Hardware Guide for Local AI

Understand what hardware you need, realistic costs, and what AI models you can run on different setups. Updated with current GPU prices, RTX 5090 guidance, and newer open-weight model recommendations.

Last updated: Apr 22, 2026

Quick Summary - April 2026 Prices

$0 (Existing)

Use your current computer for basic AI. CPU-only, 8GB RAM minimum.

Models: 1-3B

$280-450

RTX 3060/4060 Ti or RX 7600. Run medium models (8B) and basic image gen.

Models: 7-8B

$500-950

RTX 4070/4060 Ti 16GB or RX 7900 GRE. Run large models (32B) and high-quality image gen.

Models: 14-32B

$1,000-2,000+

RTX 4090 24GB, RTX 5090 32GB, or RX 7900 XTX. Run huge models (70B+) and professional image generation.

Models: 70B+

Cloud GPU Providers

Don't have hardware? Rent GPUs by the hour instead. Use these referral links for bonuses.

RunPod

Container-based GPU cloud with auto-scaling. Great for fine-tuning and training.

Get $5 extra when you add $10

Referral Link

Vast.ai

Decentralized GPU marketplace with competitive pricing. Choose from thousands of instances.

Get $5 credit with referral

Referral Link

Thunder Compute

Budget-focused GPU cloud with 80% lower costs than hyperscalers. Optimized for AI.

Visit Thunder Compute

Referral Link

NVIDIA GPU Recommendations

NVIDIA GPUs are the best-supported option for local AI. CUDA is the standard for most AI tools.

Budget GPUs

GPU	Price	VRAM	Performance	Notes	Where to Buy
RTX 3060 12GB	$290-320	12GB	Good	Best value for 12GB VRAM, great for 7-8B models	Amazon Newegg Best Buy
RTX 4060 8GB	$280-300	8GB	Good	Newer architecture, limited by 8GB VRAM	Amazon Newegg
RTX 2060 Super 8GB	$200-230	8GB	Fair	Used option, limited for large models	eBay Amazon Used

Mid-Range GPUs

GPU	Price	VRAM	Performance	Notes	Where to Buy
RTX 4060 Ti 16GB	$400-450	16GB	Very Good	Great value, 16GB for 13-14B models	Amazon Newegg Micro Center
RTX 4070 12GB	$550-600	12GB	Excellent	Fast, but 12GB limits large models	Amazon Newegg Best Buy
RTX 3060 Ti 8GB	$280-320	8GB	Good	Faster than 3060, limited VRAM	Amazon Newegg

High-End GPUs

GPU	Price	VRAM	Performance	Notes	Where to Buy
RTX 4080 Super 16GB	$950-1,050	16GB	Excellent	Great performance, 16GB VRAM limit	Amazon Newegg Best Buy
RTX 4070 Ti Super 16GB	$750-800	16GB	Excellent	Great balance, 16GB VRAM	Amazon Newegg Micro Center
RTX 4070 Ti 12GB	$700-750	12GB	Excellent	Great for 8B models, limited for larger	Amazon Newegg

Ultra High-End GPUs

GPU	Price	VRAM	Performance	Notes	Where to Buy
RTX 4090 24GB	$1,700-2,000	24GB	Best	Still great for 70B-class local inference	Amazon Newegg Micro Center
RTX 5090 32GB	$2,000-2,500+	32GB	Best	Best single-GPU local AI option today	Amazon Newegg Micro Center
RTX 4090 D 24GB	$4,000-5,000	24GB	Best	Enterprise version, water-cooled	Amazon Newegg

AMD GPU & Accelerator Recommendations

AMD GPUs offer great VRAM for the price. ROCm support is improving but some tools are CUDA-only. New AMD AI MAX accelerators compete with NVIDIA for enterprise workloads.

Budget AMD

GPU/Accelerator	Price	VRAM	Performance	Notes
RX 7600 16GB	$280	16GB	Good	Best value for VRAM, great for 7-8B models
RX 6750 GRE 12GB	$240	12GB	Good	Great for LLMs and image gen
RX 6700 XT 12GB	$320	12GB	Very Good	Strong performer, good value

Mid-Range AMD

GPU/Accelerator	Price	VRAM	Performance	Notes
RX 7900 GRE 16GB	$550	16GB	Excellent	Great for 14-32B models
RX 7700 XT 12GB	$450	12GB	Very Good	Good performance, 12GB limits model size
RX 7800 XT 16GB	$500	16GB	Excellent	Great all-rounder for AI

High-End AMD

GPU/Accelerator	Price	VRAM	Performance	Notes
RX 7900 XTX 24GB	$950	24GB	Excellent	Best AMD for consumer, 24GB VRAM
RX 7900 XT 20GB	$800	20GB	Excellent	Great for large models

