Which PC for a local LLM in 2026? Complete Guide
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Running a Large Language Model (LLM) locally has become accessible in 2026. Whether you are a lawyer, doctor, accountant, or developer, a sufficiently powerful PC can host a high-performing AI model on your own infrastructure — without the cloud, without subscriptions, and without your data leaving your premises.
This guide specifically 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, it's essential to understand the three critical parameters for running a local LLM:
1. VRAM (Graphics Card Video Memory)
This is the number one limiting factor. An LLM is loaded into GPU memory for fast inference. The larger the model, the more VRAM is needed:
| Model Size | Minimum VRAM | Recommended VRAM | Example Models |
|---|---|---|---|
| 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, Quantized Llama 3.3 70B |
| 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. CPU and System RAM
The CPU takes over when model layers don't fit into VRAM (offloading). The more fast system RAM you have, the more layers you can offload to the CPU without significantly impacting speed. Generally: 32 GB of DDR5 RAM minimum for serious use, 64 GB for models 30B+.
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) — initial loading time directly depends on it.
What PC for 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 writes reports, an accountant who searches for information in a document database: a 7B to 14B model in Q4_K_M is largely sufficient.
| 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 | ~€900-1100 | ~€1200-1600 |
| 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, Code (14-32B Models)
For RAG (Retrieval Augmented Generation) on a corporate document base, detailed contract analysis, or development assistance, you need more power.
| Component | Recommended | Optimal |
|---|---|---|
| GPU | RTX 5060 Ti 16 GB GDDR7 | RTX 5070 12 GB GDDR7 |
| CPU | Ryzen 5 9600X | Ryzen 7 7800X3D / 9800X3D |
| System RAM | 32 GB DDR5 5600 MHz | 64 GB DDR5 |
| SSD | 1 TB NVMe Gen 4 | 2 TB NVMe Gen 5 |
| Indicative Budget | ~€1600-2200 | ~€2200-3000 |
| 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 law firm with 10 people, a medical team, a company that wants to deploy an internal AI assistant for all its employees: you need to move to a dedicated server configuration.
| Component | AI Server Configuration |
|---|---|
| GPU | RTX 5070 Ti 16 GB or RTX 5080 16 GB |
| CPU | Ryzen 7 9800X3D or Ryzen 9 9950X |
| System RAM | 64-128 GB DDR5 ECC |
| SSD | 2-4 TB NVMe Gen 5 |
| Indicative Budget | €3000-6000+ |
| Compatible Models | Llama 3.3 70B Q4, Qwen2.5 72B Q4, Mixtral 8x7B |
What Software to Run a Local LLM?
Hardware is not enough — you also need software to load and serve models. The most commonly used solutions in 2026:
Ollama — The Simplest Solution
Ollama is the benchmark 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 ChatGPT-like Local Interface
Open WebUI (formerly Ollama WebUI) offers an intuitive web interface similar to ChatGPT, deployable locally via Docker. Conversation management, system prompts, documents — it has it all.
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, integrated 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 According to Your Profession?
| Profession / Use | Recommended Model | VRAM Needed | Strengths |
|---|---|---|---|
| Lawyer — contract analysis | Qwen3 14B Q4_K_M | 10 GB | Legal reasoning, long context windows |
| Doctor — reports | Mistral Small 3.1 / Llama 3.1 8B | 6-8 GB | Fluent writing, fast inference |
| Accountant — financial analysis | Qwen2.5 14B Q4 / DeepSeek-R1 14B | 10-12 GB | Calculations, data structuring, tables |
| Developer — code assistance | Qwen2.5-Coder 14B / DeepSeek-Coder | 10 GB | Code completions, debugging, refactoring |
| General / versatile use | 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 Confidentiality | ❌ 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 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 LLM
Radiance Systems designs local AI workstations specifically configured to run LLMs locally, delivered ready-to-use with Ollama and Open WebUI pre-installed upon request.
- ✅ Configurations optimized for LLM inference (VRAM, RAM, storage)
- ✅ AM5 DDR5 platform for the best memory performance
- ✅ Latest generation NVIDIA RTX GPUs (CUDA, optimized for llama.cpp)
- ✅ 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 — 50-day satisfaction guarantee
Frequently Asked Questions — Local LLM
Can a local LLM run 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 gain access to 32B Q4 models which offer significantly higher 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 16 GB of VRAM, you get GPT-4 level AI running entirely on your machine.
Do I need an internet connection to use a local LLM?
No. Once the model is downloaded, it runs entirely offline. This is one of the great advantages for sensitive environments or offices without constant connectivity.
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 station with the system of your choice.
How much does a local AI station cost compared to a cloud subscription?
A local AI station costs €1200 to €3000 depending on the configuration. A ChatGPT Pro subscription costs €20/month/user — or €240/year. For a firm of 5 people, the local AI station pays for itself in less than 24 months, with zero GDPR risk.
