Pick the wrong LLM inference server and you’ll spend a weekend fighting configuration for performance you could have had in ten minutes. Pick the wrong one at the other end of the scale and you’ll wonder why your GPU sits at 30% utilisation while users wait.
The four LLM inference servers here get compared constantly, usually as though they’re competing for the same job. They aren’t. Three of them are genuinely different answers to the question of who’s using the model and how many of them there are at once. This guide sorts out which is which.
In this guide:
- The One Question That Decides It
- Ollama: The Sensible Default
- LM Studio: The Desktop App
- llama.cpp: The Engine Underneath
- vLLM: The Serving Layer
- Side by Side
- Which LLM Inference Server Should You Pick?
- Common Mistakes
The One Question That Decides It
How many people are hitting the model at the same time?
That’s it. That’s the question that separates these LLM inference servers, and almost every other consideration is downstream of it.
One user at a time — you, on your own machine, one request in flight. Ollama, LM Studio, or llama.cpp directly. All three will feel fast because there’s no queue to manage.
Many users at once — an internal tool, an API, anything where requests overlap. vLLM, and it isn’t close. The gap in concurrent throughput between a batching server and a single-stream runner is not a tuning difference; it’s an architectural one.
The reason this matters so much is a technique called continuous batching. A single-user LLM inference server processes one request, finishes it, then starts the next. vLLM keeps a pool of requests in flight and slots new ones into the batch as earlier ones complete, which keeps the GPU saturated instead of idling between requests. On overlapping traffic that difference compounds fast.
Most of the “vLLM is so much faster” claims you’ll see online come from benchmarks with concurrent load. Run a single request and the gap largely disappears — sometimes reverses, since vLLM carries more overhead per request.
Ollama: The Sensible Default
Ollama is the LLM inference server most people should start with, and for many never need to leave.
It wraps llama.cpp in a clean CLI and a local HTTP API, handles model downloads with a single command, manages quantisation formats automatically, and serves an OpenAI-compatible endpoint so existing client code mostly just works. Install, ollama run, done.
What it’s good at: getting from nothing to a working local model faster than anything else. Model management is genuinely pleasant. Its OpenAI-compatible API means you can point most tooling at it without changes, which makes it the easy backend for a local AI agent.
Where it stops: concurrency. It handles overlapping requests, but not with the throughput of a purpose-built serving layer. It also abstracts away enough that squeezing out the last 20% of performance means dropping to llama.cpp anyway.
Pick it if: you’re one person, a small team, or prototyping. Which is most people.
LM Studio: The Desktop App
LM Studio covers Ollama’s territory as an LLM inference server with a graphical interface and a model browser, aimed at people who’d rather not use a terminal.
It runs on Windows, macOS and Linux, lets you browse and download models from Hugging Face in-app, shows you what will fit in your available memory before you commit to a download, and exposes a local server endpoint when you want to connect other tools to it.
What it’s good at: discovery and approachability. The memory-fit indicator alone saves a lot of failed downloads, and being able to see quantisation options side by side helps people learn what those choices actually do.
Where it stops: it’s a desktop application, not infrastructure. You wouldn’t deploy it. Automation and scripting are second-class compared to a CLI-first tool.
Pick it if: you’re evaluating models, learning what your hardware can handle, or you simply prefer a GUI. It’s also the one I’d point a non-engineer at.
llama.cpp: The Engine Underneath
llama.cpp is the C++ engine that a large share of the local AI ecosystem is built on, including Ollama. Working with it directly means giving up convenience for control.
It runs on almost anything — CUDA, Metal, ROCm, Vulkan, plain CPU — and it’s the reference implementation for GGUF quantisation. When a new quantisation method appears, it usually appears here first.
What it’s good at: performance tuning and unusual hardware. Explicit control over GPU layer offloading, context size, thread counts, and batch parameters. If you’re running on something awkward — an old card, an ARM board, a CPU-only box — this is often the only thing that works well.
Where it stops: ergonomics. You manage model files yourself, you build with the right flags for your hardware, and you read documentation. There’s no model registry doing it for you.
Pick it if: you need maximum performance from specific hardware, you’re on an unusual platform, or you’re embedding inference into your own application.
vLLM: The Serving Layer
vLLM is a different category of tool. As an LLM inference server it targets a different problem entirely. It’s not for running a model on your laptop; it’s for serving one to many users.
Its central innovation, PagedAttention, manages the attention key-value cache the way an operating system manages virtual memory — in pages rather than one contiguous block. That drastically reduces memory waste, which means more requests fit in the same VRAM, which means bigger batches and higher throughput. Continuous batching sits on top of that.
What it’s good at: concurrent throughput, and nothing else comes close. It also supports tensor parallelism across multiple GPUs, and serves an OpenAI-compatible API so it drops into existing architectures cleanly.
Where it stops: it’s NVIDIA-centric in practice, it wants server-class GPUs, and it will not run comfortably on a laptop. Setup is meaningfully more involved than ollama run. For a single user it’s slower per request than the alternatives, because you’re paying batching overhead with nothing to batch.
Pick it if: you’re serving a model to real concurrent traffic, on a GPU server, and throughput per dollar matters.
