Which laptops are best suited for AI tasks?

The best laptop for AI depends heavily on what kind of AI work you mean. A laptop for using AI apps is very different from one for running local LLMs, training models, Stable Diffusion, or machine learning development. In general, prioritize:

• GPU power and VRAM (most important for CUDA-based AI workloads)
• Unified memory/RAM capacity (important for running large local models)
• Cooling (sustained performance matters)
• Storage (AI models consume lots of space)

StorageReview.com Compare AI Hardware## Best AI laptops overall

1. Apple MacBook Pro — Best for local AI + creators

Best for: AI coding, local LLM inference, data science, video/image work, long battery life

Recommended configuration:

• M5 Pro or M5 Max
• 48GB–128GB unified memory if running larger models
• 1TB+ SSD

Why it stands out:

• Huge unified memory options help with local AI models
• Excellent battery life and quiet operation
• Strong performance for AI-assisted creative workflows

The MacBook Pro M5 Max is frequently recommended for large-model inference because Apple’s unified memory architecture lets the GPU access a large shared memory pool.

Example:

Downside: CUDA support is not as mature as NVIDIA’s ecosystem for deep learning training.

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2. ASUS — Best for AI development and CUDA

Best for: PyTorch, TensorFlow, Stable Diffusion, model fine-tuning

Look for:

• NVIDIA RTX 5090 Laptop GPU
• 24GB VRAM
• 64GB+ RAM
• Strong cooling system

Why:

• NVIDIA CUDA remains the standard for many AI frameworks
• RTX GPUs provide dedicated VRAM and Tensor cores
• Better compatibility for AI development tools

High-end RTX 5090 laptops are among the fastest mobile systems for AI workloads that fit within their VRAM limits.

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3. ASUS — Best portable AI workstation

Best for: Developers who travel and want big memory in a small package

Recommended:

• Ryzen AI Max+ chip
• 64GB or 128GB RAM

Why:

• Very high memory capacity for a portable machine
• Can run surprisingly large local models
• More portable than RTX gaming laptops

It has been highlighted as a strong AI laptop because of its ability to handle larger local models through its memory architecture.

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4. Razer — Best premium Windows AI laptop

Best for: Developers who want gaming performance plus a professional design

Strengths:

• RTX 5090 option
• High-end CPU
• Good display
• Portable compared with larger desktop-replacement laptops

RTX 5090-equipped Razer laptops are positioned among the fastest mobile AI-capable systems.

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5. Dell — Best balanced AI productivity laptop

Best for: Office work, coding, AI assistants, occasional local AI

Good choice if you:

• Use ChatGPT/Copilot-style tools
• Do programming
• Need portability
• Do not train models locally

The Dell XPS line is among the stronger all-around AI laptop choices for mainstream users.

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Quick recommendations by use case

Minimum specs I would target in 2026

• Light AI use: 16–32GB RAM, modern AI CPU/NPU
• Serious AI development: 32–64GB RAM, NVIDIA GPU with 12GB+ VRAM
• Local LLM work: 64GB+ memory preferred
• Heavy AI workloads: RTX 5090 laptop or high-memory MacBook Pro

ai2.work Compare AI HardwareIf you tell me your budget and whether you mean AI programming, running ChatGPT-like models locally, image generation, or just using AI apps, I can narrow it down to 2–3 specific laptops.

The best laptops for AI tasks depend entirely on whether you are running and developing local machine learning models (heavy workloads) or utilizing consumer-facing Copilot+ tools and light local prototyping (on-device efficiency) . For heavy local training and inference, machines with dedicated NVIDIA RTX graphics cards or high-unified-memory Macs are required . For general smart features and on-the-go efficiency, laptops certified with next-generation Neural Processing Units (NPUs) are ideal.

Quick Comparison Matrix

Top Laptops Recommended for AI Tasks

Add to list Best for Local Inference & Prototyping

• Features the next-generation M5 processor equipped with an integrated Neural Accelerator in every core.
• Unified memory architecture allows the system to share up to 128GB of RAM with the GPU, making it uniquely capable of running large language models (LLMs) locally that would otherwise overwhelm consumer Windows graphics cards.
• Phenomenal thermal efficiency offers up to 24 hours of battery life while processing data on the fly.

