LM Studio adds headless llmster daemon; Ollama vs. LM Studio choice still comes down to licence and workflow

The dev.to comparison credits llmster with closing LM Studio's headless gap ^[3]. The desktop app exposes its server controls through a Developer Mode ^[17]. An Ollama install is already an HTTP endpoint, and pointing a coding agent at localhost:11434 is a one-line change ^[10]. llmster installs with one command, `curl -fsSL https://lmstudio.ai/install.sh | bash`, and runs headless on a server ^[4].

Under the wrapper, both tools run llama.cpp and both now ship Apple MLX paths. The comparison concludes the split is about workflow, not raw speed ^[11]. The post's one measurement varied the model. Gemini 3.8 Flash (High) and Claude Opus 4.6 (Thinking) each scored 17 of 17 on a machine-checked, seven-constraint planning task, with median wall times of 23 and 67 seconds over three trials per model ^[14]. Opus took about 2.9 times as long ^[2]. For that result to inform a runtime choice, the same GGUF would have to be timed on llmster and on Ollama, on one machine, at the same context length.

Memory binds before either runtime does. The post lists qwen3.8:27b as an 18 GB GGUF at 256K context ^[12]. A 16 GB laptop is 2 GB short before the operating system has claimed anything ^[1]. The post's answer is a smaller or more aggressively quantised build, on either tool ^[12].

Licence is where the tools still part ways. The comparison states the rule this way: "If your policy requires auditable code, that is a hard stop; if it requires a clear right to use, LM Studio satisfies it." ^[15] On LM Studio's side, the MIT grant covers the lms CLI ^[2]. On a headless server, the daemon is what gets deployed. The post does not say which licence covers it.

The daemon's API choices are sound. Version 0.4.0 added a stateful /v1/chat REST API with local MCP support gated behind permission keys ^[5]. A key check before a model can reach local tools is the right default for a process listening on a shared server. Version 0.4.22 made tool-returned images work across the OpenAI-compatible /v1/responses and /v1/chat/completions endpoints and the Anthropic-compatible /v1/messages ^[6].

My context is a shared GPU server under a rule that deployed code must be auditable. There I would keep Ollama until llmster's licence terms are confirmed. A team whose rule is a documented right to use, with agent tooling written against Claude's API shape, has a concrete reason to try llmster first ^[6].