Best Hardware for Running Local AI Models (2026 Guide)

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Best Hardware for Running Local AI Models in 2026

A budget-tiered guide to the best GPUs, mini PCs, and Apple Silicon machines for running local LLMs privately in 2026 — from $300 starter builds to prosumer workstations.

ModemGuides Support Updated on Mar 28, 2026

Last updated: March 2026

Key Takeaways

Running AI models on your own hardware keeps your data private, eliminates recurring API costs, and frees you from dependence on cloud providers. VRAM (video memory) is the single most important spec — not clock speed, not core count.
A used NVIDIA RTX 3090 (24 GB VRAM, around $650–750) remains the best value GPU for local AI in 2026, capable of running most 7B–70B parameter models at usable speeds. For new hardware, the RTX 4060 Ti 16 GB (~$400) is the sweet spot for smaller models.
You do not need a $2,000 GPU to get started. A mini PC with 32 GB of RAM or a 16 GB MacBook can run capable 7B–8B models that handle everyday coding, writing, and research tasks surprisingly well.

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Why Run AI Locally?

Every prompt you send to a cloud AI service leaves your machine, passes through third-party infrastructure, and gets processed on servers you do not control. For anyone handling sensitive documents, proprietary code, client data, or personal conversations, this is a meaningful privacy risk — not a theoretical one.

Running AI models locally changes the equation entirely. Your data never leaves your network. There are no API fees accumulating with every prompt. No rate limits. No terms-of-service changes that could cut off your access overnight. And when your internet goes down, your local AI keeps working.

This is the same principle behind owning your own modem instead of renting from your ISP — when you control the hardware, you control the experience. Local AI is digital sovereignty applied to artificial intelligence.

The practical case is equally strong. A one-time hardware investment of $500–$1,500 can replace $100–$200 per month in API costs for developers and power users. Most setups pay for themselves within four to eight months of regular use, and electricity costs typically add only $5–$15 per month depending on usage.

And the quality gap has closed dramatically. Modern open-weight models running on consumer hardware now handle 70–80% of everyday tasks — coding assistance, document summarization, research, writing, and general question-answering — at levels that were exclusive to premium cloud APIs just two years ago.

The Golden Rule: Buy Memory, Not Speed

Before looking at any specific hardware, you need to understand the single most important concept in local AI: VRAM is everything.

When you run an AI model locally, the entire model's weights need to fit in your GPU's video memory (VRAM) for full-speed inference. If the model does not fit, your system falls back to slower system RAM or disk, and performance drops dramatically — from 30+ tokens per second down to 3–5.

Think of VRAM as counter space in a kitchen. The GPU's processing speed is how fast the chef's hands move. But if the recipe (the AI model) does not fit on the counter, the chef has to keep running back to the storage room, and everything slows to a crawl.

This is why a $700 used GPU with 24 GB of VRAM often outperforms a $500 new GPU with 8 GB of VRAM for AI workloads — even if the newer card is technically faster at gaming.

How Quantization Stretches Your Hardware

Quantization is the process of compressing a model's weights from their full precision (typically 16-bit floating point) down to 4-bit or even lower. This reduces the model's memory footprint by roughly 75% with surprisingly little quality loss for most practical tasks.

The standard quantization format in 2026 is Q4_K_M (4-bit with mixed precision), which offers the best balance of quality and memory savings. Here is a rough guide to VRAM requirements at Q4 quantization:

7B–8B parameter models: ~4–5 GB VRAM (runs on 8 GB GPUs comfortably)
13B–14B parameter models: ~8–10 GB VRAM (needs 12–16 GB GPUs)
30B–34B parameter models: ~18–20 GB VRAM (needs 24 GB GPUs)
70B parameter models: ~35–40 GB VRAM (needs 48 GB+ or CPU offloading)

A practical rule of thumb: your model's size on disk in gigabytes roughly equals the VRAM (or RAM) it needs, plus 2–3 GB of headroom for your operating system and context window.

GPU Comparison: The Hardware That Matters Most

The following table compares the GPUs most commonly used for local AI inference in 2026. All prices reflect typical retail or used-market pricing as of March 2026.

