Personal AI Devices
Hardware reviews for running AI locally: from Mac M-series to NVIDIA GPUs, mini PCs, and edge accelerators.
Each device is described for its AI use: how large a model it can run and what it costs. Scores are editorial ratings based on published specs and prices, not lab tests. Affiliate links help support this site at no extra cost to you.
What hardware do I need to run AI locally?
8GB RAM
3B, 7B models
Basic chatbots, simple text tasks
16GB RAM
7B, 13B models
Good quality chat, code assistance
32GB RAM
13B, 30B models
Capable chat and coding help
64GB+ RAM
70B models
Large quantised models, fully offline
Note: For Apple Silicon (Mac), RAM is shared between CPU and GPU, so most of a Mac's memory is available to the GPU for AI inference. For Windows PCs, VRAM (GPU memory) is the key bottleneck for running LLMs.
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Apple MacBook Pro 16" (M4 Pro)
The gold standard for running local AI models on a laptop
The MacBook Pro with M4 Pro chip is a strong laptop for AI workloads. Its unified memory architecture means the CPU, GPU, and Neural Engine share the same memory pool, so the 48GB configuration can run quantised models such as Llama 3 70B, Mistral, and Gemma locally.
ram
Up to 48GB unified
storage
Up to 4TB SSD
cpu
M4 Pro (14-core)
gpu
M4 Pro (20-core)
Pros
Cons
Apple Mac mini (M6, 16GB, 512GB)
The most affordable Mac for serious local AI
The 2026 Mac mini moves to Apple's M6 chip: a 12-core CPU, a 12-core GPU with a Neural Accelerator in each core, and a Dual 16-core Neural Engine. This configuration pairs 16GB of unified memory (153GB/s) with a 512GB SSD, which is enough to run 7B, 13B parameter models comfortably in Ollama or LM Studio. If you want larger models, Apple also builds it with 24GB or 32GB (170GB/s). Apple lists this configuration at $1,099; the 256GB model starts at $899. It's a five-by-five-inch box that stays very quiet, which makes it a good always-on local AI server.
ram
16GB unified (24GB or 32GB options)
storage
512GB SSD (1TB or 2TB options)
cpu
M6, 12-core (2 super, 4 performance, 6 efficiency)
gpu
M6, 12-core with Neural Accelerators
Pros
Cons
NVIDIA GeForce RTX 4090 24GB
The fastest consumer GPU for AI inference and training
The RTX 4090 is one of the fastest consumer GPUs for AI work. With 24GB of GDDR6X VRAM and 82.6 TFLOPS of FP32 performance, it can run models up to about 30B parameters in 4-bit quantisation (a 70B model needs more memory than one card has), fine-tune smaller models with parameter-efficient methods, and generate Stable Diffusion images in under 2 seconds. Essential for serious AI researchers and developers on Windows or Linux.
vram
24GB GDDR6X
cuda Cores
16,384
tensor Cores
512 (4th gen)
bandwidth
1,008 GB/s
Pros
Cons
NVIDIA Jetson Orin Nano 8GB
Edge AI powerhouse for makers, developers, and robotics
The Jetson Orin Nano is NVIDIA's most accessible edge AI platform. With 8GB of LPDDR5 memory shared between CPU and GPU, it can run 7B parameter models locally, deploy computer vision pipelines, and power robotics projects. It's the go-to board for developers building AI-powered devices, drones, and IoT applications.
ram
8GB LPDDR5
cpu
6-core Arm Cortex-A78AE
gpu
1024-core NVIDIA Ampere
ai Perf
40 TOPS
Pros
Cons
Minisforum UM890 Pro (Ryzen 9 8945HS)
Compact Windows mini PC with serious AI NPU capabilities
The Minisforum UM890 Pro packs AMD's Ryzen 9 8945HS with a 16-TOPS NPU into a palm-sized box. It's one of the best Windows mini PCs for running local AI tools, supporting Ollama, LM Studio, and Windows AI features. With up to 64GB DDR5 RAM and a fast integrated Radeon 780M GPU, it handles 7B, 13B models smoothly.
cpu
AMD Ryzen 9 8945HS
gpu
AMD Radeon 780M (integrated)
npu
AMD XDNA NPU (16 TOPS)
ram
Up to 64GB DDR5
Pros
Cons
Framework Laptop 16 (AMD Radeon RX 7700S)
The upgradeable AI laptop: built to last and evolve
Framework's Laptop 16 is unique: it's the only mainstream laptop with a user-upgradeable discrete GPU. The AMD Radeon RX 7700S expansion bay delivers 8GB of GDDR6 VRAM for running Stable Diffusion, local LLMs, and AI coding tools. When better GPUs arrive, you swap the module: no new laptop needed.
