Use a simple desktop engine for one person. Load-test Ollama or llama.cpp for a small team. Consider vLLM or SGLang when many active users and supported server GPUs make throughput the priority.
Use the explanations below when you want to know why each step matters.
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-throughput serving.
Simple setup and local chat
Load-test queues, memory, and context
Use supported GPUs and monitoring
Capacity testing and operations are required
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 artifact, and job.
Best first capacity test
More memory and slower individual replies
Load-test before team use
What real users report
Ollama GitHub issues show that parallel requests can raise total throughput 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.
A successful single chat proves compatibility. It does not prove team capacity.
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, artifact, 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 artifact 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.
Official facts and real user evidence
Official documentation supports product and model facts. Community discussions show real setups, failures, and questions. A community result is supporting evidence, not a promise that another computer will perform the same way.
Find a model your computer can run.
MamiLens checks your hardware and shows a careful starting point.
Run the free compatibility check →This guide is educational. Model software, licenses, and hardware support can change. Check official sources before an important deployment.