NVIDIA GPUs for Local AI (March 2026)

GPU	VRAM	Bandwidth	Approx. Price	Max Model Size (Q4)
RTX 3060 12 GB	12 GB GDDR6	360 GB/s	$180–$220 (used)	~13B parameters
RTX 4060 Ti 16 GB	16 GB GDDR6	288 GB/s	~$400 (new)	~14B parameters
RTX 3090 24 GB	24 GB GDDR6X	936 GB/s	$650–$750 (used)	~34B parameters
RTX 4090 24 GB	24 GB GDDR6X	1,008 GB/s	$1,100–$1,800	~34B parameters
RTX 5090 32 GB	32 GB GDDR7	1,792 GB/s	$1,999+ MSRP (often $2,500+)	~40B+ parameters

RTX 3060 12 GB

VRAM12 GB GDDR6

Bandwidth360 GB/s

Price$180–$220 (used)

Max Model (Q4)~13B parameters

RTX 4060 Ti 16 GB

VRAM16 GB GDDR6

Bandwidth288 GB/s

Price~$400 (new)

Max Model (Q4)~14B parameters

RTX 3090 24 GB

VRAM24 GB GDDR6X

Bandwidth936 GB/s

Price$650–$750 (used)

Max Model (Q4)~34B parameters

RTX 4090 24 GB

VRAM24 GB GDDR6X

Bandwidth1,008 GB/s

Price$1,100–$1,800

Max Model (Q4)~34B parameters

RTX 5090 32 GB

VRAM32 GB GDDR7

Bandwidth1,792 GB/s

Price$1,999+ MSRP (often $2,500+)

Max Model (Q4)~40B+ parameters

Key takeaway: The used RTX 3090 at around $700 delivers 24 GB of VRAM — the same as the RTX 4090 — at roughly half the price. The 4090 is about 25% faster per token, but the 3090 is the best dollar-per-gigabyte value in local AI today. If you are buying new and want to stay under $500, the RTX 4060 Ti 16 GB is the right choice — but do not buy the 8 GB version, which fills up immediately with even modest models.

What about AMD GPUs? AMD's ROCm software ecosystem continues to improve in 2026, with growing support in llama.cpp and other inference tools. The RX 7900 XTX offers 24 GB VRAM at a competitive price. However, NVIDIA's CUDA ecosystem remains significantly more mature for AI workloads, with better optimization, broader tool support, and more community resources. If you are building specifically for local AI and want the smoothest experience, NVIDIA is still the safer choice. If you already own a recent AMD GPU, it is worth trying — the gap is closing.

Hardware Builds by Budget

Entry Level: $0–$300 (CPU-Only and Existing Hardware)

You do not need to buy anything new to get started with local AI. If you have a laptop or desktop with at least 16 GB of RAM, you can run capable small models right now using CPU-only inference.

What you can run: 7B–8B parameter models at 5–15 tokens per second. This is slower than GPU inference, but it is genuinely usable for coding assistance, document Q&A, writing help, and conversational tasks.

Minimum requirements:

16 GB RAM (32 GB recommended)
Modern CPU with 8+ cores
SSD with at least 20 GB free space for model files

Best budget upgrade: If your laptop supports it, upgrading from 16 GB to 32 GB of RAM (~$40–$60 for DDR4 SO-DIMMs) is the single most impactful improvement you can make. This gives you headroom for 13B models and eliminates out-of-memory crashes during longer conversations.

DDR4 32 GB Laptop RAM Kit on Amazon

If you want a dedicated always-on device for small models, a mini PC like the Beelink EQR6 (~$389) with 32 GB RAM runs 7B models 24/7 at low power consumption (typically 15–25 watts under inference load) and near-silent operation.

Mid-Range: $300–$1,000 (The Sweet Spot)

This is where local AI gets genuinely useful for daily work. A dedicated GPU with 12–24 GB of VRAM opens up the 7B–34B model range at speeds that feel conversational (20–50 tokens per second).

Recommended builds:

Option A — Budget GPU Build (~$600–$800 total)

Used NVIDIA RTX 3060 12 GB (~$180–$220)
32 GB DDR4 RAM Kit (~$50)
1 TB NVMe SSD (~$60)
Existing desktop PC or a budget case/PSU/motherboard combo

This setup runs 7B–13B models at 20–35 tokens per second. Strong enough for coding assistants, summarization, and general chat.

