Ollama is a lightweight, command-line focused tool optimized for running open-source LLMs locally with minimal setup, while LM Studio provides a full-featured graphical interface with advanced model management, chat history, and local API serving. Ollama excels at simplicity and performance; LM Studio prioritizes user experience and feature richness.
Ollama vs LM Studio — Side-by-side comparison on A Versus B
Local large language model runtime for running open-source models offline on consumer hardware.
Feature-rich desktop application for discovering, downloading, and running open-source LLMs locally with intuitive GUI and advanced controls.
User Interface Type. Setup Complexity. Model Library Size. RAM Usage (7B Model). Native API Support. Chat History Persistence. Multi-GPU Support
Choose Ollama if you need a lightweight, fast-to-deploy local LLM runner with minimal overhead and prefer command-line workflows or want to integrate into existing applications via API. Choose LM Studio if you want a polished desktop experience with advanced model discovery, conversation management, GPU optimization controls, and don't mind slightly higher resource usage for convenience features.
Ollama: Full native support | LM Studio: N/A
Ollama: Command-line (CLI) | LM Studio: N/A
Ollama: Manual CLI required | LM Studio: N/A
Ollama: 3 (macOS, Linux, Windows) | LM Studio: N/A
Code Generation Accuracy (HumanEval Benchmark)
Ollama: 68% (Llama 2 70B) | LM Studio: N/A
Monthly Operating Cost (5,000 token average session)
Ollama: $0 (hardware only) | LM Studio: N/A
Ollama: 8GB (Llama 2 7B) | LM Studio: N/A
Ollama: 5-10s (CPU) / 2-4s (GPU) | LM Studio: N/A
Data Privacy (0=external servers, 1=local only)
Ollama: No (suggestions only) | LM Studio: N/A
Ollama: 15-45 sec (CPU), 3-8 sec (GPU) | LM Studio: N/A
Ollama: $0 after hardware | LM Studio: N/A
Ollama: 15-30 (CLI, GPU setup) | LM Studio: N/A
Ollama: Not required after setup | LM Studio: N/A
Ollama: Requires external plugins/API setup | LM Studio: N/A
Ollama: 4GB (minimum); 8GB recommended | LM Studio: N/A
Ollama: Unlimited (fully free) | LM Studio: N/A
Ollama: $0 (fully open-source) | LM Studio: N/A
Ollama: 100% local processing | LM Studio: N/A
Ollama: 10,000+ GitHub stars, active Discord | LM Studio: N/A
Ollama: 15-50 (GPU-dependent) | LM Studio: N/A
Total Cost of Ownership (12 months, 1M daily tokens)
Ollama: $0 (hardware amortized) | LM Studio: N/A
Inference Latency (7B model, first token)
Ollama: 8GB RAM + 4GB GPU (Llama 7B) | LM Studio: N/A
Ollama: 8-10 (including model download) | LM Studio: N/A
Ollama: 1-5 (limited by local hardware) | LM Studio: N/A
Model Inference Speed (Llama 2 7B on RTX 4090)
Ollama: ~145 tokens/sec | LM Studio: ~148 tokens/sec
Ollama: Yes (native) | LM Studio: Yes (via plugin)
Ollama: Not supported | LM Studio: Supported natively
Ollama: Command-line interface | LM Studio: N/A
Ollama: No (CLI only) | LM Studio: Yes (full desktop app)
Ollama: 3-5 minutes (depends on internet) | LM Studio: N/A
Ollama: Yes (OpenAI-compatible /v1 endpoints) | LM Studio: N/A
Ollama: Not supported | LM Studio: Supported
Ollama: NVIDIA CUDA, AMD ROCm, Metal (Apple) | LM Studio: N/A
Ollama: Medium (CLI setup required) | LM Studio: N/A
Throughput (tokens/second, batch size 32)
Ollama: 4 GB (with offloading) | LM Studio: N/A
Setup Time (from download to first inference)
Ollama: 20,000+ (registry) | LM Studio: N/A
Ollama: Llama 2 70B: 82.3% | LM Studio: N/A
Ollama: 15-30 minutes (download, install, configure) | LM Studio: N/A
Ollama: 200+ open-source models | LM Studio: N/A
Ollama: Only for initial model download; runs offline after | LM Studio: N/A
Multimodal Capabilities (Vision, Image Gen)
Ollama: Limited; vision support emerging in some models | LM Studio: N/A
Ollama: Weekly updates (as of 2026) | LM Studio: N/A
Ollama: Full support with graceful degradation | LM Studio: N/A
Ollama: 200-5000ms (hardware dependent) | LM Studio: N/A
Ollama: 1-10 (single device) | LM Studio: N/A
Ollama: Community-only (none) | LM Studio: N/A
Ollama: Unlimited (local only) | LM Studio: N/A
Ollama: No external data transmission (100% offline) | LM Studio: N/A
Ollama: Model registry only, 0 community features | LM Studio: N/A
Ollama: Not applicable (CLI tool) | LM Studio: N/A
Ollama: None (CLI/API only) | LM Studio: N/A
Ollama: 100+ open-source models (single source) | LM Studio: N/A
Ollama: 5-15 minutes (install + model download) | LM Studio: N/A
Privacy Level (0=cloud-only, 100=fully local)
Ollama: 100 (always local) | LM Studio: N/A
