Match each production serving framework to its defining strength
Drag each answer to line up with its matching prompt
vLLM
On device / consumer hardware (Apple Silicon, mobile, browser); pure C++ or compiled kernels; no server GPU required
TensorRT-LLM
Best in class continuous batching + PagedAttention; broadest open model support; the default choice when you're unsure
SGLang
HuggingFace stack with sane defaults; tighter HF Hub integration; less raw throughput than vLLM
TGI (Text Generation Inference)
RadixAttention + programmatic prompt language; best for agent / few-shot / tool use workloads with prefix overlap
llama.cpp / mlc-llm
Peak NVIDIA throughput via compiled kernels; harder operationally, NVIDIA only
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?
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.
By 2026 the question is rarely "how do I serve a model?" but "which of these five mature frameworks fits my hardware, workload, and operating budget?" The frameworks look interchangeable from a distance because they all accept an OpenAI-style API and stream tokens. Up close, each is organised around one defining technique that determines where it wins.
The single biggest mistake candidates make is treating them as a speed leaderboard, as if you simply pick the one with the highest benchmark number. That framing fails immediately in practice. The fastest framework on paper may be NVIDIA-only, may demand a per-model compile pipeline, or may give you nothing for a workload whose prompts never overlap. The skill an interviewer probes for is matching technique to context.
This deep dive takes each framework in turn, names its defining technique, explains the mechanism behind it, and states the workload where it is the right call. By the end you should be able to take a one-line deployment brief and name the correct framework with a one-sentence justification, which is exactly the senior signal this question is testing for.
vLLM: the default, built on PagedAttention
vLLM is the framework you should name when you have no other information. Its defining contribution is PagedAttention, which manages the KV cache the way an operating system manages memory. Instead of giving each request one contiguous max-length slab, it carves the cache into fixed-size blocks indexed through a page table.
The fragmentation problem this solves is real and expensive. Under naive contiguous allocation, a request that might generate 4000 tokens reserves room for its full maximum up front, even though most requests finish far short of that. The unused tail sits idle and cannot serve another request. Across a busy server this wasted reservation can consume the majority of HBM, and it directly caps how many requests you can run concurrently. Paging removes that waste by allocating a block only when a token actually needs it, and reference-counted blocks let several requests share an identical prefix without duplicating its KV state.
That density is then converted into throughput by continuous batching, which admits and retires requests at the token level rather than waiting for whole batches to finish. A short request leaves the batch the moment it completes, and a freshly arrived request joins on the next decode step, so the GPU is never stalled waiting for one slow generation. Together these two ideas push utilisation toward the hardware ceiling and reach several times the throughput of naive serving.
The deeper reason vLLM is the default is not any single benchmark. It is breadth and momentum. New open models tend to land in vLLM within days of release, the project has the largest contributor base, and the operational story is simple: install, point at a model, serve an OpenAI-compatible endpoint. Unless you have a concrete reason to deviate, vLLM is the rational starting point and the answer you give when the brief is vague.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
| 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 products, models, and research that use this idea.
- vLLM (UC Berkeley origin) is the most widely deployed open serving engine in 2026 and backs many managed inference providers.
- TensorRT-LLM (NVIDIA) is the production reference for squeezing peak throughput from H100 and B200 GPUs in owned fleets.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow does SGLang's RadixAttention differ from vLLM's prefix caching?
Both reuse shared KV prefixes, but RadixAttention organises cached prefixes in a radix tree keyed on tokens, matching and sharing automatically across many requests. Discuss how the tree handles branching prompts and how eviction interacts with shared nodes.
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.
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.
60 second bullets to scan on the way to the call.
Why these frameworks are not a single speed ranking
vLLM's PagedAttention and what fragmentation problem it solves
Primary sources. Browse if you want the original framing.
Same topic, related formats. Practice these next.