Run Local LLMs in 2026: Ollama vs LM Studio vs vLLM

Run a local LLM in 2026 and the first real decision is not which model — it is which runtime. Ollama, LM Studio, llama.cpp, vLLM, and MLX are the five engines worth knowing, and they are not interchangeable. One is a one-command desktop tool; another saturates eight NVIDIA GPUs under production load; a third only exists on Apple Silicon. Pick wrong and you either fight an over-engineered server on a laptop or starve a multi-user app of throughput.

The headline numbers do not help. A widely-cited Red Hat benchmark clocked vLLM at 793 tokens per second versus Ollama’s 41 on the same A100 — a 19x gap that gets quoted endlessly. What rarely travels with it is the caveat: at a single user, the two finish in a near dead heat. The gap is real, but it lives almost entirely under concurrency. Most guides lead with the 19x and bury the context.

This guide compares all five runtimes the way you actually choose between them — by chip, by concurrency, and by how much you want to operate. You get a five-runtime decision matrix, the Apple-Silicon format question (GGUF versus MLX) that most comparisons skip, a VRAM cheat sheet to map your hardware to a model size, and a clear read on when vLLM’s production muscle is worth the Docker. Every figure is traced to its source, and the vendor-stated ones are flagged as such.

The most-quoted number in the local-LLM world comes from a Red Hat Developer benchmark that pitted vLLM against Ollama on a single A100-PCIE-40GB running Llama 3.1-8B-Instruct, measured with GuideLLM over 300-second runs. At peak load, vLLM hit 793 tokens per second against Ollama’s 41 — the 19x gap everyone repeats.

The number that almost never travels with it: at a single user, both tools land in the same 130-180 tok/s band. The chart below shows what actually happens as you add concurrency. At eight simultaneous users, vLLM serves 187 tok/s to Ollama’s 82 (tuned), and tail latency tells the same story — vLLM’s P99 stays near 80 ms at peak while Ollama’s climbs to 673 ms. The mechanism is structural: vLLM’s PagedAttention and continuous batching pack many in-flight requests onto the GPU, whereas Ollama processes a near-serial queue.

Before the matrix, the cast. These five tools dominate local inference in 2026, and the fastest way to choose badly is to treat them as five flavors of the same thing. They are not — one is even the literal engine inside two of the others.

One practical thread ties four of the five together: an OpenAI-compatible endpoint. Ollama, LM Studio, llama.cpp’s llama-server, and vLLM all expose OpenAI-style routes, so most client code that targets the OpenAI SDK can repoint at localhost with a one-line base-URL change. The exception is mlx-lm, which ships no server of its own and is usually wrapped by Ollama or LM Studio. Watch one edge: Ollama’s compatibility layer omits a few fields the real API supports — logprobs, tool-choice, and logit-bias among them — so apps that lean on those features need to account for the gap. If you are still deciding whether to self-host open-weight models at all, settle that first; this post assumes you have.

LM Studio also pairs with LM Link to drive models from a phone — we cover that separately in our guide to running your largest local models from your iPhone via LM Link. It is a genuinely different use case from the runtime-selection question here, so we will not rehash it.

Most comparisons cover two or three of these tools and skip llama.cpp-as-a-standalone and mlx-lm entirely. Here is the full field across the axes that actually drive the choice: how hard it is to stand up, what hardware it targets, what model formats it eats, how it handles concurrency, and the situation it is built for.

Read the matrix as a gradient of intent. Ollama and LM Studio optimize for “running a model in the next five minutes.” llama.cpp optimizes for “running on hardware nobody else supports,” from a Raspberry Pi to an air-gapped server. vLLM optimizes for “serving the model to a hundred people at once.” mlx-lm optimizes for “wringing the most out of a Mac, and fine-tuning on it.” The columns that flip your answer fastest are concurrency and target hardware — everything else is preference.

For two years the standard mental model was “Ollama is llama.cpp with a nice CLI.” On Apple Silicon, that stopped being true in early 2026. Ollama added an MLX backend in preview, then promoted it to stable mid-year, replacing the llama.cpp Metal path on M-series Macs. In practice, today Ollama is MLX on a Mac — which collapses the old “Ollama versus MLX” framing that most 2025 comparisons still use.

