Production AI Serving

The Big Picture First#

In the last explanation, we covered the raw hardware — GPUs, CUDA, memory. But having powerful hardware alone doesn’t automatically mean you can serve an AI model efficiently to lots of real users at once. Here’s the specific problem this whole section addresses: running a language model for just yourself, one question at a time, is relatively straightforward — but running that same model in production, handling potentially thousands of simultaneous users, each sending requests of wildly different lengths, all while trying to keep the GPU’s expensive hardware genuinely busy rather than sitting idle, is a genuinely much harder engineering problem. Production AI serving is the whole discipline built around solving exactly this problem — a collection of specialized software tools and clever techniques specifically designed to squeeze the maximum possible useful work out of expensive GPU hardware while serving real, live traffic. Let’s work through it.

1. vLLM#

We actually already introduced vLLM briefly, back in the very first LLM APIs explanation, as a specialized tool for serving models efficiently. It’s worth revisiting here in much more depth, because it’s genuinely one of the most influential and widely-used tools in this entire space.

vLLM’s single biggest, defining innovation is something called PagedAttention. To understand why it matters, you need to understand a genuine, real problem it was specifically built to solve: when a model is actually processing a request, it needs to keep track of a growing amount of intermediate data (something we’ll cover shortly as the “KV cache”), and older, more naive serving approaches would typically reserve one single large, continuous, unbroken chunk of GPU memory for this per request — similar to trying to always park one single, entire, unbroken city block just for one specific car, even though that car might only actually need a small handful of individual parking spaces. This wastes an enormous amount of genuinely valuable, expensive GPU memory. PagedAttention instead borrows an idea from how regular computer operating systems manage memory more generally — breaking this data up into smaller, more flexible chunks (or “pages”) that can actually be allocated and freed up much more efficiently, similar to actually assigning individual parking spaces to a car as it genuinely needs them, rather than reserving one whole giant block upfront regardless of actual need.

The genuine, practical, real-world payoff of this approach is substantial — vLLM has been shown to achieve meaningfully higher throughput and better GPU memory efficiency compared to older, more naive serving approaches, letting a given GPU actually serve considerably more simultaneous users at once from the exact same underlying hardware. Because of this genuine efficiency, combined with the fact that it’s open-source, free, and reasonably straightforward to actually get started with (you can often start serving a model pulled directly from Hugging Face with comparatively little setup effort), vLLM has become something of a widely-adopted default, general-purpose choice across the industry for serving open-source language models in production.

2. TensorRT#

TensorRT is NVIDIA’s own general-purpose toolkit specifically for optimizing and actually running trained AI models as fast as genuinely, technically possible on NVIDIA’s own specific GPU hardware — it’s worth understanding as a broader, more general foundation, distinct from TensorRT-LLM (which we’ll cover next, and which is specifically built on top of it, for language models specifically).

The core underlying idea behind TensorRT is that a model, once it’s already fully trained, can actually be further optimized quite considerably, specifically for the particular exact hardware it’s actually going to be run on — techniques like combining several separate individual mathematical operations together into one single, more efficient, combined operation, or reducing the numerical precision used for certain particular calculations where that reduction doesn’t meaningfully hurt the actual final result’s quality (which connects directly to the model quantization topic we’ll cover later below). TensorRT specifically takes an already-trained model and effectively rebuilds it into a specifically optimized, hardware-specific format designed to run considerably faster on NVIDIA’s own particular chips.

The genuine tradeoff worth understanding clearly here is that this deep, hardware-specific optimization process genuinely comes at a real cost in flexibility and setup effort. Building one of these specifically optimized “engines,” as they’re generally called, genuinely requires real, deliberate, careful upfront configuration work, and the resulting optimized engine ends up genuinely tied quite specifically and closely to the particular exact hardware and particular exact model configuration it was actually built for — meaning it’s genuinely less of a quick, flexible, “just point it at a model and immediately go” kind of tool, compared to something like vLLM, and considerably more of a tool for squeezing out that final, additional degree of maximum possible raw performance once a team has already genuinely settled on their exact specific model and exact specific hardware setup, and is willing to actually, genuinely invest real, meaningful additional setup time and effort to get there.