Ollama: Extensive REST API documentation | LM Studio: N/A
Ollama: $0 (fully free) | LM Studio: N/A
Inference Throughput (RTX 4090, Llama 2 13B)
Ollama: ~175 tokens/sec | LM Studio: N/A
Ollama: 4 (CPU, Metal, CUDA, Vulkan) | LM Studio: N/A
Ollama: No (sequential only) | LM Studio: N/A
Ollama: Custom REST endpoints | LM Studio: N/A
Ollama: ~18,000 | LM Studio: ~18,000 stars
Ollama: 70B (Mixtral 8x22B) | LM Studio: N/A
Ollama: 32GB (for optimal performance) | LM Studio: N/A
Ollama: 5-30 seconds (varies by hardware/model) | LM Studio: N/A
Ollama: 100% local—zero external data transmission | LM Studio: N/A
Ollama: Limited—basic vision models available | LM Studio: N/A
Typical Response Quality (Reasoning Tasks)
Ollama: Good for general tasks; weaker on complex reasoning (88% MMLU benchmark score) | LM Studio: N/A
Ollama: 3-5 seconds | LM Studio: 5-8 seconds
Ollama: 50-100 MB | LM Studio: 300-400 MB
Ollama: 80+ models | LM Studio: 1000+ via Hugging Face
Ollama: OpenAI-compatible REST API | LM Studio: OpenAI-compatible + local HTTP API
Ollama: 6+ (GGUF, GGML, etc.) | LM Studio: 8+ (GGUF, GGML, AWQ, etc.)
Ollama: NVIDIA, AMD, Intel (manual setup) | LM Studio: NVIDIA, AMD, Intel, Metal (GUI controls)
Ollama: Not included | LM Studio: Native persistence with export
Ollama: 200,000 (Llama 2 70B via Ollama) | LM Studio: N/A
Ollama: Full (all models run offline) | LM Studio: N/A
Ollama: 110+ (Llama 2 405B) | LM Studio: N/A
Ollama: 100+ open-source models available | LM Studio: N/A
Typical Code Generation Quality (Subjective Rating)
Ollama: 7.1 (best open-source models) | LM Studio: N/A
How to choose between Ollama and LM Studio
Ollama vs LM Studio — Structured Comparison Data
Machine-readable comparison dataset for Ollama vs LM Studio. Contains 109 attribute comparisons, 5 FAQ pairs.
Ollama vs LM Studio — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Ollama vs LM Studio.
Can I use Ollama and LM Studio together?
Yes. Ollama can run as a backend service while LM Studio manages models separately, or you can use LM Studio's API output with Ollama's models. They don't conflict as they use different default ports (Ollama: 11434, LM Studio: 1234 by default). Many users run Ollama for server-side inference and LM Studio for interactive experimentation.
Which tool is better for building chatbot applications?
Ollama is superior for application integration due to its OpenAI-compatible API requiring minimal code changes. LM Studio's API is equally functional but the tool is optimized for interactive use rather than backend deployment. If building production applications, Ollama's lightweight nature and easy containerization make it the better choice.
How much RAM do I need to run each tool?
For a 7B parameter model: Ollama requires 4-6 GB RAM, while LM Studio needs 5-7 GB due to GUI overhead. For larger 13B models, budget 8-10 GB for Ollama and 10-12 GB for LM Studio. Both support quantization (4-bit, 5-bit) to reduce memory usage by 50-75%, allowing 7B models to run on 2-3 GB systems.
LM Studio supports 1000+ models through Hugging Face integration with direct search and download. Ollama's official registry contains 80+ curated models but accepts any GGUF-quantized model from any source. In practice, LM Studio's discovery interface is easier for finding models, while Ollama's flexibility allows running any quantized model manually.
Ollama is native CLI-first and runs perfectly headless—ideal for servers and containers. LM Studio requires the desktop application but can run in background mode. For headless deployments, Ollama is the clear choice; for interactive desktop use, LM Studio is more practical.
How to Choose Between Ollama and LM Studio
User Interface Type: Ollama has Command-line CLI + web chat, while LM Studio has Full desktop GUI (Windows/Mac/Linux).
Setup Complexity: Ollama has 5-10 minutes (single command), while LM Studio has 10-15 minutes (installer + configuration).
Model Library Size: Ollama has 80+ models via Ollama registry, while LM Studio has 1000+ models via Hugging Face integration.
RAM Usage (7B Model): Ollama has 4-6 GB typical, while LM Studio has 5-7 GB typical.
Native API Support: Ollama has OpenAI-compatible REST API built-in, while LM Studio has Local HTTP API with swagger docs.
Chat History Persistence: Ollama has Not built-in (requires third-party tools), while LM Studio has Native conversation management and export.
Multi-GPU Support: Ollama has Supported via environment variables, while LM Studio has Full GPU memory management UI.