Which PC for a local LLM in 2026? Complete Guide
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Running a local LLM (Large Language Model) has become accessible in 2026. Whether you are a lawyer, doctor, accountant, or developer, a powerful enough PC can host a high-performance AI model on your own infrastructure — no cloud, no subscription, and no need for your data to leave your premises.
This guide answers the question "What PC for a local LLM?" with concrete recommendations, tested configurations, and a comparison of models based on your needs.
What determines the performance of a local LLM
Before choosing your configuration (our local AI PC comparison details the full range), you need to understand the three critical parameters for running an LLM locally:
1. VRAM (graphics card video memory)
This is the number one limiting factor. An LLM is loaded into GPU memory to be inferred quickly. The larger the model, the more VRAM is required:
| Model size | Minimum VRAM | Recommended VRAM | Model examples |
|---|---|---|---|
| 7B parameters (Q4) | 4 GB | 8 GB | Mistral 7B, Llama 3.1 8B, Qwen2.5 7B |
| 13-14B parameters (Q4) | 8 GB | 12 GB | Llama 3.1 14B, Qwen3 14B, DeepSeek-R1 14B |
| 14-32B parameters (Q4) | 12 GB | 16 GB | Qwen3 32B |
| 70B parameters (Q4) | 40 GB | 48 GB+ | Llama 3.3 70B, Qwen2.5 72B |
| 70B+ (full precision) | 80 GB+ | Multi-GPU | Dedicated AI servers |
2. The CPU and system RAM
The CPU takes over when the model layers do not fit in VRAM (offloading). The more fast system RAM you have, the more layers you can offload to the CPU without significantly penalizing speed. As a general rule: 32 GB of DDR5 RAM is recommended for serious use, 64 GB for 30B+ models.
3. Storage
A 14B model in Q4 weighs about 8-9 GB. A 32B model weighs ~18 GB. Plan for a fast NVMe SSD (Gen 4 minimum) — the initial loading time depends directly on it.
What PC for a local LLM? Our recommended configurations by use case
🟢 Light use — Summaries, writing, Q&A on documents (7-14B models)
A lawyer who wants to summarize contracts, a doctor who is writing reports, an accountant who is looking for information in a document database: a 7B to 14B model in Q4_K_M is more than enough.
| Component | Minimum | Recommended |
|---|---|---|
| GPU | RTX 4060 8 GB | RTX 5060 8 GB GDDR7 |
| CPU | Ryzen 5 5600 | Ryzen 5 7500F / 9600X |
| System RAM | 16 GB DDR4 | 32 GB DDR5 |
| SSD | 500 GB NVMe Gen 3 | 1 TB NVMe Gen 4+ |
| Indicative budget | Custom-built | From €1,244 |
| Compatible models | Mistral 7B, Llama 3.1 8B, Qwen2.5 7B, Gemma 2 9B | |
| Inference speed | 30-60 tokens/s (comfortable for daily use) | |
🟡 Intermediate use — RAG, document analysis, coding (14-32B models)
For RAG (Retrieval Augmented Generation) on a corporate document database, detailed contract analysis, or development assistance, you need to step up.
| Component | Recommended | Optimal |
|---|---|---|
| GPU | RTX 5060 Ti 16 GB GDDR7 | AMD Radeon AI PRO R9700 32 GB (no CUDA, for text) |
| CPU | Ryzen 5 7500F | Ryzen 7 7800X3D / 9800X3D |
| System RAM | 16 GB DDR5, expandable in the configurator | 64 GB DDR5 |
| SSD | 1 TB NVMe Gen 4 | 2 TB NVMe Gen 5 |
| Indicative budget | From €2,068 | From €4,162 |
| Compatible models | Qwen3 14B/32B, DeepSeek-R1 14B, Llama 3.3 70B Q4 (partial) | |
| Inference speed | 20-50 tokens/s on 14B · 10-25 tokens/s on 32B | |
🔴 Intensive use — Multi-user AI server, fine-tuning (70B+ models)
A 10-person law firm, a medical team, a company that wants to deploy an internal AI assistant for all its employees: you need to switch to a dedicated server configuration.
| Component | AI server configuration |
|---|---|
| GPU | 2x RTX 5090 (64 GB) or RTX PRO 6000 (96 GB) |
| CPU | Ryzen 9 9950X3D or Ryzen 9 9900X |
| System RAM | 64 to 128 GB DDR5 |
| SSD | 2-4 TB NVMe Gen 5 |
| Indicative budget | From €20,954 |
| Compatible models | Llama 3.3 70B Q4, Qwen2.5 72B Q4, Mixtral 8x7B |
What software to run an LLM locally?