Option B — Best Value GPU Build (~$900–$1,100 total)

Used NVIDIA RTX 3090 24 GB (~$650–$750)
64 GB DDR5 RAM Kit (~$80–$120)
2 TB NVMe SSD (~$100)
750W+ power supply (the 3090 draws 350W+ under load)

The 24 GB of VRAM lets you run 30B–34B parameter models and even load 70B models with partial CPU offloading. This is the build that most experienced local AI users recommend in 2026 — it hits the price-to-capability sweet spot.

Option C — Mini PC with External GPU (~$800–$1,200 total)

MINISFORUM UM880 Plus (~$749) with 32 GB RAM and OCuLink port
External GPU enclosure via OCuLink + used RTX 3060 or RTX 3090

This approach gives you a compact, quiet system with the option to add GPU acceleration later. Many 2026 mini PCs include OCuLink or USB4 ports specifically for this purpose.

A note on internet speed: Running AI locally means your hardware does all the processing — but downloading models (5–40 GB each) and keeping them updated still depends on your connection. If you are on a slow plan, upgrading your router or switching to a faster tier can make the setup process significantly smoother.

Prosumer: $1,000–$3,000 (70B Models and Beyond)

At this tier, you can run the largest consumer-accessible models with full GPU acceleration and fast token generation.

Option A — NVIDIA RTX 4090 Build

The 4090 delivers roughly 50+ tokens per second on 70B models with quantization. Its 1,008 GB/s memory bandwidth is the key advantage over the 3090 for large model inference.

Option B — Mac Studio M4 Max (64–128 GB Unified Memory)

Mac Studio with M4 Max, 64 GB unified memory: starts at $2,599
Mac Studio with M4 Max, 128 GB unified memory: starts at $3,599

Apple Silicon takes a fundamentally different approach. Instead of discrete GPU VRAM, M-series chips use unified memory shared between CPU and GPU. A Mac Studio with 128 GB of unified memory can load models that would require multiple GPUs on a PC — including 70B parameter models running entirely in memory.

The tradeoff: Apple Silicon is slower per token than a dedicated NVIDIA GPU at the same model size. A Mac Studio M4 Max generates roughly 15–25 tokens per second on a 70B model versus the RTX 4090's 50+. But the Mac runs near-silently, draws far less power, and offers a significantly simpler setup experience. For users who prioritize quiet operation and do not need maximum throughput, it is a compelling option.

Option C — NVIDIA RTX 5090 Build

NVIDIA RTX 5090 32 GB ($1,999 MSRP, often $2,500+ street price)
64 GB DDR5 RAM (~$120)
2 TB NVMe SSD (~$100)
1000W+ PSU (the 5090 draws 575W under load)

The RTX 5090 is the flagship consumer GPU for AI in 2026. Its 32 GB of GDDR7 VRAM and 1,792 GB/s memory bandwidth represent meaningful upgrades over the 4090 — roughly 40% faster AI inference and 8 GB more VRAM for fitting larger models or longer context windows. However, availability remains limited and street prices frequently exceed MSRP. If you can find one at a reasonable price and need the absolute best single-GPU performance, it is worth the investment. Otherwise, the 4090 remains excellent value.

Workstation: $3,000+ (Brief Overview)

Dual-GPU setups (two RTX 3090s or 4090s via NVLink), the Mac Studio with M3 Ultra (96–192 GB unified memory starting at $3,999), or professional cards like the AMD Radeon PRO W7900 (48 GB, ~$3,500) serve users who need to run the largest open models without quantization or who serve multiple concurrent users. Most home users will not need this tier, but it exists for developers, researchers, and small teams replacing cloud API spending.

Open-Weight Models Worth Running Locally

The model you choose matters as much as the hardware you run it on. The following table covers the most capable open-weight models available through Ollama as of March 2026.

Top Local LLMs (March 2026)

Model	Parameters	Min RAM/VRAM (Q4)	Best For	License
Llama 3.3 8B	8B	~5 GB (8 GB system)	General chat, writing, summarization	Llama 3 Community
Mistral Small 3.1	24B	~14 GB	Instruction following, fast inference	Apache 2.0
Phi-4	14B	~9 GB (16 GB system)	Math, logic, structured reasoning	MIT
Qwen 2.5 Coder 14B	14B	~9 GB (16 GB system)	Code generation, debugging, tests	Apache 2.0
DeepSeek R1 14B	14B	~9 GB (16 GB system)	Chain-of-thought reasoning, analysis	MIT
Gemma 3 12B	12B	~8 GB (16 GB system)	Clean prose, content writing	Gemma License
Llama 4 Scout	109B total (17B active)	~24 GB	GPT-4-class general tasks (MoE)	Llama 4 Community
Qwen 2.5 72B	72B	~40 GB	Best open large model, multilingual	Qwen License
Llama 3.3 70B	70B	~40 GB	GPT-4-class general tasks	Llama 3 Community
Qwen3-Coder-Next	80B total (3B active)	~16 GB	Coding agents, tool use (MoE)	Apache 2.0

