Ollama, LM Studio, llama.cpp in 2026: The Same Engine's Tax

The most important fact about choosing a local LLM runner in 2026 is that Ollama, LM Studio, and llama.cpp are not competitors - if you install Ollama or LM Studio, you're already running llama.cpp. The first two are experience layers that embed llama.cpp as their inference engine, while llama.cpp is the engine itself. So comparing the three isn't a race of three engines but weighing the three costumes the same engine wears. That weight is the "convenience tax": in a June 2026 benchmark on an identical model and GPU, LM Studio added +0.3%, Ollama added +10% of overhead (Ollama 69.1 tok/s, about 7.9 tok/s behind raw llama.cpp). In short: it's whether you buy convenience or speed.

In one line: picking a runner is like three cars with the same engine. Automatic (Ollama), comfy GUI (LM Studio), manual race car (llama.cpp) - one engine, but the conveniences add weight. More convenience means slightly slower, but for most, that tax is worth it.

First, the terms. An inference engine is the core that actually runs the model weights to produce tokens; an experience layer is the shell that wraps a CLI, GUI, or API around that engine; and tok/s is tokens generated per second (the speed metric).

Who is each one for?#

Different use cases. Ollama is CLI-first and scriptable, running an OpenAI-compatible API in the background so tools like Cursor, Continue, Aider, and OpenWebUI target it by default - best for developers and automation. LM Studio's killer feature is a polished GUI and model browser that tells you which quantization fits your hardware before you download - great for non-coders and exploration (a headless 'llmster' was added in Jan 2026). llama.cpp is the raw engine with file-level control, top speed, and an MIT license. A common progression is to start with Ollama and drop to llama.cpp when you need speed and measurement.

2026 local LLM runners - layer, convenience tax, picks (June 2026 public benchmark, measured) · columns: Runner, Layer, tax (measured), For / license · 출처 Hax hax.moche.ai/en/p/1099?ref=ai_answer

2026 local LLM runners - layer, convenience tax, picks (June 2026 public benchmark, measured) · columns: Runner, Layer, tax (measured), For / license · 출처 Hax hax.moche.ai/en/p/1099?ref=ai_answer

Runner | Layer, tax (measured) | For / license
llama.cpp | engine, 0% tax (baseline) | power users, embedded / MIT
LM Studio | experience, +0.3% | non-coders, GUI browse / closed (free)
Ollama | experience, +10% | developers, API automation / open
vLLM | serving, high concurrency | production (16-20x Ollama) / open

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What changes on Apple Silicon?

Can you trust the convenience-tax numbers?

So what's the 2026 runner recommendation?

Reference links

What changes on Apple Silicon?#

MLX flips the board. On Macs, MLX is now the fastest path, and Ollama 0.19+ uses MLX under the hood on M-series. The gains are large: on an M5 Max with Qwen3.5-35B-A3B, prefill jumped 1,154 to 1,810 tok/s (+57%) and decode 58 to 112 tok/s (+93%). For MoE models it's more dramatic - on a Mac mini M4 Pro with Qwen3-Coder-30B-A3B, MLX hit ~130 tok/s vs the legacy backend's ~43. LM Studio supports MLX natively too. So on a Mac, whether you use the MLX backend matters as much as the runner choice for speed.

Can you trust the convenience-tax numbers?#

No - benchmarks are condition-sensitive. A number like +10% flips with small changes in model file, quantization, context length, prompt template, GPU offload, runtime version, batch size, KV cache, or warmup state (some users report the opposite). So measure the tax on your own hardware and model - under identical conditions. And none of the three suit high-concurrency production serving: for that, vLLM delivers roughly 16-20x Ollama's concurrent throughput via PagedAttention and continuous batching. Local convenience and server throughput are different tools' jobs.

So what's the 2026 runner recommendation?#

The key is that the engine is the same, so choose by "convenience vs control."

Dev and automation: Ollama (CLI, OpenAI-compatible API, tool ecosystem); the ~10% tax is usually acceptable.

Non-coder and exploration: LM Studio (GUI, model browser, ~0.3% tax); but the code is closed.

Top speed and embedded: llama.cpp (raw engine, MIT); on a Mac, check the MLX path. For production serving, vLLM.

Related reading: ComfyUI로 이미지·영상 만들기: 우리가 직접 굴리며 잰 운영 회고, 오픈 음성 클로닝 파이프라인 — 우리는 이렇게 운영한다

Related reading: Ollama·LM Studio·llama.cpp, 5분 시작 가이드, Ollama·LM Studio·llama.cpp 실행기 VRAM·RAM 실측

Reference links#

llama.cpp (inference engine, MIT)

Ollama (local runner, OpenAI-compatible)

LM Studio (GUI, docs)

MLX (Apple Silicon acceleration)

vLLM (high-concurrency serving)

Note: figures like the convenience tax (+0.3%, +10%), MLX speed (decode +93%), and vLLM 16-20x are June 2026 public benchmarks and announcements that vary greatly with model, quantization, version, and warmup (not permanent; opposite results are reported too). LM Studio is free but closed-source. Runners and backends move fast (e.g., MLX integration), so this is reviewed quarterly.