LM Studio vs Ollama for Local LLMs: How to Actually Choose

The question of lm studio vs ollama for local llms almost never gets settled by tokens per second, even though that is how most comparisons argue it. It gets settled about three weeks in, when the thing you prototyped on a laptop has to run somewhere else: a headless box, under systemd, behind an endpoint other people call. Pick on throughput first and you tend to migrate twice.

Both load the same class of model, both speak the OpenAI API, and on an NVIDIA GPU both are ultimately driving GGML kernels. The differences that matter are licensing, defaults, and packaging.

What each one actually is

Ollama is a Go daemon with a CLI in front of it, MIT licensed, binding 127.0.0.1 port 11434 by default. It pulls models from its own registry or from Hugging Face. It began as a wrapper around llama.cpp and has been moving off it: in May 2025 Ollama announced its own inference engine built directly on GGML, arguing that model isolation lets each model “expose its own projection layer, aligned with how that model was trained.”

LM Studio is a desktop application: chat UI, a Hugging Face model browser, per-model load configuration, and an OpenAI-compatible server on port 1234. Since 8 July 2025 it has been free at work as well as at home, with no form to fill in.

Free is not the same as open, and this is the first hard filter. LM Studio’s app terms license the software for personal and internal business purposes and forbid sublicensing, reselling, service-bureau and SaaS use. The lms CLI and the MLX engine are MIT separately, but the application is not. Ollama is MIT end to end. If you plan to embed a local runtime in something you ship to customers, that decides it before anything else.

The defaults that quietly change your results

Neither set of defaults is wrong, but they are tuned for different situations and both surprise people.

Ollama, per its FAQ:

• Models “are kept in memory for 5 minutes before being unloaded.” On a low-traffic internal service, that means most requests pay a full load from disk before the first token.
• OLLAMA_NUM_PARALLEL defaults to 1. Concurrent callers queue.
• OLLAMA_MAX_LOADED_MODELS defaults to 3 times the number of GPUs, or 3 for CPU inference.
• OLLAMA_CONTEXT_LENGTH defaults to 4096 tokens, regardless of what the model supports.

LM Studio, per its TTL docs:

• JIT-loaded models get a 60 minute TTL, set per request with a ttl field in seconds.
• Auto-Evict keeps at most one JIT-loaded model resident, unloading the previous one before loading the next.

That 4096 default is the single most expensive line in this article. It does not raise an error. Your 128k-context model silently gets a 4k window, long documents get truncated, and the bug report reads “the model got worse after we moved it to the server.” Set it explicitly.

Wiring it up

The API surfaces are close enough that one client handles both. What changes is the base URL and the lifecycle knob.

from openai import OpenAI

BACKENDS = {
    "lmstudio": {"base_url": "http://localhost:1234/v1", "extra": {"ttl": 3600}},
    "ollama":   {"base_url": "http://localhost:11434/v1", "extra": {"keep_alive": "1h"}},
}

cfg = BACKENDS["lmstudio"]
client = OpenAI(base_url=cfg["base_url"], api_key="not-needed")

resp = client.chat.completions.create(
    model="qwen3-8b",
    messages=[{"role": "user", "content": "Summarise this changelog."}],
    temperature=0,
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "summary",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {"headline": {"type": "string"}},
                "required": ["headline"],
            },
        },
    },
    extra_body=cfg["extra"],
)

For Ollama the durable version of that config is an environment override on the unit, not a per-request field:

# ~/.config/systemd/user/ollama.service.d/override.conf
[Service]
Environment="OLLAMA_CONTEXT_LENGTH=32768"
Environment="OLLAMA_KEEP_ALIVE=1h"
Environment="OLLAMA_NUM_PARALLEL=4"

Structured output works on both and is implemented differently. LM Studio takes an OpenAI-style response_format, backed by llama.cpp grammar sampling for GGUF and the Outlines library for MLX, with the documented caveat that “not all models are capable of structured output, particularly LLMs below 7B parameters.” Ollama takes a format field carrying a JSON schema. Check the gaps before you commit: Ollama’s OpenAI layer does not support logprobs, tool_choice, logit_bias, user or n on /v1/chat/completions.

Apple Silicon is the only real performance fork

On CUDA both are running GGML, and throughput differences there are almost always configuration rather than engine: quantization level, context length, and whether every layer actually fit on the GPU.

On Apple Silicon they genuinely diverge, because LM Studio ships an MLX runtime alongside llama.cpp and Ollama does not. LM Studio’s launch post for that feature demoed Llama 3.2 1B on an M3 Max at roughly 250 tokens per second. Treat that as a vendor figure, because it is one.

Be skeptical of tokens-per-second tables in comparison articles generally. Most omit the quantization, the context length, the batch size, and whether the model was fully offloaded, and every one of those moves the number more than the choice of application does. There is no widely cited independent benchmark of the two under matched conditions, so the only figure worth acting on is one from your own hardware, quant and prompt lengths.

Model sourcing, and knowing what you loaded

Ollama’s registry uses short names such as llama3.2, which hide the quantization behind a tag. It also runs Hugging Face GGUFs directly: ollama run hf.co/{username}/{repository}, with Q4_K_M used by default when present and a :{quantization} suffix to pick another.

LM Studio browses Hugging Face directly and shows the quantization in the picker with a fit estimate against your machine, which is the friendlier surface when comparing several builds. The underlying decision is identical either way, and it is covered in choosing a GGUF quantization level; to turn a model and context length into a memory number, use the VRAM and GGUF sizer.

Caveats worth budgeting for

• Exposing the port exposes an unauthenticated model. Both default to localhost. Rebinding to 0.0.0.0 so a teammate can reach it publishes an endpoint with no auth and no input filtering, a different threat model from calling a hosted API. Anything reachable beyond your machine needs a reverse proxy with auth in front and should be treated as an injection target.
• Running both means two model caches. Ollama keeps blobs under ~/.ollama/models on macOS, LM Studio keeps its own directory, and nothing deduplicates across them. GGUF files are not small.
• Neither exports request-level telemetry. No per-request latency, queue depth or token accounting by default. If a local model goes behind anything real, that instrumentation is yours to add and the usual monitoring practice for served models applies unchanged.
• Headless LM Studio works, but it is the newer surface. The documented service path is the llmster daemon via lms daemon up, or the app in headless mode via lms server start. Ollama has been daemon-first since the beginning.

Picking one

Redistribution, containers, CI, or a box you only reach over SSH: Ollama. The MIT license permits shipping it, the daemon is the primary interface rather than an added mode, and environment-variable configuration fits configuration management.

Apple Silicon, model evaluation, or wanting to see GPU offload and context settings before a model loads: LM Studio, with MLX as a real reason rather than a checkbox.

Running both is common and conflicts with nothing, since the ports differ. If you are still sizing the machine rather than choosing software, LM Studio’s system requirements sets out the floors first.