Llama 3.3 8B

Parameters8B

Min RAM/VRAM (Q4)~5 GB (8 GB system)

Best ForGeneral chat, writing, summarization

LicenseLlama 3 Community

Mistral Small 3.1

Parameters24B

Min RAM/VRAM (Q4)~14 GB

Best ForInstruction following, fast inference

LicenseApache 2.0

Phi-4

Parameters14B

Min RAM/VRAM (Q4)~9 GB (16 GB system)

Best ForMath, logic, structured reasoning

LicenseMIT

Qwen 2.5 Coder 14B

Parameters14B

Min RAM/VRAM (Q4)~9 GB (16 GB system)

Best ForCode generation, debugging, tests

LicenseApache 2.0

DeepSeek R1 14B

Parameters14B

Min RAM/VRAM (Q4)~9 GB (16 GB system)

Best ForChain-of-thought reasoning, analysis

LicenseMIT

Gemma 3 12B

Parameters12B

Min RAM/VRAM (Q4)~8 GB (16 GB system)

Best ForClean prose, content writing

LicenseGemma License

Llama 4 Scout

Parameters109B total (17B active)

Min RAM/VRAM (Q4)~24 GB

Best ForGPT-4-class general tasks (MoE)

LicenseLlama 4 Community

Qwen 2.5 72B

Parameters72B

Min RAM/VRAM (Q4)~40 GB

Best ForBest open large model, multilingual

LicenseQwen License

Llama 3.3 70B

Parameters70B

Min RAM/VRAM (Q4)~40 GB

Best ForGPT-4-class general tasks

LicenseLlama 3 Community

Qwen3-Coder-Next

Parameters80B total (3B active)

Min RAM/VRAM (Q4)~16 GB

Best ForCoding agents, tool use (MoE)

LicenseApache 2.0

Where to start: If you have 8 GB of RAM, install Ollama and pull Llama 3.3 8B. If you have 16 GB, try Phi-4 or Qwen 2.5 Coder 14B. If you have a GPU with 24 GB VRAM, Llama 4 Scout is the standout — its Mixture-of-Experts architecture activates only 17B parameters per query while delivering quality competitive with GPT-4 on many tasks.

A note on MoE (Mixture-of-Experts) models: Some models in the table above, like Llama 4 Scout and Qwen3-Coder-Next, use MoE architectures. These models have large total parameter counts but only activate a fraction of their weights for each query. This means they need enough memory to hold all the weights, but the actual compute per token is much lower — giving you the quality of a large model with the speed of a smaller one. MoE models are the most exciting development in local AI hardware efficiency in 2026.

Software: Getting Models Running

The software side of local AI has matured dramatically. You do not need to compile anything from source or fight dependency conflicts. Here are the four tools that matter:

Ollama is the easiest way to run local models. One command to install, one command to download and run a model. It provides an OpenAI-compatible API endpoint on your local machine, making it a drop-in replacement for cloud endpoints in existing tools and scripts. Ollama has over 100,000 stars on GitHub and is the community standard.

LM Studio provides a visual, ChatGPT-style interface for downloading and running models. If you prefer a graphical UI over the command line, this is the best option. It handles GPU detection, model management, and serving automatically.

Jan is an open-source assistant platform that wraps local models in a polished chat interface with optional cloud API integration for hybrid use.

llama.cpp is the low-level C++ runtime that powers most of the tools above. You rarely need to interact with it directly, but it is the engine behind Ollama's performance and the reason consumer hardware can run these models at all.

All four tools are free and open-source.

Pre-Built Options: Mini PCs for Local AI

Not everyone wants to build a desktop PC. Mini PCs offer a compact, energy-efficient alternative for running smaller models (7B–34B) with CPU-only or integrated GPU inference.

