Personal AI Devices
Hardware reviews for running AI locally — from Mac M-series to NVIDIA GPUs, mini PCs, and edge accelerators.
Every device is reviewed for its AI capabilities: how large a model it can run, how fast, and whether it's worth the price. 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
Near-GPT-3.5 quality locally
64GB+ RAM
70B models
Near-GPT-4 quality, fully offline
Note: For Apple Silicon (Mac), RAM is shared between CPU and GPU — 16GB Mac is roughly equivalent to a 12GB discrete GPU for AI inference. For Windows PCs, VRAM (GPU memory) is the key bottleneck for running LLMs.
Affiliate disclosure: This page contains Amazon affiliate links (tag: seperts-20). If you purchase through these links, GuideTopics earns a small commission at no extra cost to you. Our reviews are independent — we only recommend hardware we'd genuinely use for AI work.
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 the most capable laptop for AI workloads available today. Its unified memory architecture means the CPU, GPU, and Neural Engine share the same memory pool — allowing you to run large language models like Llama 3 70B, Mistral, and Gemma locally with no GPU bottleneck.
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 (M4, 16GB)
The most affordable entry point for serious local AI
The M4 Mac Mini is arguably the best value AI machine you can buy in 2025. At $599 for the base model, it delivers enough power to run 7B–13B parameter models smoothly.
ram
16GB or 24GB unified
storage
256GB–2TB SSD
cpu
M4 (10-core)
gpu
M4 (10-core)
Pros
Cons
NVIDIA GeForce RTX 4090 24GB
The fastest consumer GPU for AI inference and training
The RTX 4090 remains the undisputed king of consumer AI acceleration. With 24GB of GDDR6X VRAM and 82.6 TFLOPS of FP32 performance, it can run 70B models in 4-bit quantisation.
vram
24GB GDDR6X
cuda Cores
16,384
tensor Cores
512 (4th gen)
bandwidth
1,008 GB/s
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 at full precision and 70B models in 4-bit quantisation.
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.
vram
24GB GDDR6
compute Units
96
bandwidth
960 GB/s
tdp
355W
Pros
Cons
Apple Mac Studio (M4 Max, 128GB)
The most powerful desktop AI machine Apple makes
The Mac Studio with M4 Max is Apple's most powerful non-Pro desktop. With up to 128GB of unified memory, it can run 70B parameter models at full precision — no quantisation required.
ram
Up to 128GB unified
cpu
M4 Max (16-core)
gpu
M4 Max (40-core)
neural Engine
32-core, 38 TOPS
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.
cpu
AMD Ryzen 9 7940HX
gpu
AMD Radeon RX 7700S (8GB GDDR6)
ram
Up to 64GB DDR5
storage
Up to 8TB NVMe
Pros
Cons
Raspberry Pi 5 (8GB)
The most affordable way to experiment with local AI
The Raspberry Pi 5 is the most accessible entry point into local AI. With 8GB of LPDDR4X RAM, it can run small language models (1B–3B parameters) via llama.cpp and Ollama.
cpu
Broadcom BCM2712 (4-core Arm Cortex-A76)
ram
8GB LPDDR4X
storage
microSD / NVMe via HAT
power
5W idle, 12W peak
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 and deploy computer vision pipelines.
ram
8GB LPDDR5
cpu
6-core Arm Cortex-A78AE
gpu
1024-core NVIDIA Ampere
ai Perf
40 TOPS
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. With 64GB of unified LPDDR5 memory, it can run 13B models locally and deploy production AI pipelines.
ram
64GB LPDDR5
cpu
12-core Arm Cortex-A78AE
gpu
2048-core NVIDIA Ampere
ai Perf
275 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. 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
Beelink SEI12 Pro (Intel Core i7-12650H)
The budget mini PC that punches above its weight for AI
The Beelink SEI12 Pro is the best 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.
cpu
Intel Core i7-12650H (10-core)
gpu
Intel Iris Xe (integrated)
ram
32GB DDR4
storage
500GB 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.
cpu
AMD Ryzen Z1 Extreme
gpu
AMD Radeon 780M (integrated)
ram
24GB LPDDR5X shared
storage
1TB NVMe
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. The best Intel CPU for building a Windows AI PC from scratch.
cores
24 cores (8P + 16E)
npu
Intel AI Boost NPU (13 TOPS)
tdp
125W (up to 250W PL2)
socket
LGA1851
Pros
Cons
Razer Blade 18 (RTX 4090, 32GB)
Desktop-class AI performance in a premium laptop
The Razer Blade 18 is the most powerful AI laptop money can buy. Its full-power RTX 4090 with 16GB GDDR6 VRAM delivers near-desktop performance for AI workloads.
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.
chip
Google Edge TPU
performance
4 TOPS (int8)
interface
USB 3.0
power
~2W
Pros
Cons
Logitech MX Master 3S
The productivity mouse built for AI power users
The MX Master 3S is the preferred mouse of AI developers and power users worldwide. Its customisable buttons, MagSpeed scroll wheel, and multi-device support make it ideal for switching between AI tools.
dpi
200–8,000 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. The Samsung 990 Pro delivers 7,450 MB/s sequential read speeds, meaning a 7GB Llama model loads in under 1 second.
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. This 64GB DDR5 kit opens the door to running 30B–70B models on compatible systems.
capacity
64GB (2x32GB)
type
DDR5-5600
latency
CL46
voltage
1.1V
Pros
Cons
Anker USB-C Hub 16-in-1 (USB4)
Connect all your AI peripherals to a single USB-C port
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.
ports
16 ports total
interface
USB4 / Thunderbolt 4
video Out
Dual 4K@60Hz
data Speed
10Gbps
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.