AMD AI Accelerators

GPU/Accelerator	Price	VRAM	Performance	Notes
AMD AI MAX 300	$2,000-3,000	128GB HBM3	Excellent	For enterprise AI workloads
AMD AI MAX 395	$5,000-7,000	192GB HBM3	Excellent	Top-tier AMD AI accelerator
MI300X	$12,000-15,000	192GB HBM3	Excellent	Enterprise/workstation GPU

Hardware Tiers & Model Recommendations

Minimal Setup

Use your current computer for basic AI tasks

$0 (Existing)

Specifications

CPU:Any modern CPU (Intel i5+/AMD Ryzen 5+)

RAM:8GB RAM (16GB recommended)

GPU:None required (CPU only)

Storage:10GB free space

Recommended LLMs

Model	Quantization	Size	Speed	Notes
Llama 3.2 3B	Q4_K_M	2.3GB	Slow (CPU)	Good for simple chat
Phi-4 Mini 3.8B	Q4_K_M	2.6GB	Decent (CPU)	Surprisingly capable for size
Qwen 3.5 0.5B/3B	Q8_0	0.6-2.0GB	Fast (CPU)	Great for coding and writing
Gemma 2 2B	Q4_K_M	1.5GB	Decent (CPU)	Good general purpose model

Image Generation Models

Model	Quantization	Size	Speed	Notes
SDXL Turbo	FP16	6.9GB	Very Slow	Low resolution only

Best For

Simple chat

Basic writing

Learning AI

Testing prompts

Limitations

Slow generation
Limited model access
Cannot run advanced image gen
CPU bottlenecks

Recommended Tools

GPT4All

LM Studio (light mode)

Ollama (CPU-only)

Budget GPU Setup

Affordable GPU for solid AI performance

$250-450

Specifications

CPU:Intel Core i5+/AMD Ryzen 5+

RAM:16GB RAM (32GB recommended)

GPU:RTX 3060 12GB / RTX 4060 8GB / RX 7600 16GB

Storage:50GB+ NVMe SSD

Recommended LLMs

Model	Quantization	Size	Speed	Notes
Llama 4 Scout 8B	Q4_K_M	5.0GB	Good (20-30 t/s)	Great balance of performance
DeepSeek R1 Distill Qwen 7B	Q4_K_M	4.3GB	Good (25-35 t/s)	Excellent reasoning
Qwen 3.5 7B	Q4_K_M	4.6GB	Good (25-35 t/s)	Strong coder and writer
Mixtral 8x7B (MoE)	Q4_K_M	25GB	Slow (needs 16GB+ VRAM)	Only on 16GB VRAM cards

Image Generation Models

Model	Quantization	Size	Speed	Notes
SDXL 1.0	FP16	6.9GB	Decent (15-20s)	Good quality 1024x1024
FLUX.1 Schnell	FP8/INT8	12GB	Decent (8-12s)	Great for quick iterations
Stable Diffusion 3.5	FP16	10GB	Fast (5-8s)	Excellent quality

Best For

Daily chat

Coding assistance

Image generation

Document analysis

Limitations

Large models still slow
Limited batch processing
VRAM limits model size

Recommended Tools

Ollama

LM Studio

Stable Diffusion WebUI

ComfyUI

Mid-Range GPU Setup

Balanced setup for serious AI work

$500-900

Specifications

CPU:Intel Core i7+/AMD Ryzen 7+

RAM:32GB RAM

GPU:RTX 4070 12GB / RTX 4060 Ti 16GB / RX 7900 GRE 16GB

Storage:100GB+ NVMe SSD

Recommended LLMs

Model	Quantization	Size	Speed	Notes
Llama 4 Scout 8B	Q6_K or Q8_0	7.2GB	Excellent (40-50 t/s)	Fast and high quality
Llama 4 Maverick 17B	Q4_K_M	10GB	Good (20-30 t/s)	Fits in 16GB VRAM
DeepSeek V3.2 32B	Q4_K_M	20GB	Good (20-25 t/s)	Great for complex tasks
Qwen 3.5 14B	Q4_K_M	9GB	Excellent (35-45 t/s)	Very capable open model

Image Generation Models

Model	Quantization	Size	Speed	Notes
SDXL 1.0	FP16	6.9GB	Fast (8-12s)	High quality
FLUX.1 Dev	FP8	24GB	Slow (20-30s)	Top tier quality
Stable Diffusion 3.5	FP16	10GB	Fast (10-15s)	Latest model