Add to list Premium All-Rounder & Windows Development

• Driven by an Intel Core Ultra processor engineered with built-in Intel AI Boost.
• Combines Windows Copilot+ PC intelligence with up to 64GB of lightning-fast LPDDR5X memory.
• Offers configurations with dedicated NVIDIA graphics to comfortably support CUDA-based Python development and model building.

Add to list Best Portable Creator & Developer Studio

• Packs massive power via the AMD Ryzen AI processor coupled with an NVIDIA GeForce graphics card.
• Highly versatile 360-degree convertible design optimized with proprietary creator-focused local AI applications like MuseTree.
• 32GB of unified memory smoothly navigates complex development environments, dataset prepping, and AI-accelerated rendering simultaneously.

Add to list Best for Cloud-First Developers & Daily Productivity

• Powered by the Snapdragon X Elite chip, which features a dedicated Hexagon NPU capable of pulling off 45 trillion operations per second (TOPS).
• Perfect if your workflow involves writing backend agent logic, constructing data pipelines, and offloading heavy model training to cloud platforms like Google Colab.
• Offers an impressive 20-hour battery runtime and whisper-quiet operation during extended coding sessions.

What to Look for When Buying

If you plan to customize your machine, ensure it targets these expert metrics:

• RAM/Memory : Do not settle for less than 16GB. Opt for 32GB or 64GB if you want to deploy local LLMs or train neural networks without running into memory caps.
• GPU (Graphics) : If choosing Windows, a dedicated NVIDIA card is non-negotiable for serious AI engineering due to its industry-standard CUDA software ecosystem.
• NPU (Neural Processing Unit) : Look for 40+ TOPS of NPU performance to ensure compliance with advanced Windows Copilot+ features and highly efficient on-device background processing.

Perspectives From AI Developers

Experienced engineers typically emphasize that heavy model training belongs in the cloud rather than on consumer laptops:

“A MacBook Pro with as much ram as can be afforded. You still can't do anything meaningful on a laptop, but you can use it to prototype and send to a more powerful computer...”

To narrow down your options, tell me more about your specific plans:

• What kinds of models are you looking to run (e.g., local LLMs, deep learning models, or cloud-based API development)?
• What is your preferred operating system (Windows or macOS)?
• Do you need something ultra-portable with long battery life , or a heavy workstation replacement?

When shopping for a laptop tailored for local AI tasks (running small language models, rapid local inference, creative AI generation, or heavy multitasking via Copilot+ features), look for a dedicated NPU (Neural Processing Unit) delivering high TOPS (Trillion Operations Per Second—ideally 45–50+ TOPS), coupled with at least 32GB of RAM for smooth local model handling.

Top-tier AI laptop options span across major hardware ecosystems:

Powered by the AMD Ryzen AI processors featuring robust local NPU capability (up to 50 TOPS), wrapped in a sleek, ultra-thin Ceraluminum chassis with a stunning 3K 120Hz OLED display.

An ideal ultra-portable 2-in-1 convertible for creative professionals. It packs next-gen AMD Ryzen AI hardware, creator-focused software integration (like MuseTree and StoryCube), and high unified memory architecture for on-the-go AI workflows.

Built around Intel's Core Ultra architecture with integrated Intel AI Boost delivering roughly 50 NPU TOPS, paired with exceptional multi-day battery life and military-grade durability.

While not technically branded as a Windows "Copilot+ PC," Apple's unified memory configuration and powerful internal Neural Engine make MacBooks absolute powerhouses for local developer-focused AI/ML pipelines, local fine-tuning, and running open-source LLMs (via Ollama or LM Studio).

If you want to narrow down your choice, tell me:

• Are you planning to run local AI models/coding or just use productivity/creative AI tools?
• Do you prefer Windows Copilot+ PCs or macOS?

If by AI tasks you mean running local LLMs, AI coding tools, image/video generation, fine-tuning, or machine-learning development, the best laptop depends heavily on whether you need NVIDIA/CUDA or lots of unified memory.

My top picks for 2026

• Apple MacBook Pro 16-inch M5 Max - $4,999.00 (Best Buy)

• ASUS ROG Zephyrus G16 RTX 5090 - $4,399.99 (Best Buy)

• Lenovo Legion Pro 7i RTX 5090 - $4,599.99 (Best Buy)

• ASUS ProArt P16 RTX 5090 - $5,499.99 (Best Buy)

• Best for large local AI models: MacBook Pro M5 Max — Apple now offers M5 Max configurations with very large unified-memory capacities, making them particularly attractive for running models that exceed the 16–24 GB VRAM of typical NVIDIA laptops. Apple also specifically emphasizes on-device AI performance with M5 Pro/Max.