Side by Side
| Ollama | LM Studio | llama.cpp | vLLM | |
|---|---|---|---|---|
| Best for | Solo dev, prototyping | Exploring models, GUI users | Tuning, odd hardware | Concurrent serving |
| Interface | CLI + API | Desktop GUI | CLI / library | Server + API |
| Setup effort | Minutes | Minutes | Hours | Half a day |
| Concurrency | Limited | Limited | Limited | Excellent |
| Hardware | Broad | Broad | Broadest | NVIDIA server-class |
| Model format | GGUF | GGUF | GGUF | Safetensors / full weights |
| OpenAI-compatible API | Yes | Yes | Yes (server mode) | Yes |
One format note worth flagging, because it trips people up: the first three run quantised GGUF files, while vLLM primarily serves full-precision or differently-quantised weights. You don’t move a model between them by copying a file.
Which LLM Inference Server Should You Pick?
Just starting with local models? Ollama is the LLM inference server to begin with. Nothing else gets you working as fast, and you can move later.
Want to browse and compare models visually? LM Studio, then switch to Ollama once you know what you’re running.
Stuck with an older GPU, an AMD card, or CPU-only? llama.cpp built for your specific backend. The tuning work pays off here more than anywhere else.
Building an internal tool for a team? Depends on the team size. Under about ten occasional users, Ollama on a decent box is usually fine. Above that, or with sustained overlapping usage, vLLM.
Serving an API to customers? vLLM, on server hardware — it’s the only LLM inference server here designed for it. This is the case it exists for.
Not sure how much VRAM any of this needs? That’s the prior question, and it’s covered in our guides to best local LLMs and how much VRAM you need.
Quantisation: The Setting That Matters Most
Whichever LLM inference server you land on, quantisation affects output quality more than the server choice does, and it’s the setting people get wrong most often.
Quantisation reduces the numerical precision of model weights to shrink memory footprint. A 70-billion-parameter model at full precision needs far more memory than most people have; at 4-bit it becomes feasible on a single high-end consumer card. The trade is quality, and the curve isn’t linear.
Rough guidance from running these: 8-bit is close to indistinguishable from full precision for most tasks. Around 5–6 bit is the sweet spot where quality loss is minor and memory savings are large. 4-bit is usable and widely deployed, with noticeable degradation on reasoning-heavy work. Below 4-bit, quality falls off sharply enough that a smaller model at higher precision is usually the better trade.
The mistake is chasing parameter count. A 13B model at 6-bit will frequently outperform a 70B at 2-bit on the same hardware, despite the bigger number looking more impressive. Test both on your actual task — every LLM inference server here makes swapping quantisations easy enough that there’s no excuse for guessing.
Running More Than One
Nothing stops you running more than one LLM inference server. The pattern I see most often in practice: LM Studio for exploring and comparing models, Ollama as the everyday local backend for development, and vLLM on a server for anything user-facing.
They coexist cleanly because they all expose OpenAI-compatible endpoints. Your application code points at a base URL, and switching means changing that URL rather than rewriting integration logic. Worth designing for from the start — hardcoding a provider-specific client is the thing that makes migration painful later.
Common Mistakes
Choosing your LLM inference server by benchmark headline. Reaching for vLLM too early is the usual form of this. It’s the tool with the best benchmark numbers, which makes it tempting. If you’re the only user, you’re taking on real operational complexity for throughput you can’t use.
Assuming Ollama can’t scale at all. It can handle modest concurrency perfectly well. Plenty of small internal tools run on it happily. Measure your actual load before migrating.
Comparing throughput at batch size one. Almost every benchmark argument online is two people measuring different workloads. Single-stream and concurrent numbers say different things, and neither is wrong.
Over-quantising. Dropping to very aggressive quantisation to fit a bigger model often produces worse output than a smaller model at higher precision. Test both on your actual task rather than assuming more parameters wins.
Ignoring the tokens-per-second you actually need. Human reading speed is roughly 5–10 tokens per second. If you’re already comfortably above that for a single user, further optimisation of your LLM inference server is time spent on a number nobody experiences.
LLM Inference Server FAQ
Can I use Ollama in production?
For internal tools with modest, bursty usage — yes, plenty of teams do. The failure mode is sustained concurrent load, where request queuing shows up as latency. Measure your real concurrency before assuming you need to migrate; a lot of “we need vLLM” decisions are made on hypothetical traffic that never arrives.
Does vLLM work on consumer GPUs?
It can run on high-end consumer cards, but it’s built assuming server-class hardware and plenty of VRAM. On a single consumer GPU with one user, you’re carrying the complexity of a batching LLM inference server and getting none of its benefit.
What about AMD cards?
llama.cpp is the most reliable path, with ROCm or Vulkan backends depending on your card. Ollama inherits that support. vLLM’s AMD story has improved but remains second-class compared to CUDA. See our best GPUs for local AI guide for what’s realistic on specific hardware.
Is GGUF worse than full weights?
GGUF is a container format for quantised models, so the question is really about quantisation level rather than format. At 6-bit and above the quality difference is small for most tasks. The practical constraint is that GGUF and safetensors aren’t interchangeable between these tools.
How do I pick a quantisation without guessing?
Download two levels of the same model, run both against twenty prompts representative of your actual work, and compare outputs side by side. It takes an hour and it beats every general recommendation, including the ones above.
The Bottom Line
Choosing an LLM inference server comes down to one decision: single-stream or concurrent.
For single-stream — which covers most individual use, prototyping, and small internal tools — Ollama is the default, LM Studio is the friendlier variant, and llama.cpp is where you go when you need to squeeze the hardware. For concurrent serving, vLLM is the answer and the others aren’t really competing.
The mistake I’d most want you to avoid is starting at the complicated end. Run Ollama first. If you hit a wall, you’ll know exactly which wall, and that tells you where to go next.
Related: best local LLMs, local AI agent, and best GPUs for local AI.