3. TensorRT-LLM#

TensorRT-LLM is NVIDIA’s own further, more specialized version of TensorRT, specifically built and tailored for actually serving large language models in particular, rather than covering the considerably broader, more general range of different AI model types that the base TensorRT toolkit itself is more generally designed to actually handle.

Building directly on top of everything TensorRT already provides, TensorRT-LLM specifically adds in a whole range of additional optimizations genuinely specific to how language models actually work — things like specialized, further-optimized handling of the KV cache we’ll discuss shortly below, and specific further support for some of the more advanced serving techniques we’ll also cover later in this same explanation, like speculative decoding. The general, overall goal remains fundamentally the same as with the base TensorRT toolkit itself: squeeze out the genuine, absolute maximum possible raw performance specifically from NVIDIA’s own particular hardware, now specifically tailored and focused on language models in particular.

In practice, TensorRT-LLM is generally considered the choice for teams that are genuinely fully, deliberately committed to NVIDIA’s hardware specifically, and that genuinely have both the real, meaningful technical expertise and the real, meaningful time available to actually properly invest in its more involved setup process — reports suggest this initial setup can genuinely take something like one to two full weeks of dedicated work, compared to the considerably faster, more immediate path to getting started that a tool like vLLM typically genuinely offers. The genuine payoff for that real additional upfront investment is typically the very best possible raw performance actually achievable specifically on NVIDIA’s own hardware — but it does genuinely come at the real cost of that meaningfully greater setup complexity, and a real, genuine, tighter lock-in specifically to NVIDIA’s own particular hardware and software ecosystem as a whole.

4. Ollama#

We actually already covered Ollama in real detail, back in the very first LLM APIs explanation, specifically as a tool for running AI models locally on your own personal computer. It’s genuinely worth briefly revisiting here, specifically to clarify how it fits properly into this whole broader serving landscape, alongside the various other, more production-oriented serving tools we’re covering throughout this whole explanation.

Where the other tools covered throughout this whole explanation (vLLM, TensorRT-LLM, and the others we’re about to get to) are all specifically built and optimized for serving many simultaneous users efficiently in real, live production, Ollama’s own particular design focus remains fundamentally different — it’s specifically, deliberately optimized for ease of use and genuine simplicity, on a genuinely single individual personal machine, rather than for maximizing raw genuine throughput across many simultaneous users at real, meaningful production scale. This makes Ollama a genuinely excellent, well-suited choice specifically for local individual development, experimentation, and genuinely personal individual use, but it’s generally, genuinely not the right, appropriate tool specifically for actually serving a genuine, real production application handling many real, simultaneous concurrent users at once.

Understanding this particular distinction clearly genuinely matters, precisely because it’s a fairly common, easy point of real confusion for beginners specifically getting properly started in this whole broader space — the fact that a given tool is genuinely excellent and well-suited for one entirely particular specific use case (like Ollama genuinely is, specifically for local individual development) doesn’t necessarily, automatically mean it’s also genuinely the right, correct tool for an entirely different, separate specific use case (like actually serving real production traffic at genuine, meaningful scale), even though both of these particular use cases genuinely do, on the surface, broadly, loosely involve “running an AI model” in some fairly general, broad overall sense.

5. Hugging Face TGI#

TGI (Text Generation Inference) is Hugging Face’s own dedicated production serving framework, and it’s genuinely worth understanding both its real, significant historical importance to this whole broader field, and, importantly, its genuinely current actual status as of right now.

