LM Studio vs Ollama: Which Local LLM App Should You Use in 2026?

What are LM Studio and Ollama?

Ollama is an MIT-licensed local LLM runtime: a command-line tool and background server that downloads, serves and runs open models on your own machine. Its official repository lists support for Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen and Gemma, and it exposes a local API that editors and agents can call.

LM Studio is a free desktop application with a point-and-click interface. Per its site, it runs local LLMs using llama.cpp and MLX, adds offline voice transcription, and links up to five devices through LM Link. The two overlap in purpose but not in form: Ollama is built to be scripted, LM Studio is built to be clicked.

LM Studio vs Ollama pricing

Both products are free at the core and charge only for hosted cloud inference. Verified on the official pricing pages on 2026-10-04:

• Ollama: Free tier at $0 (run models locally, starter usage credits, no service fees); Pro at $20/month or $200/year ($16.67/month billed annually); Max at $100/month. Concurrency is capped at 1 request on Free, 3 on Pro, and 10 on Max and Team.
• LM Studio: Free tier at $0 (local LLMs, Bionic Agent, offline voice transcription, LM Link for up to 5 devices, limited web search); Bionic+ at $20/month, which adds US-hosted open-source models including Kimi K3, GLM 5.3 and DeepSeek V4 Flash; Bionic+ Pro at $100/month with 5x usage limits and early access.

The two price ladders match almost exactly. If you only run models on your own hardware, neither app costs a cent; the subscriptions buy cloud capacity, not the local runtime.

The new third option: ds4 (DwarfStar 4)

A third contender reached the Hacker News front page on 2026-10-02, when ds4 (DwarfStar 4) drew 341 points. It is a local inference engine written in C by the antirez account that also created Redis, and it is MIT-licensed with 23,173 GitHub stars as of 2026-10-04.

ds4 is deliberately narrow. Its site states it is not a generic GGUF runner: instead of accepting arbitrary model files, it validates a short list of layouts end to end, covering DeepSeek V4 and V4.1, GLM 5.x and Qwen3.8 across Metal (Apple Silicon), CUDA (NVIDIA DGX Spark) and ROCm (AMD Strix Halo). It applies asymmetric 2-bit quantization that compresses the routed experts while keeping more critical weights at higher precision.

The project's own benchmark row for DeepSeek V4 Flash on an M5 Max with 128GB unified memory, q2, reports 39.4 tokens/second at 2,048-token context and 27.6 tokens/second at 65,536, with prefill falling from 790.2 to 398.5 tokens/second over the same range. Those are the vendor's numbers from ds4-bench, not independent tests.

Backends and model support compared

Ollama and LM Studio both lean on llama.cpp, the MIT-licensed C/C++ inference engine with 130,231 GitHub stars. LM Studio names llama.cpp and MLX as its engines; Ollama pairs a local runtime with a cloud tier that serves larger hosted models on pay-as-you-go credits.

ds4 goes the other way. Rather than supporting any GGUF file, it ships a validated layout per supported model family. That narrowness is exactly what lets it push 2-bit quantization further than a general-purpose runner, and it is also why it will not load a random community fine-tune. If catalog breadth matters more than squeezing a specific frontier model onto your laptop, Ollama or LM Studio is the safer bet.

Which local LLM app should you choose?

The right pick follows how you work, not which benchmark table looks fastest:

• Choose Ollama if you want an open-source CLI and server you can wire into scripts, editors or agents, and you need a model catalog that stretches beyond one vendor's family.
• Choose LM Studio if you want a graphical app, Apple Silicon MLX acceleration on a laptop, and extras such as offline voice transcription without touching a terminal.
• Choose ds4 only when the models it supports are the ones you actually want (DeepSeek V4, GLM, Qwen3.8) and you have the memory for them, since its hardware guide puts Qwen3.8 Q2 at 64GB and DeepSeek V4.1 Q4 or PRO in the 512GB class.

The honest catch with running models locally

Going local trades subscription fees for hardware. ds4's Qwen3.8 Q2 layout keeps 41.73GiB resident and streams a 95.37GiB n-gram table from disk, so a 32GB machine cannot hold it. DeepSeek V4 Flash Q2 is the practical baseline at 128GB, while V4.1 Q4 and PRO belong to the 512GB class, or to two 512GB machines in the distributed setup.

Ollama and LM Studio are friendlier to smaller machines because they run a wide range of quantized GGUF and MLX models, but their free local tiers carry limits: Ollama Free allows one concurrent request, and LM Studio's free tier includes only limited web search. Below roughly 32GB of memory, a metered cloud API is often cheaper than the hardware upgrade that local inference would demand.

At a glance

LM Studio vs Ollama vs ds4: verified on official pages, 2026-10-04

FAQ

Is LM Studio free?

Yes. Running models locally in LM Studio costs $0. The paid tiers are for cloud inference: Bionic+ is $20 per month for US-hosted open-source models, and Bionic+ Pro is $100 per month for 5x usage limits and early access.

Does Ollama have a free plan?

Yes. Ollama's Free tier is $0 and runs models locally with starter usage credits and no service fees, but it allows only one concurrent request. Pro raises that to 3 for $20 per month, and Max to 10 for $100 per month.

Can I run DeepSeek V4 locally for free?

Yes, with ds4, which is MIT-licensed and free. The constraint is hardware: its guide puts DeepSeek V4 Flash Q2 at the 128GB memory class, while V4.1 Q4 and PRO need 512GB-class memory. Ollama and LM Studio can also serve DeepSeek weights on smaller machines at lower quantization.

Related reading

best local LLM tools, LM Studio Bionic cloud pricing, ToolBistro radar index, What Are the Best Ollama Alternatives for Running LLMs Locally in 2026?

Sources

• Ollama official pricing
• LM Studio official pricing
• ds4 (DwarfStar 4) official site
• ds4 benchmarks (official)
• ds4 hardware requirements (official)
• GitHub: antirez/ds4
• GitHub: ollama/ollama