• Best for AI development/training: RTX 5090 laptop — An RTX 5090 laptop provides 24 GB of dedicated GDDR7 VRAM and the CUDA ecosystem, which remains the safer choice for PyTorch, CUDA-based development, fine-tuning, and GPU-accelerated workloads.

• Best value high-performance option: Lenovo Legion Pro 7i RTX 5080/5090 — A good choice if sustained GPU performance matters more than having an ultra-thin machine.

• Best AI + creative-workstation combination: ASUS ProArt P16 RTX 5090 — Especially compelling if you're also doing video editing, 3D, photography, or other GPU-heavy creative work.

What specs matter most

One important distinction: NPU/TOPS marketing isn't nearly as important as GPU/VRAM or unified-memory capacity for serious local AI.

Which one I'd buy

• Running 30B–70B+ LLMs locally: MacBook Pro M5 Max with 64–128 GB unified memory
• Training/fine-tuning with PyTorch/CUDA: RTX 5090 laptop with 24 GB VRAM
• AI image/video generation: RTX 5090
• AI coding + normal productivity: MacBook Pro M5 Pro/Max
• AI + gaming: RTX 5090 Lenovo/ASUS/Razer
• AI + creative professional work: ASUS ProArt P16 RTX 5090

For example, a currently available MacBook Pro 16" M5 Max with 48 GB/2 TB is around $5,000, while an ASUS ROG Zephyrus G16 with RTX 5090, 32 GB/2 TB is around $4,400 in current listings.

If you tell me your budget (e.g. $1,500 / $2,500 / $4,000+) and whether you're mainly doing local LLMs, ChatGPT/Copilot-style AI, image generation, or AI/ML programming, I can narrow this to the 3 best laptops for you.

Choosing the "best" laptop for AI depends entirely on whether you are doing light productivity AI (copilots, real-time transcription, background blurring), creative AI (local image generation like Stable Diffusion/Flux), or heavy machine learning engineering (training models, fine-tuning LLMs with PyTorch/TensorFlow).

Laptops are broken down below by category and hardware capability (NPUs vs. Discrete GPUs).

1. Best for Heavy Machine Learning & Deep Learning (NVIDIA CUDA)

If your work requires training neural networks, running local 27B+ parameter LLMs, or utilizing frameworks like PyTorch and TensorFlow, an NVIDIA RTX GPU with high VRAM is mandatory because of the CUDA ecosystem.

Asus ROG Zephyrus G16 Go to product viewer dialog for this item. / Strix SCAR 18 : These machines pack high-end mobile NVIDIA RTX architecture (including RTX 4090 or newer 50-series options) with up to 16GB of dedicated VRAM . They offer the thermal headroom and massive parallel processing required for serious local model work.

*Lenovo ThinkPad P1 / P16s Go to product viewer dialog for this item. * : The gold standard for data science and AI engineering workstations . They offer professional ISV certifications, highly stable drivers, and configurations supporting massive amounts of system RAM alongside discrete GPU power.

Laptops with high VRAM GPUs, such as the Zephyrus G16 Go to product viewer dialog for this item. and ThinkPad P1 Go to product viewer dialog for this item. , provide reliable, memory-efficient performance necessary for intensive, long-duration deep learning tasks and model training.

2. Best for AI Development, Prototyping & Inference (Apple Silicon)

For developers prioritizing a Unix-based system with high efficiency and excellent battery life, Apple’s M-series chips excel at local inference.

*Apple MacBook Pro (16-inch, M4/M5 Pro or Max)Go to product viewer dialog for this item. * : Features unified memory, allowing large models to run efficiently by allocating memory directly to the GPU/Neural Engine . It is highly optimized for local inference, rapid prototyping, and development workflows.

3. Best Copilot+ / Thin-and-Light AI PCs (NPU-Focused)

For thin-and-light portability with all-day battery life and on-device AI efficiency, look for Copilot+ certified PCs with NPUs exceeding 40–50+ TOPS.