cpu
AMD Ryzen 9 7940HS
gpu
AMD Radeon RX 7700S (8GB GDDR6)
ram
Up to 64GB DDR5
storage
Up to 8TB NVMe
Pros
Cons
ASUS ROG Ally X (AMD Z1 Extreme)
Run AI models on a handheld gaming PC
The ROG Ally X is a handheld Windows gaming PC that doubles as a surprisingly capable local AI device. With 24GB of LPDDR5X RAM shared between CPU and GPU, it can run 7B models via Ollama and even handle light Stable Diffusion workflows. A genuinely portable option for AI experimentation on the go.
cpu
AMD Ryzen Z1 Extreme
gpu
AMD Radeon 780M (integrated)
ram
24GB LPDDR5X shared
storage
1TB NVMe
Pros
Cons
Logitech MX Master 3S
The productivity mouse built for AI power users
The MX Master 3S is a popular productivity mouse. Its customisable buttons, MagSpeed scroll wheel, and multi-device support make it ideal for switching between AI tools, coding environments, and creative applications. The silent click switches reduce fatigue during long AI workflow sessions.
dpi
200 to 8,000 DPI
buttons
7 customisable
connectivity
Bluetooth + USB receiver
battery
Up to 70 days
Pros
Cons
Samsung 990 Pro 2TB NVMe SSD
Store your entire AI model library at lightning speed
Running local AI models requires fast storage: models can be 4 to 70GB each, and loading speed directly impacts how quickly inference begins. The Samsung 990 Pro delivers 7,450 MB/s sequential read speeds, so model files load quickly; real load times depend on the app and system. Essential for anyone building a local AI model library.
capacity
2TB
interface
PCIe 4.0 NVMe M.2
read Speed
7,450 MB/s
write Speed
6,900 MB/s
Pros
Cons
Crucial 64GB DDR5-5600 RAM Kit
Max out your RAM to run larger AI models locally
RAM is the single biggest bottleneck for running large language models locally. At 4-bit quantisation a model needs roughly 0.5 to 0.6GB of memory per billion parameters, plus room for context. This 64GB DDR5 kit opens the door to running 30B, 70B models on compatible systems, and dramatically speeds up inference on smaller models by eliminating memory swapping.
capacity
64GB (2x32GB)
type
DDR5-5600
latency
CL46
voltage
1.1V
Pros
Cons
Razer Blade 18 (RTX 4090, 32GB)
Desktop-class AI performance in a premium laptop
The Razer Blade 18 is one of the most powerful AI laptops in this list. Its full-power RTX 4090 with 16GB GDDR6 VRAM delivers near-desktop performance for AI workloads: running Stable Diffusion XL, fine-tuning 7B models, and handling complex ComfyUI workflows without compromise. The 18" QHD+ 240Hz display makes it a complete workstation replacement.
cpu
Intel Core i9-14900HX
gpu
NVIDIA RTX 4090 (16GB GDDR6)
ram
32GB DDR5
storage
2TB NVMe
Pros
Cons
Google Coral USB Accelerator
Add edge AI inference to any USB device for $60
The Google Coral USB Accelerator is the most affordable way to add dedicated AI inference hardware to any computer. Its Edge TPU co-processor performs 4 TOPS of ML inference while consuming just 2W of power. Perfect for running TensorFlow Lite models, image classification, and object detection on Raspberry Pi, laptops, or any USB host.
chip
Google Edge TPU
performance
4 TOPS (int8)
interface
USB 3.0 (USB 2.0 compatible)
power
~2W
Pros
Cons
NVIDIA GeForce RTX 4080 SUPER 16GB
The sweet spot between price and AI performance
The RTX 4080 SUPER hits the ideal balance for serious AI work without the RTX 4090's price premium. Its 16GB of GDDR6X VRAM handles 13B models in 8-bit and models up to about 20B in 4-bit quantisation, while its 52 TFLOPS of FP32 performance makes Stable Diffusion XL generation feel instant. It is a good fit for many AI developers.
