What Are LLMs and How Do They Perform on Consumer Hardware?

Running large language models (LLMs) locally have become a realistic option for developers who want privacy, predictable costs, and full control over their AI workflows. Tools like Ollama, LM Studio, and mlx‑based models on Apple Silicon make it possible to run capable models directly on a laptop or compact desktop machine.

This article provides an overview of how local LLMs work, what the key concepts mean, and what you can expect from consumer hardware such as the Mac mini M4.

What a Large Language Model Actually Is

A large language model is a statistical system trained on large text datasets. It predicts the next token in a sequence, where a token is a small unit of text (roughly 3–4 characters on average). Everything an LLM does—writing code, explaining errors, generating documentation—comes from this next‑token prediction process.

At runtime, the model does not “look up” answers. It performs a sequence of matrix multiplications using its internal parameters (weights). These weights encode patterns learned during training.

Model Size: What “7B”, “13B”, or “70B” Means

Model sizes are usually expressed in billions of parameters:

3B–7B	Fast chat, simple coding tasks, lightweight agents	Runs on almost any modern machine
13B	More coherent reasoning, better coding support	Needs more RAM and bandwidth
30B–70B	High‑quality reasoning, strong coding performance	Requires high memory bandwidth and large RAM/VRAM

A parameter is a single floating‑point value. More parameters generally mean better reasoning and more context understanding, but also higher memory usage and slower inference.

How this works at runtime

When you send a prompt to a local model:

1. The model loads its weights into RAM (or VRAM).
2. The prompt is converted into tokens.
3. The model processes these tokens through its layers.
4. It predicts the next token.
5. The new token is appended to the input.
6. Steps 3–5 repeat until the output is complete.

The speed of this loop depends on:

• memory bandwidth
• CPU/GPU architecture
• quantization level
• model size
• context length

Apple Silicon performs well here because of its unified memory architecture and high bandwidth.

The Mac mini M4 is a strong machine for local AI development. Even the base model offers:

• high memory bandwidth
• efficient matrix multiplication hardware
• unified memory (shared between CPU and GPU)
• excellent performance per watt

Practical Model Sizes on a Mac mini M4:

Llama 3.1 8B	~4–5 GB	Smooth	Good for chat and basic coding
Llama 3.1 12B	~7–8 GB	Smooth	Better reasoning, solid coding
Qwen 2.5 Coder 7B	~4–5 GB	Smooth	Strong coding performance
Qwen 2.5 Coder 14B	~8–10 GB	Good	Slower but usable for coding tasks
Llama 3.1 70B	35–40 GB	Not practical	Too large for local RAM limits

Beyond Apple Silicon systems, several other current hardware options handle local LLMs effectively. AMD’s recent desktop processors, such as the Ryzen 7000 and 9000 series, offer strong CPU‑side inference performance thanks to high core counts and substantial memory bandwidth, making them suitable for running 7B–13B models in quantized formats.

For users who prefer GPU acceleration, mid‑range NVIDIA cards like the RTX 4060, 4070, or 4070 Ti provide stable throughput for models in the 7B–30B range, depending on available VRAM. An RTX 4060 with 8 GB VRAM is well suited for 7B models in FP16 or larger models in 4‑bit quantization, while a 4070 with 12 GB VRAM can handle 13B models at higher precision with comfortable performance. These configurations give developers a broad set of options for running local models, depending on whether they prioritize CPU inference, GPU acceleration, or a balance of both.

Local Models vs. Cloud Models: What you should know

Local models offer:

• Privacy: your code never leaves your machine
• Predictable cost: no API billing
• Offline capability
• Full control over model versions and behavior

Cloud models still lead in:

• complex reasoning
• long‑context tasks
• multi‑modal workflows
• raw performance

For many developers, a hybrid workflow makes sense: local models for everyday coding and cloud models for complex tasks.

Summary

Local LLMs have reached a point where they are practical for everyday development tasks. With tools like Ollama and Continue and modern hardware such as the Mac mini, you can run capable models without relying on cloud services.

Key points:

• LLMs predict tokens using learned parameters.
• Model size affects quality and hardware requirements.
• A Mac mini M4 handles 7B–12B models very well.
• Local models are ideal for private, cost‑controlled development workflows.