🧠 Running LLMs on Your PC: 2025 Update

Date Created: 2024-09-04
Updated: 2025-04-22
By: 16BitMiker
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As artificial intelligence continues its rapid evolution, the landscape for running Large Language Models (LLMs) on personal machines has shifted significantly over the past year. With new GPU architectures, more efficient quantization techniques, and broader support across operating systems, it’s now more feasible than ever to bring powerful AI models right to your desktop.

In this 2025 update, we revisit the original 2024 guide with the latest hardware and software developments—including updates from NVIDIA, AMD, Apple, and the open-source community. Whether you're a developer, researcher, or AI tinkerer, this guide will help you navigate the current requirements and possibilities of running LLMs locally.

📋 Understanding Model Sizes and Requirements (2025)

LLMs vary widely in size and corresponding hardware needs. Here's a refreshed overview:

🔄 4-bit quantization and new attention mechanisms have made it more practical to run 13B and even 30B models on high-end consumer hardware.

🖥️ Key Hardware Components in 2025

🎮 1. GPUs: The Backbone of LLM Inference

GPUs remain the most important component for running LLMs efficiently.

🔧 VRAM Requirements (2025):

• 7B models: 8–10 GB VRAM (RTX 3060, 4060 Ti, RX 7600 XT)

• 13B models: 16–20 GB VRAM (RTX 3090, 4080, RX 7900 XTX)

• 30B models: 24–48 GB VRAM (RTX 6000 Ada, AMD MI300A)

• 70B models: 80+ GB VRAM or multi-GPU (NVIDIA H100, AMD MI300X)

⚙️ Notable GPU Updates:

• NVIDIA RTX 5000 Series expected Q4 2025 with increased VRAM and lower TDP.

• AMD MI300X offers 192 GB of shared memory, now accessible via ROCm 6.x for LLMs.

• Apple M3 Ultra supports efficient 34B model inference with up to 192 GB unified memory.

📦 Tip: Check for support in your LLM backend (e.g. llama.cpp, vLLM, Hugging Face Optimum).

🔢 2. CPU

While less critical for inference, the CPU matters for data handling, I/O, and preprocessing.

• Best for LLMs: AMD Threadripper 7000 Pro, Intel Xeon W-3400

• Consumer-grade: AMD Ryzen 9 7950X, Intel i9-14900K

• Apple Silicon: M2/M3 Pro/Max/Ultra includes neural engines for offloading certain ML workloads

👥 Multi-threaded CPUs can help with tasks like tokenization, prompt formatting, or preparing datasets for fine-tuning.

🧠 3. RAM

RAM usage varies depending on model size and precision (FP16 vs 4-bit quantized).

• 7B models: 16 GB (minimum)

• 13B models: 32 GB recommended

• 30B models: 64–128 GB

• 70B models: 128–256 GB (or use CPU/GPU offloading)

📌 ECC RAM is preferred for large model stability, especially on workstation builds.

💾 4. Storage

Model files and tokenizers can be large and need fast access speeds.

• Recommended: PCIe Gen 4/5 NVMe SSDs (e.g., Samsung 990 PRO, WD SN850X)

• Sizes:

  • LLaMA 3 8B (GGUF): ~5.5 GB (4-bit)

  • LLaMA 3 70B (FP16): ~140 GB

• Tip: Use separate drives for OS, software, and models to reduce I/O contention.

🖥️ 5. Operating System

• Linux (Ubuntu 22.04+, Arch, Debian): Best performance, full ROCm and CUDA support.

• Windows 11: Improved support for WSL2 + CUDA, but less stable for ROCm.

• macOS (M2/M3): Great for 7B–34B models using llama.cpp or MLX.

✅ Most open-source projects support Linux first; macOS support is growing rapidly via Homebrew and Apple-optimized backends.

📦 LLM Tooling and Frameworks in 2025

The ecosystem to run LLMs locally has matured significantly:

🔄 Quantization tools like AutoGPTQ, awq, and ggml are now standard for reducing model weights without large accuracy loss.

🚀 New in 2025: Specialized Hardware and Architectures

🍏 Apple Silicon (M3 Pro/Max/Ultra)

• Supports 34B models with GGUF format via llama.cpp

• Unified memory (up to 192 GB) helps with token throughput

• M3 Max benchmarks show 40–45 tokens/sec on quantized 13B models

✅ Excellent for developers on the go or those in the Apple ecosystem.

🔥 AMD's 2025 AI Push

• MI300X: 192 GB HBM3, now supported by open-source inference frameworks

• Ryzen 8000G with Ryzen AI: New NPU cores for on-device inference

• ROCm 6.x: Improved support for PyTorch and Hugging Face Transformers

🧪 ROCm now integrates seamlessly with transformers and text-generation-webui via patched backends.

📋 Choosing the Right Setup in 2025

Here’s a simplified decision matrix:

✅ Optimization Tips for 2025

• 🔄 Quantization: Use 4-bit GGUF or GPTQ to reduce memory and storage.

• 🚥 FlashAttention 2: Boosts attention speed and reduces VRAM usage.

• 🔃 Offloading: Combine CPU + GPU memory for larger models.

• 📶 Use vLLM or llama.cpp: These engines are optimized for throughput and latency.

• 🧪 Benchmark before choosing a model: Token speed varies based on backend and quantization.

🏁 Conclusion

Running LLMs locally in 2025 has never been more practical. With advances in GPU technology, quantization techniques, and software tooling, even 30B+ parameter models are within reach for enthusiasts and professionals alike. Whether you're using a Linux workstation with a 3090 or an M3 MacBook Pro, there's a path forward for efficient and private AI inference at home.

🔍 As always, match your hardware to your use case—and stay tuned. With LLaMA 3, Gemma, and other open-source giants pushing performance and openness, the local LLM ecosystem is only getting stronger.

📚 Read More

• Hugging Face Transformers Documentation

• llama.cpp GitHub

• text-generation-webui

• AMD ROCm Documentation

• vLLM Project

• Apple MLX Framework