrix-vector to matrix-matrix multiplication and dumping the prompt’s tokens into each forward pass in parallel. Each forward pass will create a new token, but it can be discarded unless it is from the last forward pass, as that will be the first new token generated as part of token generation. Now, you put that token into the next forward pass to get the token after it, and so on. It would be nice if all of the forward passes could be done in parallel, but you do not know the future, so you ordinarily cannot. However, if you make a draft model that is a very fast model runs in a fraction of the time and guesses the next token correctly most of the time, then you can sequentially run the forward pass for that instead N times. Now, you can take the N tokens and put it into the prompt processing routine that did N forward passes in parallel. Instead of discarding all tokens except the last one like in prompt processing, we will compare them to the input tokens. All tokens up to and including the first token that differ, that come out of the parallel forward pass are valid tokens for the output of the main model. This is guaranteed to always produce at least 1 valid token since in the worse case the first token does not match, but the output for the first token will be equal to the output of running the forward pass without having done speculative decoding. You can get a 2x to 4x performance increase from this if done right.
Now, I do not work on any of this professionally, but I am willing to guess that beyond these techniques, they have groups of machines handling queries of similar length in parallel (since doing a batch where 1 query is much longer than the others is inefficient) and some sort of dynamic load balancing so that machines do not get stuck with a query size that is not actively being utilized.
1. You load all the weights of the model into GPU VRAM, plus the context.
2. You construct a data structure called the "KV cache" representing the context, and it hopefully stays in the GPU cache.
3. For each token in the response, for each layer of the model, you read the weights of that layer out of VRAM and use them plus the KV cache to compute the inputs to the next layer. After all the layers you output a new token and update the KV cache with it.
Furthermore, my understanding is that the bottleneck of this process is usually in step 3 where you read the weights of the layer from VRAM.
As a result, this process is very parallelizable if you have lots of different people doing independent queries at the same time, because you can have all their contexts in cache at once, and then process them through each layer at the same time, reading the weights from VRAM only once.
So once you got the VRAM it's much more efficient for you to serve lots of people's different queries than for you to be one guy doing one query at a time.
batching requests increase latency to first token, so it's tradeoff and MoE makes it more tricky because they are not equally used.
there was somewhere great article explaining deepseek efficiency that explained it in great detail (basically latency - throughput tradeoff)
For OpenAI, I’d assume that a GPU is dedicated to your task from the point you press enter to the point it finishes writing. I would think most of the 700 million barely use ChatGPT and a small proportion use it a lot and likely would need to pay due to the limits. Most of the time you have the website/app open I’d think you are either reading what it has written, writing something or it’s just open in the background, so ChatGPT isn’t doing anything in that time. If we assume 20 queries a week taking 25 seconds each. That’s 8.33 minutes a week. That would mean a single GPU could serve up to 1209 users, meaning for 700 million users you’d need at least 578,703 GPUs. Sam Altman has said OpenAI is due to have over a million GPUs by the end of year.
I’ve found that the inference speed on newer GPUs is barely faster than older ones (perhaps it’s memory speed limited?). They could be using older clusters of V100, A100 or even H100 GPUs for inference if they can get the model to fit or multiple GPUs if it doesn’t fit. A100s were available in 40GB and 80GB versions.
I would think they use a queuing system to allocate your message to a GPU. Slurm is widely used in HPC compute clusters, so might use that, though likely they have rolled their own system for inference.
As already answered, AI companies use extremely expensive setups (servers with professional cards) in large numbers and all these things concentrated in big datcenters with powerful networking and huge power consumption.
Imagine - last time, so huge investments (~1.2% of GDP, and unknown if investments will grow or not) was into telecom infrastructure - mostly wired telephones, but also cable TV and later added Internet and cell communications and clouds (in some countries wired phones just don't cover whole country and they jumped directly into wireless communications).
Larger investments was into railroads - ~6% of GDP (and I'm also not sure, some people said, AI will surpass them as share of possible for AI tasks constantly grow).
So to conclude, just now AI boom looks like main consumer of telecom (Internet) and cloud infrastructure. If you've seen old mainframes in datacenters, and extremely thick core network cables (with hundreds wires or fibers in just one cable), and huge satellite dishes, you could imagine, what I'm talking about.
And yes, I'm not sure, will this boom end like dot-coms (Y2K), or such huge usage of resources will sustain. Why it is not obvious, because for telecoms (internet) also was unknown, if people will use phones and other p2p communications for leisure as now, or will leave phones just for work. Even worse, if AI agents become ordinary things, possible scenario, number of AI agents will surpass number of people.
