Ollama, llama.cpp, LM Studio, vLLM, or SGLang: choose by users and workload

The direct answer

For one beginner, choose LM Studio. For one developer or an app that needs a local API, choose Ollama. Choose llama.cpp when you need direct GGUF control, unusual hardware support, or detailed performance settings.

For a small team, Ollama can be a good first pilot if the chosen model fits and simultaneous users are tested. For many active users on supported server GPUs, vLLM or SGLang may fit better because they focus on high-work completed per second serving.

Why active users change memory

Ollama's official FAQ explains that parallel requests increase required memory because each request needs context space. Four people using 8K context do not cost the same as one person using 8K context.

An engine recommendation must therefore use active users, context length, hardware memory, operating system, model model file, and job.

What real users report

Ollama GitHub issues show that parallel requests can raise total work completed per second while making each individual reply slower. Maintainers also explain that Ollama normally shares one model's weights and creates separate context buffers rather than loading duplicate copies.

The rule is simple: ten users will not receive ten fast replies just because one reply worked. Test with the expected number of simultaneous requests.

When each engine fits

• LM Studio: one person who wants a visual model browser and chat app.
• Ollama: a developer, automation, or small pilot that needs a simple local API.
• llama.cpp: direct GGUF use, broad hardware backends, and detailed offload or cache controls.
• MLX LM: Apple Silicon development and Apple-focused workflows.
• vLLM: supported server GPUs, batching, and OpenAI-compatible serving.
• SGLang: advanced serving, structured generation, and agent workloads.

Check exact model support first

An engine can be excellent and still be wrong for one model. Confirm the exact architecture, model file, quantization, chat template, and input type before ranking ease of use.

A text GGUF path does not prove vision, audio, tools, or every template. Exact model file evidence should outrank a general compatible label.

Run a small-team load test

• Choose one exact model and context setting.
• Warm the model before measuring.
• Record one-request delay and total time.
• Repeat with two, five, and ten requests.
• Watch VRAM, RAM, GPU use, queue time, errors, and temperature.
• Set a maximum wait time and error rate first.
• Reduce context, model size, or active users when the system fails.

Add the missing production controls

Put team access behind authentication, encrypted transport, request limits, logs, and a private network. Do not expose an unauthenticated model port directly to the internet.

Production also needs health checks, version control, monitoring, and recovery. The engine is one part of the system, not the full product.

Count requests, not accounts

A team of ten people may send one request at a time or ten requests together. Those are different workloads. Simultaneous requests can need more memory, share computing time and create a queue.

Measure one user first. Then test two and more requests at once with normal prompt lengths. Record the longest wait, failed requests and memory peak. Repeat for long enough to catch heat and queue problems.

When do I need a server engine?

When measured traffic and service needs justify it. Start with a small pilot, then choose software that supports the queue, monitoring and access controls you need.