When would you choose vLLM, TensorRT-LLM, SGLang, or TGI for a production serving deployment?
Compare vLLM, TensorRT-LLM, SGLang, and TGI as production serving stacks. For each, name what it's best at and when you would deliberately pick it. What's the default and what would push you off the default?
Start with vLLM as the default for its throughput and model coverage, then switch on a measured constraint: TensorRT-LLM for peak NVIDIA throughput, SGLang for prefix-heavy agent workloads, TGI for HuggingFace shops.
Imagine you need a delivery van for a business. Most people should just buy the popular mid-size van: it carries plenty, parts are everywhere, and any mechanic can fix it. That is vLLM. But sometimes your route is special. If you only ever drive one brand of highway and want the absolute fastest trip, you buy the tuned race truck that costs more to maintain. That is TensorRT-LLM. If you keep delivering to the same neighbourhoods over and over, you want the van that remembers the route and skips re-planning it. That is SGLang. And if your whole garage already uses one toolmaker, you grab their matching van so everything clicks together. That is TGI. The trick is not to buy the race truck before you know your route actually needs it.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Choosing a serving framework is one of the highest-leverage decisions in an LLM deployment, and it is also where senior candidates most often reveal whether they have actually run production traffic. The naive instinct is to open a benchmark blog post, find the framework with the tallest throughput bar, and ship it. That instinct is wrong, because the tallest bar was measured on someone else's prompt mix, batch size, precision, and hardware, none of which match yours. The same engine that wins a 128-token-in, 128-token-out synthetic benchmark can lose badly on long-context retrieval traffic, and a number that looks decisive in a vendor's chart often shrinks to noise once you normalise for precision and batching.
The honest 2026 picture is that the four mainstream GPU servers, vLLM, TensorRT-LLM, SGLang, and TGI, have largely converged on the same core primitives. They all do paged KV cache, continuous (in-flight) batching, and chunked prefill. Five years ago these were genuine differentiators; today they are table stakes, and a feature checklist no longer separates the options. The differences that remain are about which workload each one squeezes hardest, how much operational machinery it demands, and how quickly it tracks new model architectures. A fifth option, llama.cpp, sits outside this group entirely: it is the answer when there is no datacenter GPU at all.
This deep dive frames the choice as a default plus a set of measured switch conditions. By the end you should be able to state why vLLM is the default, name the single signal that would push you off it for each alternative, and explain why a vendor benchmark is never that signal on its own. The framing matters because, in an interview, the candidate who recites four feature lists sounds junior, while the candidate who states a default and a falsifiable switch rule sounds like someone who has owned a serving budget.
Why vLLM is the default
vLLM became the reference open-source server by getting three things right at once. It introduced PagedAttention, which allocates the KV cache in fixed-size blocks like an operating-system page table, eliminating the fragmentation that wasted GPU memory in earlier servers. Older servers reserved a contiguous max-length slot per request, so a batch sized for worst-case context length left most of the cache idle; paging carves that waste away and lets the scheduler pack far more concurrent requests onto the same GPU. It paired that with continuous batching, scheduling requests at the token level so a finished request frees its slot immediately and a new one fills it without waiting for the whole batch.
The second thing it got right is coverage. New model architectures land in vLLM within days of release, and it now runs across NVIDIA, AMD, and TPU backends rather than a single vendor. That breadth matters operationally: it means a model swap or a hardware migration rarely forces a framework migration too. The third is community: a large contributor base means bugs surface and get fixed quickly, quantization and speculative-decoding features arrive early, and most tooling, benchmarks, and tutorials assume vLLM first, so your engineers can find answers fast.
The practical consequence is an asymmetry. Standing up vLLM costs almost nothing, and its throughput is within a small margin of the best tuned alternative for most workloads. So the burden of proof sits on any decision to leave it. You should be able to articulate, in numbers from your own traffic, exactly what vLLM is failing to deliver before you reach for something harder. In practice that discipline saves teams from a common failure mode: adopting a specialised stack for a benchmark win, then discovering the maintenance burden eats the engineering time they hoped to save.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
| Framework | Best at | Pick it when | Main cost |
|---|---|---|---|
| vLLM | Throughput plus broadest model coverage; the default | Almost always the starting point for open-model serving | Slightly below a tuned TensorRT-LLM build on raw peak throughput |
| TensorRT-LLM | Peak throughput per GPU on NVIDIA via compiled kernels | NVIDIA-only fleet where the last 10 to 30 percent of throughput pays off | Hardware and version pinned engines; every swap forces a rebuild |
| SGLang | Prefix reuse across requests via RadixAttention | Agent, few-shot, or tool-use traffic with high prefix overlap | Little gain when requests share few prefixes |
| TGI | Tight HuggingFace Hub integration and sane defaults | Teams already deep in HuggingFace tooling wanting one-line deploys | Trails vLLM slightly on raw throughput |
| llama.cpp | CPU and edge inference with aggressive quantization | Laptops, phones, or CPU-only boxes with no datacenter GPU | Not built for datacenter-scale concurrent throughput |
Real products, models, and research that use this idea.
- vLLM (UC Berkeley) is the default open-source serving stack in 2026, running Llama 4 and Qwen 3 across NVIDIA, AMD, and TPU backends.
- TensorRT-LLM (NVIDIA) is the throughput reference on H100 and B200, using compiled kernels and in-flight batching for NVIDIA-only fleets.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow does SGLang's RadixAttention differ from vLLM's automatic prefix caching?
Both reuse KV state for shared prefixes. vLLM hashes fixed-size blocks and matches block by block. SGLang maintains a radix tree over tokens, so it can share arbitrary-length prefixes and branch points, which fits tree-shaped agent and few-shot traffic more tightly.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Reaching for TensorRT-LLM first because it benchmarks fastest, then paying weeks of compile and deploy friction for throughput your traffic never needed. Pick the default, then switch on a measured constraint.
60 second bullets to scan on the way to the call.
Primary sources. Browse if you want the original framing.
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