vram
16GB GDDR6X
cuda Cores
10,240
tensor Cores
320 (4th gen)
bandwidth
736 GB/s
Pros
Cons
AMD Radeon RX 7900 XTX 24GB
24GB VRAM for the price of an RTX 4080: AMD's AI challenger
The RX 7900 XTX offers 24GB of GDDR6 VRAM at a price point well below the RTX 4090, making it compelling for AI workloads that need maximum VRAM. With ROCm support, it now runs PyTorch, Ollama, and llama.cpp natively on Linux. The best choice for budget-conscious AI developers who need 24GB VRAM.
vram
24GB GDDR6
compute Units
96
bandwidth
960 GB/s
tdp
355W
Pros
Cons
Apple Mac Studio (M4 Max, 128GB)
A previous-generation Mac Studio for local AI
The Mac Studio with M4 Max is the previous-generation Mac Studio; Apple's 2026 models use M5 Max and M5 Ultra. With up to 128GB of unified memory, it can run 70B parameter models with 8-bit or 4-bit quantisation. Full 16-bit precision for a 70B model needs about 140GB, more than this machine has.
ram
Up to 128GB unified
cpu
M4 Max (16-core)
gpu
M4 Max (40-core)
neural Engine
32-core, 38 TOPS
Pros
Cons
Raspberry Pi 5 (8GB)
The most affordable way to experiment with local AI
The Raspberry Pi 5 is an inexpensive entry point into local AI. With 8GB of LPDDR4X RAM and a significantly faster CPU than its predecessors, it can run small language models (1B, 3B parameters) via llama.cpp and Ollama. Perfect for learning, prototyping, and building AI-powered IoT projects on a $80 budget.
cpu
Broadcom BCM2712 (4-core Arm Cortex-A76)
ram
8GB LPDDR4X
storage
microSD / NVMe via HAT
connectivity
2x USB 3.0, 2x USB 2.0, Gigabit Ethernet, WiFi 5
Pros
Cons
Intel Core Ultra 9 285K (Arrow Lake)
Intel's first desktop CPU with a dedicated AI NPU
The Intel Core Ultra 9 285K is Intel's flagship desktop AI CPU, featuring a dedicated NPU capable of 13 TOPS of AI inference. While it requires a discrete GPU for serious AI work, its NPU accelerates AI tasks in Windows 11, Microsoft Copilot, and compatible AI applications. The best Intel CPU for building a Windows AI PC from scratch.
cores
24 cores (8P + 16E)
threads
24
npu
Intel AI Boost NPU (13 TOPS)
tdp
125W (up to 250W PL2)
Pros
Cons
Beelink SEI12 Pro (Intel Core i7-12650H)
The budget mini PC that punches above its weight for AI
The Beelink SEI12 Pro is a budget mini PC for local AI experimentation. At under $300, it delivers an Intel Core i7-12650H with 32GB DDR4 RAM: enough to run 7B models smoothly via Ollama. Its compact size and silent operation make it ideal as a dedicated home AI server that runs 24/7 without the noise or power draw of a desktop.
cpu
Intel Core i7-12650H (10-core)
gpu
Intel Iris Xe (integrated)
ram
32GB DDR4
storage
500GB NVMe
Pros
Cons
NVIDIA Jetson AGX Orin 64GB
Professional edge AI platform for serious deployments
The Jetson AGX Orin is NVIDIA's most powerful edge AI platform, delivering 275 TOPS of AI performance in a compact module. With 64GB of unified LPDDR5 memory, it can run 13B models locally and deploy production AI pipelines for robotics, autonomous vehicles, and industrial applications.
ram
64GB LPDDR5
cpu
12-core Arm Cortex-A78AE
gpu
2048-core NVIDIA Ampere
ai Perf
275 TOPS
Pros
Cons
Anker USB-C Hub 16-in-1 (USB4)
Connect all your AI peripherals to a single USB-C port
Running a local AI workstation often means juggling multiple peripherals: external GPUs, NVMe drives, monitors, and keyboards. The Anker 16-in-1 USB4 hub turns a single Thunderbolt/USB4 port into a full workstation dock, with 100W passthrough charging, dual 4K display support, and 10Gbps data transfer for fast model loading from external drives.
ports
16 ports total
interface
USB4 / Thunderbolt 4
video Out
Dual 4K@60Hz
data Speed
10Gbps USB-A/C
Pros
Cons
Got the hardware? Now find the right software.
Browse our full directory of AI tools: many of which run locally on the hardware above. From Ollama to LM Studio, we cover every major local AI tool.