For storage, they also have massive amount of hard disks and SSD behind planet scale object file systems (like AWS's S3 or Tectonic at Meta or MinIO in prem) all connected by massive amount of switches and routers of varying capacity.
So in the end, it's just the good old Cloud, but also with GPUs.
Btw, OpenAI's infrastructure is provided and managed by Microsoft Azure.
And, yes, all of this requires billions of dollars to build and operate.
It's more cost effective to farm eggs from a hundred thousand chickens than it is for individuals to have chickens in their yard.
You CAN run a GPT-class model on your own machine right now, for several thousand dollars of machine... but you can get massively better results if you spend those thousands of dollars on API credits over the next five years or so.
Some people will choose to do that. I have backyard chickens, they're really fun! Most expensive eggs I've ever seen in my life.
I for one look forward to another 10 years of progress - or less - putting current models running on a laptop. I don’t trust any big company with my data
Even beyond privacy just the availability is out of your control - you can look at r/ChatGPT's collective spasm yesterday when 4o was taken from them, but basically, you have no guarantees to access for services, and for LLM models in particular, "upgrades" can completely change behavior/services that you depend on.
Google has been even worse in the past here, I've seen them deprecate model versions with 1 month notices. It seems a lot of model providers are doing dynamic model switching/quanting/reasoning effort adjustments based on load now.
Consider a system were running a dozen queries at once is only marginally more expensive than running one query. What would you build?
That's a really, really big "sure."
Almost every trick to run a LLM at OpenAI's scale is a trade secret and may not be easily understood by mere mortals anyways (e.g. bare-metal CUDA optimizations)
With all the staff poaching the trade secrets may have now leaked?
I've had Google Gemini start talking to be in the context of someone else's chat, unrelated to mine.
ME: Does POSIX have any API for creating a filesystem object (such as a file) with a predetermined inode number supplied by the caller?
AI: <reformats my question into LaTeX syntax, out of the blue>
\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\begin{document}
\section*{Does POSIX have any API for creating a filesystem object with a predetermined inode number supplied by the caller?}
\textbf{No, POSIX does not provide a standard API for creating a filesystem object (like a file or directory) with a caller-specified inode number.}
\subsection*{Inode Allocation in POSIX Filesystems}
[... SNIP]
AI: You are absolutely right! My apologies. That was a mistake on my part. I got carried away with the instruction to use LaTeX for mathematical and scientific notations and incorrectly applied it to the entire response. [... SNIP]
There was no such instruction. I've never chatted with any AI about LaTeX. it leaked from the tokens of someone else's chat.
Nope. That's not how it works. Attention doesn't work across multiple independent prompts queued in the same batch. It's not physically possible for the tokens of another chat to leak.
What most likely happened is that the model glitched out to the instructions in its (hidden) system prompt, which most likely does include instructions about using LaTeX for mathematical and scientific notation.
https://openai.com/index/march-20-chatgpt-outage/
"We took ChatGPT offline earlier this week due to a bug in an open-source library which allowed some users to see titles from another active user’s chat history. It’s also possible that the first message of a newly-created conversation was visible in someone else’s chat history if both users were active around the same time."
You are probably right about this particular LaTeX issue though.
If you're interested in a bit of a deeper dive, I can highly recommend reading some of what DeepSeek has published: https://arxiv.org/abs/2505.09343 (and actually quite a few of their Technical Reports and papers).
I'd also say that while the original GPT-4 was a huge model when it was originally released (rumored 1.7T-A220B), these days you can get (original release) "GPT-4-class" performance at ~30B dense/100B sparse MoE - and almost all the leading MoEs have between 12-37B activations no matter how big they get - Kimi K2 (1T param weights) has only 32B activations). If you do a basic quants (FP8/INT8) you can easily push 100+ tok/s on pretty bog standard data center GPUs/nodes. You quant even lower for even better speeds (tg is just MBW) for not much in quality loss (although for open source kernels, usually without getting much overall throughput or latency improvements).
A few people have mentioned speculative decoding, if you want to learn more, I'd recommend taking a look at the papers for one of the (IMO) best open techniques, EAGLE: https://github.com/SafeAILab/EAGLE
The other thing that is often ignored, especially for multiturn that I haven't seen mentioned yet is better caching, specifically prefix caching (radix-tree, block-level hash) or tiered/offloaded kvcaches (LMCache as one example). If you search for those keywords, you'll find lots there as well.
