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Articles, guides, and insights on local artificial intelligence
Reading paths
Start here
The in-order path for newcomers to local AI: from basic concepts to installation and your first model.
Optimize performance
Pick the right hardware and get the most out of what you already own: CPU vs GPU, Mac, Raspberry Pi, and fast serving.
Data and privacy
What actually goes over the network with local AI: the data, the security, the compliance, and the control that stays in your hands.
Choosing hardware and model
How to pick the right hardware and the model suited to your use: VRAM, quantization, GPU or CPU and response speed.
9 articles
Intel and AMD collaborate on PyTorch/oneDNN: what it means for local AI on CPUs
AMD is merging ZenDNN into oneDNN and Intel is folding in its ACE extensions: one open-source x86 acceleration stack in PyTorch, already the default. What it changes if you run models locally on CPUs.
Mac Studio M5 Ultra: 512GB of unified memory on the desktop
Four dies, 512GB of unified memory at 1.2 TB/s: the Mac Studio M5 Ultra runs 100-560B MoE models locally. The speed math, the cost comparison with the DGX Station, and when the Mac is the right choice.
The 16GB Blackwell cards: 5060 Ti, 5070, 5070 Ti
The 16GB VRAM tier is the center of gravity of local AI in 2026. It's enough memory to run the best open dense models in Q4 formats — the 27B class, the new balance point of the open catalog — without giving up too much context, and a list price that (depending on the month) stays within a personal budget. The Blackwell generation offers three cards exactly in the middle of this tier: the RTX 5060 Ti, 5070 and 5070 Ti. Same generation, same GDDR7, three very different experiences. Here's how they compare: what each one really brings you, and where your budget should go.
Unified memory: from Strix Halo to Mac Studio M5 Ultra — what fits and at what speed
128GB or 512GB of memory shared between CPU and GPU: three real platforms, the bandwidth that determines speed, and the math on what fits and for whom it makes sense.
A Model Without a GPU: When the CPU Is Actually Enough
In 2026, CPU-only inference is no longer a fallback: when it genuinely works, what speed to expect, and which levers to pull to squeeze the most out of your processor.
GPU or CPU: when the CPU is enough
How prefill and decode work, what matters for speed (memory bandwidth), real numbers on a mainstream laptop, the cases where the CPU is enough and where VRAM is needed — with the commands to measure.
Local AI on Mac: the complete Apple Silicon guide
Why Apple Silicon (unified memory, GPU cores) is the simplest machine for local AI: the RAM you need, installing Ollama, recommended models per chip generation, and realistic performance expectations.
Hardware for Local AI: What You Need in 2026
The memory rule (weights + KV cache), real October 2026 prices for GPUs, Apple Silicon and CPU, the 36-month cost math and the choice by budget.
Local AI on Raspberry Pi
From first install to a model that answers on a Pi 5: real 2026 prices, the tok/s = bandwidth ÷ file formula, the electricity bill and the tricks that matter.