Historically, TGI was genuinely one of the very first widely-adopted dedicated serving engines specifically built for language models, and it genuinely played a real, significant, important role in actually powering some of Hugging Face’s own major, widely-used products, while also genuinely helping push the whole broader industry forward toward much better-optimized ways of actually serving these kinds of models efficiently, well beyond simply, naively calling a model directly in raw Python code. It integrates especially closely and tightly with the very broad, extensive Hugging Face ecosystem of models more generally, making it a genuinely natural, comfortable fit specifically for teams that are already, genuinely working extensively within that particular same ecosystem.

However, it’s genuinely important to be upfront and honest here about a real, significant, current development: as of quite recently, Hugging Face has actually put TGI into what’s specifically called “maintenance mode” — meaning it now generally only genuinely receives bug fixes going forward, rather than continuing to receive genuinely significant, meaningful new features and improvements as it did previously. Hugging Face itself has actually, publicly recommended that teams instead move toward using either vLLM or SGLang (which we’ll cover just next, right below) going forward, and Hugging Face’s own Inference Endpoints product has itself actually already switched over to defaulting to vLLM. This particular detail genuinely, concretely illustrates something worth keeping in mind throughout this entire fast-moving space more generally — specific tools and specific frameworks genuinely, actively rise and fall in relative prominence and relative recommendation status fairly quickly, so it’s genuinely always worth checking current, up-to-date, recent sources before actually committing your own team to any one single particular specific tool for any genuinely serious, real production use.

6. SGLang#

SGLang is a considerably newer serving framework that has actually, genuinely risen to real prominence fairly quickly and fairly recently, specifically built around a core technique called RadixAttention, which is genuinely particularly, especially well-suited to a specific category of workload that’s become increasingly, genuinely common and important: multi-turn conversations, RAG-based applications, and genuine AI agents — all cases where a considerable amount of the actual underlying prompt content genuinely, meaningfully gets reused, in whole or in significant part, again and again across multiple separate, successive requests.

Here’s the core, genuine underlying insight behind RadixAttention. In a genuinely typical multi-turn chat conversation, or in a genuine agent that’s repeatedly calling tools and continuing to build on the exact same underlying growing context, a very considerable, large portion of a given new incoming request’s actual full prompt is often genuinely, literally identical to what a given system has already, previously processed just moments before, in an earlier immediately preceding turn. RadixAttention specifically, deliberately, cleverly caches this genuinely already-processed shared computation, so the given system genuinely doesn’t ever have to actually redo that same exact already-completed work all over again from scratch, each and every single subsequent time. Reported real-world benchmark results suggest SGLang can genuinely, meaningfully outperform vLLM by a real, quite significant margin specifically on exactly these kinds of shared-context, conversational-style workloads — with some reported real benchmarks showing genuinely dramatically higher throughput and genuinely, considerably lower latency specifically for these kinds of repeated-context use cases in particular.

The genuinely practical overall takeaway here is that SGLang has become an especially, particularly strong, well-suited specific choice specifically for exactly the kinds of genuinely conversational, RAG-based, and agent-based applications we’ve actually covered at considerable, real length throughout this entire broader series — precisely the specific kinds of applications where a meaningful, substantial portion of the given underlying prompt content genuinely does tend to actually repeat quite considerably, again and again, across many successive requests, in a way that a more general-purpose serving engine might genuinely, otherwise simply, unnecessarily end up needlessly, wastefully redoing all over again, entirely from complete scratch, every single time.

7. LMDeploy#

LMDeploy is a specialized model-serving and model-compression toolkit that comes from the InternLM ecosystem, and its own particular core engine is specifically called TurboMind. Its own particular, specific design focus centers especially heavily around achieving genuinely maximum possible raw throughput per individual GPU, especially and particularly specifically when working together in combination with quantized models (connecting quite directly to the model quantization topic we’ll actually cover shortly, later below, in this same explanation).

LMDeploy’s own particular, specific combination of techniques — including its own persistent batching approach, and its own specific, particular blocked approach to KV cache management (connecting directly to the KV cache topic we’ll also cover shortly below) — has reportedly, genuinely allowed it to achieve meaningfully, considerably higher raw request throughput compared to vLLM in certain specific, particular benchmark comparisons, especially and particularly specifically for 4-bit quantized versions of certain popular, well-known specific model families.