Just illustrative guesses, not real numbers, I underestimate overheads here but anyway ...
Let's assume a $20k expert node can produce 500 tokens per second (15,000 per year). $5k a year for the machine per year. $5k overheads. 5 experts per token (so $50k to produce 15,000 megatokens with a 100% throughput). Say they charge up to $10 per million tokens ... yeah it's tight but I can see how it's doable.
Say they cost $100 per user per year. If it's $10 per million tokens (depends on the model) then they are budgeting 10 million tokens per user. That's like 100 books per year. The answer is that users probably don't use as much as the api would cost.
The real question is, how does it cost $10 per megatoken?
500 tokens per second per node is like 15,000 megatokens per year. So a 500 token node can bring in $150,000 per node.
Call it 5 live experts and a router. That's maybe $20k per expert per year. If it's a kilowatt power supply per expert, and $0.1 per kW power that's $1000 for power. The hardware is good for 4 years so $5k for that. Toss in overheads, and it's maybe $10k costs.
So at full capacity they can make $5 off $10 revenue. With uneven loads they make nothing, unless they have some optimisation and very good load balancing (if they can double the tokens per second then they make a decent profit).
You can’t run GPT4 for yourself because the fixed costs are high. But the variable costs are low, so OAI can serve a shit ton.
Or equivalently the smallest available unit of “serving a gpt4” is more gpt4 than one person needs.
I think all the inference optimisation answers are plain wrong for the actual question asked?
https://www.tripadvisor.com/Restaurant_Review-g60763-d477541...
But I also have to say 700M weekly users could mean 100M daily or 70k a minute (low ball estimate with no returning users...) is a lot, but achievable at startup scale. I don't have out current numbers but we are several orders of magnitude smaller of course :-)
The big difference to home use is the amount of VRAM. Large VRAM GPUs such as H100 are gated being support contracts and cost 20k. Theoretically you could buy a Mac Pro with a ton of RAM as an individual if you wanted to run auch models yourself.
The largest chatgpt models are maybe 1-1.5tb in size and all of that needs to load into pooled vram. That sounds daunting, but a company like open ai has countless machines that have enough of these datacenter grade gpus with gobs of vram pooled together to run their big models.
Inference is also pretty cheap, especially when a model can comfortably fit in a pool of vram. Its not that the pool of gpus spool up each time someone sends a request, but whats more likely is that there’s a queue to f requests from someone like chatgpts 700 million users, and the multiple (I have no idea how many) pools of vram keep the models in their memory to chew through that nearly perpetual queue of requests.
They probably are using some interesting hardware, but there's a strange economy of scale when serving lots of requests for a small number of models. Regardless of if you are running single GPU, clustered GPU, FPGAs, or ASICs, there is a cost with initializing the model that dwarfs the cost of inferring on it by many orders of magnitude.
If you build a workstation with enough accelerator-accessible memory to have "good" performance on a larger model, but only use it with typical user access patterns, that hardware will be sitting idle the vast majority of the time. If you switch between models for different situations, that incurs a load penalty, which might evict other models, which you might have to load in again.
However, if you build an inference farm, you likely have only a few models you are working with (possibly with some dynamic weight shifting[1]) and there are already some number of ready instances of each, so that load cost is only incurred when scaling a given model up or down.
I've had the pleasure to work with some folks around provisioning an FPGA+ASIC based appliance, and it can produce mind-boggling amounts of tokens/sec, but it takes 30m+ to load a model.
[1] there was a neat paper at SC a few years ago about that, but I can't find it now
- the models are not too big for the cards. Specifically, they know the cards they have and they modify the topology of the model to fit their hardware well
- lots of optimisations. Eg the most trivial implementation of transformer-with-attention inference is going to be quadratic in the size of your output but actual implementations are not quadratic. Then there are lots of small things: tracing the specific model running on the specific gpu, optimising kernels, etc
- more costs are amortized. Your hardware is relatively expensive because it is mostly sitting idle. AI company hardware gets much more utilization and therefore can be relatively more expensive hardware, where customers are mostly paying for energy.
https://www.seangoedecke.com/inference-batching-and-deepseek...
Here is an example of what happens
> The only way to do fast inference here is to pipeline those layers by having one GPU handle the first ten layers, another handle the next ten, and so on. Otherwise you just won’t be able to fit all the weights in a single GPU’s memory, so you’ll spend a ton of time swapping weights in and out of memory and it’ll end up being really slow. During inference, each token (typically in a “micro batch” of a few tens of tokens each) passes sequentially through that pipeline of GPUs
Take a look at vLLM for an open source solution that is pretty close to the state of the art as far as handling many user queries:https://docs.vllm.ai/en/stable/
The reason why they can handle 700M users is money. I'm not saying you're poor, I'm saying they are extremely rich, and with all that money they can afford these machines.
