By 2026 there are several mature open-source frameworks for serving large language models, and interviewers want to know you can pick the right one rather than just naming the fastest.
**vLLM** is the safe default. It pioneered PagedAttention, which manages the KV cache like operating-system memory pages, and it pairs that with continuous batching to keep the GPU busy. It supports almost every open model quickly, so reach for it when you are unsure.
TensorRT-LLM is NVIDIA's framework. It compiles model-specific kernels ahead of time to squeeze peak throughput out of NVIDIA hardware, but it is harder to operate and only runs on NVIDIA chips.
SGLang is built around RadixAttention, which reuses the cached prefix when many requests share the same starting text. That makes it strong for agents, few-shot prompts, and tool-use workloads where prompts overlap heavily.
Finally, TGI is HuggingFace's server with sane defaults and tight Hub integration, while llama.cpp and mlc-llm target on-device inference on laptops, phones, and browsers, running without a server GPU at all.
These frameworks are not a speed leaderboard. Each one wins on a different axis, so the match is about the defining technique and the workload it serves.
**vLLM** is the default. Its PagedAttention manages the KV cache in fixed blocks like virtual memory. Continuous batching keeps utilisation high. It adds new open models fast, so it is the safe pick when requirements are unclear.
TensorRT-LLM chases peak throughput on NVIDIA via ahead-of-time compiled kernels. You pay for it operationally. There is a build step, and it is NVIDIA-only. Choose it when you own H100s or B200s and need the last few percent of performance.
SGLang is defined by RadixAttention. It stores cached prefixes in a tree and shares them across requests. That shines on agent loops, few-shot prompts, and tool use where many requests share a long prefix. TGI is the HuggingFace server with sane defaults and tight Hub integration, trading a little throughput for ecosystem fit. The llama.cpp and mlc-llm pair is the edge option. They run on Apple Silicon, phones, and browsers, with no server GPU required.
Quick reference
TL;DR
Each serving framework has one defining strength: vLLM is the easy default via PagedAttention, TensorRT-LLM peaks on NVIDIA, SGLang wins on prefix reuse, TGI is HF-native, llama.cpp runs on the edge.
Imagine five food trucks that all serve the same dish but optimise for different things. One is the reliable all-rounder parked downtown that anyone can run. One is a Formula 1 pit crew, blazing fast but only on a specific track and tricky to operate. One is brilliant at reusing prepped ingredients when many orders share the same base. One comes pre-loaded with the chain's whole menu and plugs straight into headquarters. The last one is a tiny portable stove you carry in your backpack and run anywhere, no power hookup needed. Picking a serving framework is the same choice: do you want the easy default, peak speed on one brand of hardware, smart reuse of shared prompts, tight ecosystem integration, or the ability to run on a laptop?
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Reaching for TensorRT-LLM on a small or short-lived deployment The per-model compile pipeline and NVIDIA lock-in rarely pay back below large sustained scale.
Choosing SGLang for traffic with no shared prompt prefixes RadixAttention's prefix reuse gives little benefit when requests do not overlap, so vLLM is simpler.
Running llama.cpp for a high-throughput multi-tenant server Edge frameworks lack the batching and scheduling needed to saturate datacenter GPUs efficiently.
Picking TGI purely to chase maximum tokens per second It trades some raw throughput for HuggingFace integration; vLLM or TensorRT-LLM win on speed.
Quick scan · 2–4 min
Everything important, quickly.
3 min: why these are not a leaderboard + vLLM default (PagedAttention) + TensorRT-LLM peak NVIDIA + SGLang RadixAttention prefix reuse + TGI HF-native + llama.cpp edge, then a selection rule.
Side-by-side
Framework
Defining technique
Best fit
Main tradeoff
vLLM
PagedAttention + continuous batching
Default open-model serving
Not absolute peak on NVIDIA
TensorRT-LLM
Ahead-of-time compiled kernels
Peak throughput on owned NVIDIA
Build pipeline, NVIDIA-only
SGLang
RadixAttention prefix sharing
Agent, few-shot, tool-use overlap
Less general than vLLM
TGI
HuggingFace-native server
HF stack and Hub integration
Lower throughput than vLLM
llama.cpp / mlc-llm
Compiled or hand-tuned edge kernels
On-device, mobile, browser
Not for high-batch server scale
Real-world examples
Real products, models, and research that use this idea.
1vLLM (UC Berkeley origin) is the most widely deployed open serving engine in 2026 and backs many managed inference providers.
2TensorRT-LLM (NVIDIA) is the production reference for squeezing peak throughput from H100 and B200 GPUs in owned fleets.
3SGLang's RadixAttention is favoured for agent and tool-use stacks serving Llama 4 and Qwen 3 with heavy shared system prompts.
4Hugging Face TGI powers Inference Endpoints and integrates directly with the Hub for one-click deployment of Hub models.
5llama.cpp and mlc-llm run frontier-class quantised models on Apple Silicon laptops and phones with no server GPU required.
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 prefix caching?
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QWhen is TensorRT-LLM's compile step worth the operational pain over vLLM?
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QWhy might a team run llama.cpp in production rather than only on laptops?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Treating the frameworks as ranked fastest to slowest. They are not a leaderboard; each wins on a different axis, so the right pick depends on hardware, workload shape, and operational budget.