LMDeploy genuinely tends to be a particularly well-suited, strong specific choice for teams that are genuinely, specifically prioritizing squeezing the genuine, absolute maximum possible raw efficiency specifically out of quantized models in particular — meaning models that have already been deliberately, specifically compressed down to use meaningfully, considerably less raw memory and meaningfully less raw computation than their genuine, original full-precision counterpart versions, at the cost of only some genuinely quite small, typically fairly minor, and often genuinely well worth it corresponding real reduction in overall raw output quality. Like several of the other more specialized tools we’ve covered throughout this whole explanation, it genuinely represents a real, meaningful, particular specific optimization tradeoff — trading off some genuine additional setup complexity and some genuine additional specificity of its own particular focus, specifically in exchange for genuinely, meaningfully superior raw real-world performance within that own particular, specific focused use case.

8. Continuous Batching#

Continuous batching (which you may also sometimes genuinely see referred to elsewhere as “inflight batching”) is one of the single most genuinely important underlying techniques that actually makes efficient, modern production LLM serving genuinely possible in the first place — and understanding it clearly genuinely helps make sense of why several of the specific tools we’ve already covered throughout this whole explanation are actually so meaningfully, considerably faster than a more naive, simple, unoptimized serving approach would otherwise genuinely be.

To understand why continuous batching genuinely matters so much, it helps to first understand the real, genuine problem it actually, specifically solves. GPUs, as we already discussed at real length in the previous explanation, are genuinely, fundamentally at their own particular best specifically when they’re actually doing the exact same kind of calculation on many different, separate pieces of data all together at once, in parallel — this general underlying idea, when specifically applied to genuinely serving many separate individual user requests together, is generally referred to as “batching.” But here’s the genuine, real practical complication: different individual user requests naturally, genuinely arrive at meaningfully different times, and they each genuinely, naturally require meaningfully different amounts of actual total processing time to fully, completely finish (since some given individual requests genuinely, naturally want considerably longer overall responses than others do). A genuinely more naive, simple, older batching approach would typically wait around to actually collect a genuinely full batch of separate individual requests together first, and would then genuinely, correspondingly have to wait for literally every single individual request within that entire given batch to fully, completely finish, before it could then finally actually go on to start any genuinely new, fresh batch of requests at all.

Continuous batching instead genuinely, cleverly allows genuinely new incoming individual requests to actually, dynamically join an already actively in-progress, ongoing batch, and correspondingly also allows individual requests that have already, genuinely finished to actually leave that same ongoing batch and immediately free up their own space, all without the given overall system ever needing to actually wait around for the entire whole batch, considered together, to fully finish all together, all at once, in perfect lockstep. This genuinely keeps the GPU considerably, meaningfully more consistently, continuously busy overall, since it genuinely never has to sit around uselessly waiting on just one single particular slow, long individual request to fully finish before it can genuinely go on to actually pick up and start any brand new incoming work. This particular technique genuinely represents one of the single most significant overall reasons why modern, dedicated serving engines like vLLM are genuinely so considerably, dramatically more efficient in practice than simply, naively calling a model directly, one individual request entirely at a time, in a purely sequential, one-after-another fashion.

9. KV Cache#

The KV cache (short for “key-value cache”) refers to intermediate data that a language model genuinely needs to actually keep track of and properly store while it’s actually in the process of generating a given response, and understanding it clearly genuinely helps explain why GPU memory (which we discussed at real length in the previous explanation) becomes such a genuinely critical, real, and often quite significant limiting practical factor specifically when actually, genuinely serving language models in real production.