*ASUS Zenbook S 16 / A16 Go to product viewer dialog for this item. * : Features high-performance NPUs (50–80+ TOPS) on AMD or Snapdragon platforms, designed for slim portability and superior battery efficiency.

*HP OmniBook Ultra 14 Go to product viewer dialog for this item. * : Combines an AMD Ryzen AI processor with strong sustained performance for everyday AI-enhanced tasks [1.20].

*Acer Swift Go 16 AI Go to product viewer dialog for this item. * : A high-value option with ample RAM and capable NPUs for everyday multitasking and local AI functionality.

Key Specs Checklist Before Buying

To ensure a smooth AI experience, prioritize the following specifications:

• RAM/Unified Memory : 32 GB is the recommended minimum for local models or heavy coding/Docker work, with 64 GB preferred.
• Storage : 1 TB NVMe SSD or higher is necessary for storing large datasets and model weights.
• AI Accelerator : Select an NVIDIA RTX GPU (≥ 12 GB VRAM) for training/coding , a high-memory Apple Silicon chip for macOS, or an NPU with ≥ 45 TOPS for Windows on-device acceleration.

To help narrow down the ideal choice, tell me:

If by AI tasks you mean running models locally—LLMs, image generation, AI coding, fine-tuning, or machine-learning development—the best laptop depends heavily on whether you want NVIDIA/CUDA or large unified memory.

My top picks for 2026

MacBook Pro 16-inch M5 Max 128GB

$7,489.00

ASUS ROG Zephyrus G16 RTX 5090

$4,399.99

MSI Raider 18 HX AI RTX 5090

$3,899.99

Acer Predator Helios Neo 16 AI RTX 5070 Ti

$1,999.99

The important distinction

For AI/ML development: I'd choose an RTX 5090 Windows laptop. CUDA remains the safer choice for PyTorch, CUDA libraries, fine-tuning, and GPU-based experimentation.

For running large LLMs locally: I'd seriously consider a MacBook Pro M5 Max with 96GB or 128GB unified memory. The large memory pool can be more useful than a faster GPU when the model simply doesn't fit in GPU VRAM.

For AI image/video generation: RTX 5090 is generally the better choice because of NVIDIA's software ecosystem and dedicated VRAM.

For AI coding assistants, ChatGPT/Copilot, transcription, summarization, etc.: you don't need a $4,000+ workstation. A MacBook Air M5 or good Copilot+ Windows laptop with 32GB RAM is plenty.

What I'd prioritize

For a serious AI laptop, aim for:

• 32GB RAM minimum; 64GB+ for local models
• 1–2TB SSD
• NVIDIA RTX 5070 Ti or better if you're doing GPU-based AI
• RTX 5090 / 24GB VRAM if budget allows
• Or 96–128GB unified memory on an M5 Max MacBook Pro for large local LLMs
• Good cooling—AI workloads can run the machine at sustained maximum power
• Don't choose solely based on NPU TOPS; for serious local AI, RAM, VRAM, GPU compute, and software compatibility are generally more consequential.

Bottom line: If I were buying specifically for AI today, I'd narrow it to MacBook Pro M5 Max 128GB for local LLM inference and RTX 5090 Windows for CUDA/ML development and training.

If you tell me your budget (e.g. $1,500, $2,500, $4,000) and whether you want AI coding, local ChatGPT/LLMs, image generation, or machine-learning training, I can narrow this to the 3 best choices.

The best laptops for AI tasks are the Apple MacBook Pro M5 / M4 Max Go to product viewer dialog for this item. (ideal for local model prototyping and massive unified memory pools) and high-end Windows machines like the ASUS ROG Zephyrus G16 Go to product viewer dialog for this item. or Lenovo ThinkPad P1 Gen 7 Go to product viewer dialog for this item. (equipped with dedicated NVIDIA RTX GPUs and powerful NPUs for CUDA and hybrid workloads).

The ideal laptop depends heavily on whether you are running lightweight productivity features, coding local agents, or training deep neural networks.