The payoff, per Ollama’s own benchmark on an M5 Max running a Qwen 3.5 mixture-of-experts model, was a roughly doubled decode rate and a prefill lift of more than half versus the previous Metal backend. Those figures are vendor-stated and not yet widely replicated, so treat the magnitude as directional rather than exact — but the direction is clear, and it reflects a broader shift: Apple Silicon is now a legitimate inference platform, not a curiosity.

The bandwidth point is the one to internalize, because it governs the whole local-on-Mac economics. If you are weighing a high-memory Mac against a cloud subscription, the trade is real but not automatic — we walk through it in our piece on calculating the ROI of local AI versus cloud subscriptions. The short version: unified memory lets a Mac load models a consumer GPU cannot, but bandwidth, not capacity, sets how fast they run.

On Apple Silicon you are really making two decisions at once: which runtime, and which model format. GGUF — the llama.cpp lineage — is the universal, cross-platform option; it runs everywhere and is what Ollama auto-fetches. MLX is Apple-native and only runs on M-series chips. The two are not interchangeable files, and the choice has measurable consequences.

One third-party benchmark found MLX models a few percent smaller on disk and modestly faster on M-series hardware than their GGUF equivalents — for example, Llama 3.1 8B measured at 4.92 GB in GGUF versus 4.53 GB in MLX, and Qwen 2.5 32B at 19.8 GB versus 17.9 GB. Because that comparison is single-source, treat the throughput edge as directional, not a guaranteed number. Quality is close at 4-bit; the same write-up suggests GGUF’s Q4_K_M holds quality marginally better below 8B. If you want the full picture on what each quantization level costs you, choosing the right quantization level is its own decision worth getting right.

No runtime can run a model that does not fit in memory, so the hardest constraint is rarely speed — it is VRAM (or, on a Mac, unified memory). Weight memory scales almost linearly: estimate it as parameters × bytes-per-weight, where FP16 is 2.0 bytes, Q8_0 is about 1.0, and Q4_K_M is about 0.6 bytes per weight. The cheat sheet below applies that formula straight down the model ladder.

The Q4_K_M column is the one most people live in: a 7B fits in roughly 4 GB of weights, a 13B in under 8 GB, and a 70B in the 38-48 GB band that independent guides converge on. The “comfortable” column adds the KV-cache and runtime headroom you need in practice — which is why a 7B that weighs 4.2 GB still wants an 8 GB card, and why a 70B realistically needs 48 GB before you give it real context.

vLLM is the only runtime here that asks for Docker or a Python serving stack, and it is the only one that rewards the effort with serious production capability. Its signature is PagedAttention, which stores KV-cache tensors in non-contiguous, virtual-memory-style paged blocks instead of one pre-allocated contiguous chunk per sequence. That eliminates the memory fragmentation that throttles naive servers, and it is the mechanism behind the peak-throughput numbers from Section 01.

Two more capabilities make it a genuine SaaS backend rather than a faster Ollama. It supports multi-LoRA serving — hundreds of fine-tuned adapters can share one base model with per-request adapter switching and no reload latency, which is exactly what you want when every customer has their own fine-tune. And it covers 200+ model architectures while speaking OpenAI, the Anthropic Messages API, and gRPC, so it slots into most existing client code. SGLang is a credible competitor that can edge vLLM on shared-prefix workloads like RAG in some benchmarks, but for raw model variety and the fastest path to a running server, vLLM is the more common default.

The honest framing: vLLM is a production tool that happens to run locally, not a local tool that happens to scale. If the workload is a multi-user app, it is worth every line of the compose file — and the crossover point where self-hosting beats per-token API pricing is a real calculation, which we lay out in our look at when cloud inference is actually cheaper than local GPU hardware. If it is one developer and one model, the simpler runtimes win on every axis that matters to you.

Strip away the benchmarks and the decision is mostly about who you are and what you are building. Here is the fast read by situation — and the privacy case for going local at all is covered in our guide to the privacy benefits of local inference if you still need to make it.

Most teams end up running two of these, not one: a friendly tool for local development and a production server for the deployed app. If you are standing up local or self-hosted inference as part of a broader build, our AI digital transformation engagements start with exactly this kind of runtime-and-hardware decision, and our web development work wires the resulting endpoint into a real product.