Without getting too deeply into the underlying technical mathematics here, the genuine, core underlying idea is this: as a model actually, genuinely generates a response, one individual token (a small individual word-like piece of text) at a genuine time, it needs to actually keep genuinely referring back to everything it has already, previously seen and generated so far, in order to properly, correctly decide on the actual next appropriate token to generate. Rather than needlessly, wastefully recalculating this exact same underlying relevant information completely again from total scratch for literally every single new individual token it happens to generate, the model instead genuinely, cleverly caches (meaning it actually genuinely stores) certain particular specific intermediate values, so it can then genuinely just efficiently reuse them again immediately, rather than ever needing to needlessly redo that exact same underlying calculation work all over again.

This particular caching approach genuinely, meaningfully speeds things up considerably — but it also genuinely, correspondingly consumes real, actual, meaningful GPU memory, and critically, the total overall size of this particular cache genuinely, actually grows the longer a given ongoing conversation or a given individual response actually genuinely becomes. This is exactly, precisely the specific underlying real problem that vLLM’s own PagedAttention technique, which we already actually discussed above, was specifically, deliberately built to actually help solve — managing this particular growing cache considerably more efficiently, so a given available GPU can genuinely, actually serve meaningfully more simultaneous concurrent users at once, without needlessly, wastefully running out of its own genuinely available memory. Understanding the KV cache clearly genuinely helps explain why longer conversations and genuinely longer individual responses meaningfully, correspondingly cost real, more money and genuinely take real, more memory to actually properly serve, compared to considerably shorter, more brief individual exchanges.

10. Speculative Decoding#

Speculative decoding is a genuinely clever technique specifically designed to actually speed up how fast a model can genuinely generate its own given response, and the core underlying idea here is genuinely worth understanding well, since it’s a fairly clever, non-obvious real approach.

Here’s the core, genuine underlying idea. Normally, a genuinely large, powerful, and fairly slow language model has to actually generate its response one individual single token entirely at a time, with each new individual token genuinely, fully depending directly on all of the ones that actually already came immediately before it. Speculative decoding instead genuinely uses a considerably smaller, meaningfully faster “draft” model to actually quickly generate several individual candidate next tokens all at once, all together, and then has the larger, considerably more capable, “real” primary model genuinely just verify all of those particular candidate tokens together, all at once, in a genuinely single verification pass, rather than the larger model ever needing to actually, individually, separately generate every single one of those tokens completely by itself, entirely from complete scratch, one at a genuinely time.

The genuine, real payoff here comes specifically from the fact that verifying a whole given set of already-proposed candidate tokens together, all at once, in one single combined pass, is genuinely considerably faster overall than the larger, primary model actually having to individually, separately generate that exact same equivalent number of tokens completely on its own, entirely from scratch, one at a genuinely, painstaking time. When the smaller, faster draft model’s own proposed candidate guesses genuinely happen to actually turn out correct (which, for many fairly common, everyday, predictable pieces of text, does genuinely happen quite frequently and quite often in practice), the given overall system genuinely gets a meaningful, real speed boost, essentially entirely for free. When the smaller draft model’s own particular guesses genuinely happen to turn out wrong instead, the larger, primary model simply falls back to properly, correctly generating that particular specific token itself, entirely on its own, exactly as it always genuinely, normally would have anyway — meaning this particular technique genuinely never actually risks any real reduction whatsoever in the given final response’s own overall quality, it only genuinely, potentially ever helps speed things up.

11. Model Quantization#

We’ve now touched on model quantization several separate times throughout this whole explanation, so it’s genuinely worth properly covering it here as its own fully dedicated, complete topic. Quantization refers to the general technique of actually reducing the numerical precision genuinely used to actually store a given model’s own internal learned parameters — meaning representing each individual internal number using genuinely fewer actual bits of raw data than the model’s own genuine, original full precision would normally otherwise actually require.