Hardware Factors for AI

• Unified Memory vs. VRAM: Large Language Models (LLMs) need massive memory. Apple Silicon allows the system RAM to act as unified GPU memory, making a 48GB+ MacBook ideal for running large open-weights models locally. For Windows, an NVIDIA RTX card with dedicated VRAM is mandatory for CUDA-accelerated machine learning.
• NPUs (Neural Processing Units): Measured in TOPS (Trillion Operations Per Second), modern chips (like Intel Core Ultra, AMD Ryzen AI 300 series, and Snapdragon X) feature dedicated NPUs. These handle localized light AI tasks (background blurring, live captions, Copilot features) efficiently without draining your battery.
• System RAM: 32GB of RAM is the realistic minimum for heavy AI development, multi-container environments, and local agent testing in 2026; 16GB will bottleneck quickly when juggling IDEs, local vector stores, and browser contexts.

Developer Perspectives

If you tell me whether you plan to run models locally / train neural networks or rely on cloud APIs / use Copilot productivity tools , I can narrow down the ideal budget and configuration for you.

The “best” AI laptop depends on what you mean by AI work:

• Running local AI models (LLMs, Stable Diffusion, coding models) → prioritize GPU VRAM or large unified memory
• AI development (PyTorch, TensorFlow, CUDA) → prioritize NVIDIA RTX GPU
• Everyday AI features (Copilot, Apple Intelligence, transcription, summaries) → prioritize a modern NPU-equipped CPU
• AI-assisted creative work (video, images, 3D) → prioritize GPU + RAM + cooling

Best AI laptops by use case

My recommendations

1. Best overall AI laptop

MacBook Pro 16-inch with M5 Pro (48GB+ memory if possible)
Best balance of:

• local AI experimentation
• programming
• battery life
• portability
• creative work

Apple’s unified memory can be very useful for running larger local models because CPU/GPU memory is shared.

2. Best for serious AI/ML development

Windows laptop with NVIDIA RTX 5080/5090 Laptop GPU

Look for:

• RTX 5080 or RTX 5090 Laptop GPU
• 32–64GB RAM
• 1–2TB SSD
• strong cooling system

NVIDIA remains the easiest path for CUDA-based AI development.

Good examples:

• ASUS ROG Zephyrus G16 RTX 5090
• ASUS ProArt P16 RTX 5090
• Razer Blade 16 RTX 5090

3. Best budget AI laptop

Aim for:

• 16–32GB RAM
• modern Intel Core Ultra / AMD Ryzen AI processor
• RTX 4060/4070/5060-class GPU if you want local models

Avoid buying an “AI PC” based only on the NPU marketing. NPUs are great for lightweight AI features, but GPU power matters much more for training, fine-tuning, and running larger models locally.

Specs I would target in 2026

Minimum for serious AI work

• RAM: 32GB
• Storage: 1TB SSD
• GPU: RTX 4070 or better (Windows)
• Memory: 24–48GB unified memory (Mac)

Heavy AI workloads

• RAM: 64GB+
• GPU: RTX 5080/5090
• VRAM: 16GB+ preferred
• Cooling: large chassis/workstation design

If you tell me your budget and whether you mean AI coding, running ChatGPT-like models locally, image generation, video AI, or just using AI apps, I can narrow it to 3–5 specific laptops.

If by AI tasks you mean running local LLMs, image generation, AI coding, model experimentation, or ML development, the key specs are GPU power, VRAM/unified memory, CUDA support, and cooling—not just an “AI PC” NPU.

My top picks in 2026

ASUS ProArt P16 RTX 5090

$5,499.99

ASUS ROG Zephyrus G16 RTX 5090

$4,599.99

Apple MacBook Pro 16-inch M5 Max

$4,999.00

ASUS ROG Zephyrus G14 RTX 5080

$3,129.00

Lenovo Legion Pro 7i RTX 5090

$4,599.99

The important distinction: NVIDIA vs. Apple

For serious AI/ML development, I'd generally choose NVIDIA. CUDA remains the safest choice for PyTorch, TensorFlow, CUDA-accelerated libraries, local image-generation tools, and research software. An RTX 5090 laptop gives you 32GB of dedicated VRAM, which is extremely valuable.

For example, the ProArt P16 configuration currently listed has an RTX 5090, 64GB system memory and 4TB SSD.

For local LLMs specifically, the M5 Max MacBook Pro is unusually compelling. Its unified memory can be configured dramatically higher than laptop GPU VRAM—up to 128GB—and Apple has specifically improved the M5 Pro/Max architecture for on-device AI. Independent testing also ranks the M5 Pro MacBook Pro among the strongest AI laptops.