Mini PCs for Local AI (March 2026)

Mini PC	RAM	Approx. Price	Max Model (Q4)	Best For
Apple Mac mini M4 (24 GB)	24 GB unified	~$699	~14B	Silent operation, easy setup
Beelink SER8 (24 GB)	24 GB DDR5	~$450	~14B	Best value, always-on server
MINISFORUM UM880 Plus	32 GB DDR5	~$749	~14B–30B	OCuLink eGPU expansion
Beelink SER9 MAX (64 GB)	64 GB DDR5	~$799	~34B–70B (slow)	Largest Beelink for AI
Apple Mac mini M4 Pro (64 GB)	64 GB unified	~$1,799	~34B–70B (slow)	Quiet 70B inference

Apple Mac mini M4 (24 GB)

RAM24 GB unified

Price~$699

Max Model (Q4)~14B

Best ForSilent operation, easy setup

Beelink SER8 (24 GB)

RAM24 GB DDR5

Price~$450

Max Model (Q4)~14B

Best ForBest value, always-on server

MINISFORUM UM880 Plus

RAM32 GB DDR5

Price~$749

Max Model (Q4)~14B–30B

Best ForOCuLink eGPU expansion

Beelink SER9 MAX (64 GB)

RAM64 GB DDR5

Price~$799

Max Model (Q4)~34B–70B (slow)

Best ForLargest Beelink for AI

Apple Mac mini M4 Pro (64 GB)

RAM64 GB unified

Price~$1,799

Max Model (Q4)~34B–70B (slow)

Best ForQuiet 70B inference

Mini PCs excel as always-on home AI servers — running a local assistant, handling document processing, or powering Home Assistant automations. Their low power draw (15–65 watts) and small footprint make them ideal for 24/7 operation without the noise, heat, or electricity costs of a full desktop build.

Security and Privacy Considerations

Running AI locally is a significant privacy upgrade over cloud services, but it is not automatically secure. Here are the steps that matter:

Network isolation. If you run a local AI server that accepts API calls, make sure it is only accessible on your local network — not exposed to the internet. Ollama binds to localhost by default, which is correct. Do not change this without understanding the implications.

Model provenance. Only download models from trusted sources: the official Ollama library, Hugging Face model pages from verified organizations (Meta, Mistral, Google, Alibaba/Qwen), or direct project repositories. Quantized model files (GGUF format) from unknown uploaders could theoretically contain manipulated weights.

DNS-level privacy. Pair your local AI setup with a DNS-level ad and tracker blocker to prevent telemetry from model runners or system components phoning home. While Ollama and LM Studio are open-source and generally privacy-respecting, defense in depth is always worthwhile.

VPN for model downloads. If you want to prevent your ISP from seeing which AI models you download, route your traffic through a reputable VPN. We recommend Proton VPN or Mullvad VPN — both have strong no-logging policies and transparent ownership structures. We do not recommend VPN providers with opaque ownership or incentive structures that conflict with user privacy, regardless of affiliate commission potential.

Firmware and OS updates. Keep your GPU drivers, operating system, and inference tools updated. Local AI hardware is still a computer on your network, and basic security hygiene applies.

What Not to Do

A few common mistakes to avoid:

Do not buy a GPU with less than 12 GB of VRAM for AI. 8 GB cards fill up immediately and leave no room for context windows. The RTX 4060 8 GB and similar cards are frustrating for AI use despite being fine for gaming.
Do not assume you need the biggest model. Modern 7B–14B models in 2026 outperform the 70B models from two years ago on many benchmarks. Test smaller models first — you may be surprised by how capable they are.
Do not skimp on system RAM. Even with a dedicated GPU, your system RAM handles the operating system, context windows, and CPU offloading for models that partially exceed VRAM. 32 GB is the minimum for a dedicated AI machine; 64 GB is recommended.
Do not rely on cloud GPU rental as your primary solution. Services like RunPod and Lambda have their place for occasional heavy workloads, but the per-hour costs add up quickly for daily use. Owning your hardware is nearly always cheaper within a few months.

Recommended Shopping List by Budget

Here is a quick summary of every product recommended in this guide with direct purchase links.

Under $300 (Upgrades and Accessories)

$300–$800 (GPUs and Mini PCs)

$800–$2,000 (High-Performance)

$2,000+ (Flagship)

Frequently Asked Questions

Can I run AI models on a Mac?