Hardware is not enough — you also need software to load and serve the models. The most used solutions in 2026:
Ollama — The simplest solution
Ollama is the standard for getting started. One command is enough to download and launch a model: ollama run qwen3:14b. It exposes an OpenAI-compatible REST API, usable from any application.
Open WebUI — The local ChatGPT-like interface
Open WebUI (formerly Ollama WebUI) offers an intuitive web interface similar to ChatGPT, deployable locally via Docker. Conversation management, system prompts, documents — everything is there.
LM Studio — For non-developers
LM Studio is the most accessible option for non-technical professionals. Graphical interface, one-click model downloads from Hugging Face, built-in local server.
llama.cpp — For maximum performance
llama.cpp is the most optimized inference engine. Used as a backend by Ollama and LM Studio, it can be used directly to extract the latest performance from your hardware.
Which LLM models to recommend based on your profession?
| Profession / Use | Recommended model | Required VRAM | Strengths |
|---|---|---|---|
| Lawyer, contract analysis | Qwen3 14B Q4_K_M | 10 GB | Legal reasoning, long context windows |
| Doctor, medical reports | Mistral Small 3.1 / Llama 3.1 8B | 6-8 GB | Fluid writing, fast inference |
| Accountant, financial analysis | Qwen2.5 14B Q4 / DeepSeek-R1 14B | 10-12 GB | Calculations, data structuring, tables |
| Developer, coding assistance | Qwen2.5-Coder 14B / DeepSeek-Coder | 10 GB | Code completions, debugging, refactoring |
| General use / versatile | Qwen3 32B Q4_K_M | 18-20 GB | Best quality/size balance in 2026 |
| Multi-user server | Llama 3.3 70B Q4 | 40 GB+ | Maximum quality, concurrent use |
Local LLM vs cloud: why regulated professionals choose local
| Criterion | Cloud LLM (ChatGPT, Mistral AI…) | Local LLM (Radiance Systems) |
|---|---|---|
| Data privacy | ❌ Data sent to third-party servers | ✅ Data on your own machine |
| GDPR compliance | ⚠️ Depends on the provider | ✅ Full compliance |
| Monthly cost | ❌ €20-100/month/user | ✅ Zero recurring cost |
| Availability | ⚠️ Depends on the internet connection | ✅ Works offline |
| Model customization | ❌ Limited | ✅ Fine-tuning possible |
| Sensitive data (medical, legal…) | ❌ Real legal risk | ✅ Only compliant option |
Radiance Systems PCs for local LLMs
Radiance Systems designs local AI workstations specially configured to run LLMs locally, delivered ready to use with Ollama and Open WebUI pre-installed upon request. You can compare all these configurations on the page of our local AI workstation and server range.
- ✅ Configurations optimized for LLM inference (VRAM, RAM, storage)
- ✅ AM5 DDR5 platform for the best memory performance
- ✅ Latest generation NVIDIA RTX GPU (CUDA, optimized for llama.cpp), or AMD Radeon AI PRO R9700 32 GB for text
- ✅ Windows 11 Pro or Linux according to your preference
- ✅ On-site installation possible throughout the EU
- ✅ Dedicated technical support before and after purchase
- ✅ 2-year warranty
Frequently asked questions — Local LLMs
Can you run an LLM locally without a dedicated graphics card?
Yes, llama.cpp supports CPU inference. A 7B model in Q4 runs on any modern PC but at 3-8 tokens/s — too slow for daily use. A dedicated GPU is essential for a smooth experience (30+ tokens/s).
What is the difference between 8 GB and 16 GB of VRAM for an LLM?
With 8 GB, you can run models up to 13B in Q4 — sufficient for many uses. With 16 GB (like the RTX 5060 Ti 16 GB), you get access to 14 billion parameter models in 8 bits that offer significantly superior quality, close to GPT-4.
Is a local LLM as powerful as ChatGPT?
In 2026, the best open-source models (Qwen3 32B, Llama 3.3 70B) rival GPT-4o on most professional tasks. On a GPU with 32 GB of VRAM, you get a GPT-4 level AI running entirely on your machine.
Do you need an internet connection to use a local LLM?
No. Once the model is downloaded, it works entirely offline. This is one of the great advantages for sensitive environments or offices without a constant connection.
What operating system for a local LLM?
Linux (Ubuntu) offers the best performance with llama.cpp and Ollama. Windows 11 works very well with LM Studio and Ollama for non-developers. Radiance Systems can deliver your workstation with the system of your choice.
How much does a local AI workstation cost compared to a cloud subscription?
A local AI workstation is a one-time purchase, whose price depends on the configuration. A ChatGPT Plus subscription costs €23 incl. VAT per month per user, or €276 per year. For a firm of 5 people, this represents €1,380 every year. Our local AI server guide contains a calculator to compare this cost to the price of a machine. And since your data stays with you, local AI facilitates GDPR compliance.