That makes the choice roughly:

• CUDA / PyTorch / ML research → RTX 5090
• Stable Diffusion / ComfyUI → RTX 5090
• AI video generation → RTX 5090
• Gaming + AI → RTX 5090
• Large local LLMs → M5 Max 96/128GB can be very attractive
• AI coding + many local models → M5 Max 64–128GB
• Battery life + AI → M5 Max
• Maximum raw GPU throughput → RTX 5090

One caveat: 32GB VRAM is still a hard constraint on an RTX 5090 laptop. Larger models can exceed it and require CPU/RAM offloading, which can dramatically reduce performance. Conversely, Apple's 128GB unified memory isn't equivalent to 128GB of high-speed dedicated VRAM—the GPU architecture and software stack matter too. Community testing reflects this trade-off: 5090 tends to win on speed, while high-memory Apple systems can accommodate larger models.

What I'd buy

If you want one laptop for serious AI work:
RTX 5090 + 64GB RAM + 2TB/4TB SSD. The ASUS ProArt P16 is particularly appealing because it's less of a stereotypical gaming laptop.

If your priority is local LLMs and AI agents:
MacBook Pro M5 Max with 96GB or 128GB unified memory. The memory capacity is the big attraction.

If your budget is around $2,000–$3,000:
I'd look for an RTX 5080 laptop with 16GB VRAM and 32–64GB RAM rather than spending money on an NPU-heavy machine with a weak discrete GPU.

If you tell me your budget and the AI work you want to do (e.g. ChatGPT/Copilot, local LLMs, image generation, coding, model training, or video), I can narrow this to the 3 best laptops for you.

The "best" laptop for AI tasks depends heavily on what kind of AI work you are doing: light daily productivity/Copilot+ features (using an NPU) versus heavy local model training and running large language models (LLMs) (using a high-VRAM GPU or Apple Silicon unified memory).

Category 1: Everyday AI, Office Productivity, & Light Inference (Copilot+ PCs)

Best for: Windows Studio effects, live translation, on-device automation, and general power-efficiency. Requires a dedicated Neural Processing Unit (NPU) hitting at least 45–80 TOPS.

• Microsoft Surface Laptop 7 (Snapdragon X Elite)Go to product viewer dialog for this item. : A premier thin-and-light choice featuring a 45 TOPS NPU, crisp 120Hz touchscreen, and stellar battery life.
• ASUS Zenbook A14/A16 OLED (Snapdragon X2 Elite)Go to product viewer dialog for this item. : Pushes NPU performance higher (up to 80 TOPS on select configurations) wrapped in an ultra-lightweight frame with great OLED visuals.
• Lenovo Yoga Slim 7x / Yoga 7a (AMD Ryzen AI 9)Go to product viewer dialog for this item. : Balances a strong ~50 TOPS AMD NPU with superb sustained performance and an incredible OLED display, avoiding some of the app compatibility quirks of ARM architecture.
• Acer Swift Go 14 AI Go to product viewer dialog for this item. : A fantastic value-focused entry point for Copilot+ certification, offering high-end NPU capability without breaking the bank.

Category 2: Power Users & Local LLM Runners (Unified Memory)

Best for: Running large open-source language models (like Llama or Gemma) locally in memory, data science, and development prototyping.

• Apple MacBook Pro (M4/M5 Pro or Max)Go to product viewer dialog for this item. : The gold standard for local model inference and software development. Thanks to Apple's unified memory architecture, you can spec these with 48GB, 64GB, or up to 128GB of RAM shared directly with the GPU, allowing you to load much larger models locally than traditional discrete mobile GPUs permit.

Category 3: Heavy Duty Machine Learning & CUDA Workstations

Best for: CUDA-dependent deep learning, model fine-tuning, and heavy graphic/AI creator workflows.

If your toolchain relies on CUDA, you need an NVIDIA discrete GPU. Top-tier gaming/creator flagships pack massive mobile GPUs with up to 16GB–24GB of dedicated VRAM, making them absolute powerhouses for local training and accelerated rendering.

Tailored explicitly for creative professionals and engineers who need a blend of an AMD Ryzen AI processor (for NPU tasks) paired with a robust NVIDIA RTX discrete graphics card.

Perspectives on Local AI Hardware

To help narrow down the best choice, tell me:

• Are you planning to run models locally (like Llama or Stable Diffusion) or just use cloud/system AI features?
• What is your budget range?