LivoPC team · AI-assisted editorial summary
How to plan a PC for local AI in 2026
Size a text inference setup around the model, memory and context, with documented requirements and limits.
Updated on · Games and apps
What to consider first
For local text inference, first choose the application, model, quantization and context size. Only then check RAM, VRAM and backend support. No single configuration fits every AI workload.
The scope is text inference. Training, image generation and cloud services are not sized here. We do not measure tokens per second or guarantee that a model fits in the memory of a GPU in the catalog.
1. Describe the workload
Record the model and version, distribution format, quantization, session count and intended context. Using a local interface does not mean all processing runs on the GPU. Also check the application and model instructions before selecting hardware.
2. Separate application requirements from model memory needs
The LM Studio guidance below applies to the application on Windows x64. It is not a promise of capacity for every model. Ollama documents that larger contexts require more memory; the model file size alone does not represent all memory needed at runtime.
3. Confirm support and room for expansion
Check the combination of operating system, backend and exact GPU model in the runtime's current documentation. Record these outstanding checks in the plan. Then check the power supply, cables, case, slots and memory with the compatibility tools already available in LivoPC.
4. Compare against a reproducible measurement
If you use external benchmarks, record the model, quantization, context, versions and full configuration. Do not compare figures from different tasks as if they were interchangeable. First plan a configuration that can be reviewed; record a part as purchased or installed only when that happens.
Application guidance for Windows x64; local text inference
LM Studio
- CPU
- AVX2 support required on Windows x64
- RAM
- 16 GB or more recommended by the application
- Dedicated GPU
- At least 4 GB of VRAM recommended by the application
These recommendations do not size a setup for a specific model. Model, quantization, context, backend and GPU support need to be checked separately. Training and image generation have different needs.
Source accessed on : LM Studio — system requirements.
My local AI plan
This plan starts without parts: application requirements are not enough to size a setup for your model. Fill in the workload and compare alternatives in the builder.
Explore and plan without an account. Parts are added as planned; checks are in the builder.
Find options for your plan
Compare parts and offers in your country. Set a maximum price in the filters and check unresolved configuration issues before choosing.
Explore and plan without an account. Parts are added as planned; checks are in the builder.
How this guide was prepared
Original guide based on the public documentation below, accessed on each reference date. No performance tests or physical inspections were performed for this text. Check current documentation for your model and revision before acting; examples are not universal buying recommendations.
- LM Studio — system requirements · accessed on
- Ollama — context and memory usage · accessed on
- Ollama — GPU support · accessed on