When would you choose vLLM, TensorRT-LLM, SGLang, or TGI for a production serving deployment?

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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?

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Quick reference

TL;DR

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.

Memory aid

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Easy to grasp

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.

Key concepts

vLLM TensorRT-LLM SGLang Continuous batching

Quick scan · 2–4 min

Everything important, quickly.

4 min: name vLLM as default + the shared primitives + each alternative's niche (TRT throughput, SGLang prefixes, TGI HuggingFace, llama.cpp edge) + the measured-constraint switch rule.

Side-by-side

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-world examples

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.
SGLang's RadixAttention powers agent and tool-use serving where shared system prompts and few-shot prefixes repeat across thousands of requests.
HuggingFace TGI backs many Inference Endpoints deployments, giving HuggingFace-native shops one-line model serving with sane defaults.
llama.cpp serves quantized DeepSeek V4 and Llama 4 on laptops and CPU-only edge boxes where no datacenter GPU is available.

Interviewer follow-ups

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?

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QWhat exactly makes TensorRT-LLM operationally expensive compared to vLLM?

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QWhere does llama.cpp fit relative to these GPU-first servers?

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QHow would you actually measure whether to leave vLLM for TensorRT-LLM?

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.