To understand this idea clearly and intuitively, think of it somewhat like this: a model’s own internal parameters are typically originally, genuinely stored using a fairly high degree of raw numerical precision (meaning many actual decimal places of genuine numerical accuracy). Quantization genuinely, deliberately reduces that overall precision — for example, converting from a genuinely higher-precision original format down to a meaningfully lower-precision format that uses considerably, meaningfully fewer actual bits per individual stored number. This is genuinely somewhat analogous to rounding a genuinely long, complex, precise decimal number like 3.14159265 down to simply, roughly 3.14 instead — you genuinely do lose some real, small degree of precise underlying precision in doing so, but the overall resulting number genuinely still remains quite close to, and quite usably, practically representative of, the actual real original value.

The genuine, real payoff here is considerable and quite significant: a genuinely quantized model takes up considerably less actual raw GPU memory space, and it can genuinely often actually run meaningfully, considerably faster too, since there’s genuinely, correspondingly less actual raw data that needs to be moved around and genuinely processed. The genuine tradeoff, correspondingly, is some real, small potential reduction in the given model’s own overall output quality — though, genuinely, well-designed quantization approaches can often actually genuinely achieve quite considerable, meaningful real memory and speed savings while only incurring a genuinely quite small, often barely noticeable corresponding real reduction in overall practical output quality. This is exactly, precisely why quantization has genuinely become such a widely, commonly used technique throughout this whole broader field — it lets teams genuinely run meaningfully larger, more capable models on genuinely more modest, more affordable available hardware, or genuinely serve considerably more simultaneous users at once from that exact same given underlying hardware, all in direct exchange for only a genuinely quite small, often well worth it corresponding tradeoff in the model’s own overall raw output precision.

12. AI Serving Optimization#

To genuinely close out this whole explanation, it’s worth pulling together the various individual specific techniques we’ve actually covered throughout this entire explanation into one final, unified, overall picture of what genuinely, practically effective AI serving optimization actually, genuinely looks like altogether, in real, genuine, holistic combined practice.

Good AI serving optimization generally, genuinely means thoughtfully combining several distinct techniques together, rather than ever genuinely relying on just any single one of them entirely alone, all on its own. Choosing the genuinely right underlying serving engine for your own particular specific given workload matters enormously — as we’ve genuinely seen quite clearly throughout this whole explanation, vLLM genuinely offers a strong, solid, well-rounded general-purpose default; SGLang genuinely particularly, specifically excels at conversational and genuinely agent-based workloads specifically; and TensorRT-LLM genuinely offers the very best possible raw performance specifically for teams genuinely fully, deliberately committed to NVIDIA hardware and genuinely willing to actually properly invest the real, additional required setup effort. Applying quantization where it genuinely, reasonably makes real practical sense can genuinely meaningfully reduce both cost and improve overall speed, often quite considerably, in direct exchange for only some genuinely quite small, often well worth it corresponding tradeoff in output precision. Using continuous batching and efficient KV cache management genuinely helps ensure the given underlying GPU stays as consistently, genuinely busy and as efficiently, productively utilized as reasonably possible, rather than ever wastefully sitting around idle. And considering speculative decoding, where it genuinely, reasonably applies well, can genuinely help meaningfully speed up actual response generation, all without ever actually, genuinely sacrificing any real degree of overall output quality whatsoever in doing so.

The genuinely important, broader overall takeaway to end on here, and really across this whole entire Week 13 material as a genuine, complete whole, is that serving an AI model efficiently, genuinely well, at real, meaningful production scale, is a genuinely real, distinct engineering discipline entirely all its own — every bit as genuinely real and important, and every bit as genuinely worth taking real, careful, deliberate time to properly, thoroughly learn and understand well, as the actual prompting, the actual RAG, and the actual agent-building work we’ve genuinely already covered together at such real, considerable length throughout this whole entire broader course. A genuinely brilliant, well-designed AI application, even one that’s genuinely built with real, considerable, careful skill and real thoughtful care throughout, can still genuinely end up feeling frustratingly slow, and can still genuinely end up costing considerably, unnecessarily more than it really, genuinely needs to, if the actual underlying serving infrastructure quietly sitting there underneath it hasn’t itself also actually, genuinely been properly, carefully, thoughtfully optimized right alongside it.