Yes. Apple Silicon Macs are one of the best platforms for local AI in 2026 thanks to unified memory. A MacBook Pro with 36 GB of unified memory can run 30B+ models, and a Mac Studio with 128 GB can run 70B models entirely in memory. The M4 Max and M3 Ultra chips offer particularly strong inference performance. The tradeoff is that Apple Silicon is slower per token than a dedicated NVIDIA GPU at the same model size, but the silent operation, low power draw, and simple setup make it an excellent choice for many users.

How much VRAM do I need for a 70B parameter model?

At Q4_K_M quantization (the standard in 2026), a 70B model requires approximately 35–40 GB of VRAM or unified memory. This means you need either a dual-GPU setup, a Mac with 64 GB+ unified memory, an RTX 5090 with CPU offloading, or one of the newer mini PCs with 96–128 GB of RAM for CPU-only inference at slower speeds. A single RTX 4090 (24 GB) cannot fully load a 70B model without offloading significant portions to system RAM, which reduces performance substantially.

Is it cheaper to run AI locally or use a cloud API?

For regular daily use, local hardware almost always wins financially within three to eight months. A mid-range build costing $700–$1,000 can replace $100–$200 per month in API costs for developers who make heavy use of coding assistants and general-purpose AI. Electricity adds roughly $5–$15 per month depending on usage and local power rates. Cloud APIs remain more cost-effective for infrequent, bursty usage or when you need access to the very largest frontier models that cannot run on consumer hardware.

What is quantization and does it affect quality?

Quantization reduces the numerical precision of model weights — for example, from 16-bit floating point (FP16) down to 4-bit integers (Q4). This cuts memory requirements by roughly 75% while preserving most of the model's capability. At Q4_K_M quantization, most users cannot distinguish the output quality from the full-precision version for everyday tasks like chat, coding, and summarization. Quality loss becomes more noticeable on tasks requiring very precise numerical reasoning or highly creative writing, but for practical daily use, quantized models are excellent.

Do I need an NVIDIA GPU or will AMD work?

NVIDIA remains the recommended choice for local AI in 2026 due to its mature CUDA software ecosystem, which is supported by virtually every AI tool and framework. AMD's ROCm platform has improved significantly and works with popular tools like llama.cpp, but compatibility gaps, driver issues, and fewer community resources remain real friction points. If you already own an AMD GPU (like the RX 7900 XTX with 24 GB VRAM), it is absolutely worth trying. If you are buying new hardware specifically for AI, NVIDIA offers a smoother experience.

Can I run a local AI model and use it across my home network?

Yes. Tools like Ollama expose a local API endpoint that any device on your network can access. You can run the model on a dedicated machine (a desktop, mini PC, or even a NAS) and send requests to it from your laptop, phone, or other devices. This is one of the best reasons to set up a dedicated local AI server — one machine serves your entire household. Just ensure the server is only accessible on your local network, not exposed to the public internet. A properly configured home router and network security setup is essential.

What is the best single purchase for someone just getting started?

If you already own a reasonably modern computer (made after 2020) with 16 GB of RAM, spend $0 — just install Ollama and try Llama 3.3 8B. If you want a dedicated device, the Beelink SER8 mini PC is a strong entry point for an always-on local AI server. If you want GPU-accelerated performance and have a desktop PC, a used RTX 3060 12 GB ($180–$220) is the cheapest way to get meaningfully faster inference. The best single purchase for someone serious about local AI is still the used RTX 3090 at around $700 — it punches far above its price point.

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Best Hardware for Running Local AI Models in 2026

Why Run AI Locally?

The Golden Rule: Buy Memory, Not Speed

How Quantization Stretches Your Hardware

GPU Comparison: The Hardware That Matters Most

Hardware Builds by Budget

Entry Level: $0–$300 (CPU-Only and Existing Hardware)

Mid-Range: $300–$1,000 (The Sweet Spot)

Prosumer: $1,000–$3,000 (70B Models and Beyond)

Workstation: $3,000+ (Brief Overview)

Open-Weight Models Worth Running Locally

Software: Getting Models Running

Pre-Built Options: Mini PCs for Local AI

Security and Privacy Considerations

What Not to Do

Recommended Shopping List by Budget

Frequently Asked Questions

Can I run AI models on a Mac?

How much VRAM do I need for a 70B parameter model?

Is it cheaper to run AI locally or use a cloud API?

What is quantization and does it affect quality?

Do I need an NVIDIA GPU or will AMD work?

Can I run a local AI model and use it across my home network?

What is the best single purchase for someone just getting started?

Leave a comment

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