The other reason is optimization techniques, but I don't have enough experience to talk about that.
Look at VLLM. It's the top open source version of this.
But the idea is you can service 5000 or so people in parallel.
You get about 1.5-2x slowdown on per token speed per user, but you get 2000x-3000x throughput on the server.
The main insight is that memory bandwidth is the main bottleneck so if you batch requests and use a clever KV cache along with the batching you can drastically increase parallel throughput.
1. Physical/Hardware Layer At the very bottom is the GPU silicon and its associated high-bandwidth VRAM. The model weights are partitioned, compiled, and efficiently placed so that each GPU chip and its VRAM are used to the fullest (ideally). This is where low-level kernel optimizations, fused operations, and memory access patterns matter so that everything above the chip level tries to play nice with the lowest level.
2. Intra-Node Coordination Layer Inside a single server, multiple GPUs are connected via NVLink (or equivalent high-speed interconnect). Here you use tensor parallelism (splitting matrices across GPUs), pipeline parallelism (splitting model layers across GPUs), or expert parallelism (only activating parts of the model per request) to make the model fit and run faster. The key is minimizing cross-GPU communication latency while keeping all GPUs running at full load - many low level software tricks here.
3. Inter-Node Coordination Layer When the model spans multiple servers, high-speed networking like InfiniBand comes into play. Techniques like data parallelism (replicating the model and splitting requests), hybrid parallelism (mixing tensor/pipeline/data/expert parallelism), and careful orchestration of collectives (all-reduce, all-to-all) keep throughput high while hiding model communication (slow) behind model computation (fast).
4. Request Processing Layer Above the hardware/multi-GPU layers is the serving logic: batching incoming prompts together to maximize GPU efficiency and mold them into ideal shapes to max out compute, offloading less urgent work to background processes, caching key/value attention states (KV cache) to avoid recomputing past tokens, and using paged caches to handle variable-length sequences.
5. User-Facing Serving Layer At the top are optimizations users see indirectly — multi-layer caching for common or repeated queries, fast serialization protocols like gRPC or WebSockets for minimal overhead, and geo-distributed load balancing to route users to the lowest-latency cluster.
Like the OSI model, each “layer” solves its own set of problems but works together to make the whole system scale. That’s how you get from “this model barely runs on a single high-end GPU” to “this service handles hundreds of millions of users per week with low latency.”
IMO outfits like OpenAI are burning metric shit tonnes of cash serving these models. It pails in comparison to the mega shit tonnes of cash used to train the models.
They hope to gain market share before they start charging customers what it costs.
Going over the comments the only plausible explanation I could see is KV cache being extremely useful - don't know if this is really just the case.
Would love to know the true answer to the question.
As soon as you have enough users you can let your GPUs burn with a high load constantly, while your home solution would idle most of the time and therefore be way too expensive compared to the value.
https://ut.philkr.net/advances_in_deeplearning/
Especially the "Advanced Training" section to get some idea of tricks that are used these days.
But software wise, they shard, load balance, and batch. ChatGPT gets 1000s (or something like that) of requests every second. Those are batched and submitted to one GPU. Generating text for 1000 answers is often the same speed as generating for just 1 due to how memory works on these systems.
I have had the same question lingering, so I guess there are many more people like me and you benefiting from this thread!
Different hardware, batching, etc.
So basically the main tricks are batching (only relevant when you have > 1 query to process) and MoE sharding.
1. prompt caching
2. some RAG to save resources
3. of course lots model optimizations and CUDA optimizations
4. lots of throttling
5. offloading parts of the answer that are better served by other approaches (if asked to add numbers, do a system call to a calculator instead of using LLM)
6. a lot of sharding
One thing you should ask is: What does it mean to handle a request with chatgpt? It might not be what you think it is.
source: random workshops over the past year.
AI development is for rich people right now. Maybe when the bubble pops and the hardware becomes more accessible, we'll start to see some actual value come out of the tech from small companies or individuals.
They are throwing money at this problem hoping you throw more money back.
No, really. They just have entire datacenters filled with high end GPUs.
Hint: it's a money thing.
They are also partnering with rivals like Google for additional capacity https://www.reuters.com/business/retail-consumer/openai-taps...