Best For

Production work

Multiple users

Advanced image gen

Training small models

Limitations

Very large models (70B+) still optimized
CPU offload slows 70B models

Recommended Tools

Ollama

LM Studio

ComfyUI

Stable Diffusion WebUI

High-End GPU Setup

Professional-grade AI workstation

$1,000-2,500

Specifications

CPU:Intel Core i9+/AMD Ryzen 9+

RAM:64GB RAM

GPU:RTX 4080 Super 16GB / RTX 4090 24GB / RTX 5090 32GB / RX 7900 XTX 24GB

Storage:500GB+ NVMe SSD

Recommended LLMs

Model	Quantization	Size	Speed	Notes
Llama 4 Maverick 17B	Q4_K_M	10GB	Excellent (50-60 t/s)	Best performance locally
DeepSeek V3.2 67B	Q4_K_M	40GB	Excellent (25-35 t/s)	Top tier reasoning
Mixtral 8x22B	Q4_K_M	94GB	Good (15-20 t/s)	Needs CPU offload
Qwen 3.5 32B	Q4_K_M	19GB	Excellent (40-50 t/s)	Great all-rounder

Image Generation Models

Model	Quantization	Size	Speed	Notes
FLUX.1 Dev	FP16	24GB	Excellent (5-8s)	Best quality
SD3.5 Large	FP16	10GB	Excellent (6-10s)	Fast and high quality
Stable Diffusion 3.5	FP16	10GB	Excellent (6-10s)	Latest with great quality

Best For

Professional work

Enterprise AI

Model fine-tuning

Multiple concurrent users

Limitations

Huge models (400B+) still need cloud
Power consumption

Recommended Tools

Ollama

ComfyUI

Stable Diffusion WebUI

Automatic1111

Professional Workstation

Multiple GPUs or accelerators for advanced AI research

$4,000-15,000+

Specifications

CPU:AMD Threadripper / Intel Xeon

RAM:128GB-256GB RAM

GPU:2-4x RTX 4090/5090 / AMD AI MAX 300/395 / A100 80GB / H100 80GB

Storage:1TB+ NVMe SSD

Recommended LLMs

Model	Quantization	Size	Speed	Notes
DeepSeek V3.2 67B	Q6_K or Q8_0	50-70GB	Excellent (50+ t/s)	Multiple concurrent users
Llama 4 Maverick 17B (full)	BF16	35GB	Excellent (60+ t/s)	Full precision frontier model
Qwen 3.6 72B	Q6_K	56GB	Excellent (40-50 t/s)	Great for production
Mixtral 8x22B	Q6_K	140GB	Excellent (30-40 t/s)	Great for MoE

Image Generation Models

Model	Quantization	Size	Speed	Notes
FLUX.1 Pro	FP32	48GB	Excellent (3-5s)	Best quality available
SD3 5B Large	FP16	10GB	Excellent (5-8s)	Fast with great quality
Custom Training	FP16	Variable	Variable	Train your own models

Best For

AI research

Model training

Enterprise deployment

Service provider

Limitations

Very expensive
Requires expertise
High power consumption

Recommended Tools

Ollama (multi-GPU)

ComfyUI (multi-GPU)

PyTorch/TensorFlow

vLLM

Apple Silicon (M1/M2/M3/M4)

Apple's M-series chips have excellent AI performance due to Unified Memory. No separate GPU required. Perfect for privacy and portable AI workstations.

Apple Silicon is unique because CPU and GPU share the same memory pool. This means you're not limited by GPU VRAM - you can use your full system RAM for AI models. However, you cannot upgrade the memory later.

M1/M2 (8GB Unified Memory)

$700-1,000 (used)

Specifications:

ChipM1 or M2

Unified Memory8GB

CPU Cores8 (4 performance + 4 efficiency)

GPU Cores7-8 GPU cores

Recommended Models:

Phi-4 Mini 3.8B

Q4_K_M · 2.6GB · Good (20-25 t/s)

Llama 3.2 1B/3B

Q4_K_M · 0.8-2.3GB · Good (20-25 t/s)

Gemma 2 2B

Q4_K_M · 1.5GB · Good (20-25 t/s)

Limitations:

Cannot run large models
Limited multitasking
8GB constrains model size

M1/M2/M3 (16GB Unified Memory)

$1,200-1,800

Specifications:

ChipM1 Pro/Max, M2 Pro/Max, or M3

Unified Memory16GB

CPU Cores10-12

GPU Cores16-19 GPU cores

Recommended Models:

Llama 4 Scout 8B

Q4_K_M · 5.0GB · Excellent (30-40 t/s)

Qwen 3.5 7B

Q4_K_M · 4.6GB · Excellent (30-40 t/s)

DeepSeek R1 Distill Qwen 7B

Q4_K_M · 4.3GB · Excellent (30-40 t/s)

Limitations:

70B models require offload
No CUDA for some tools
Limited to 16GB models

M2 Max/M3 Max (24GB Unified Memory)

$2,000-2,500

Specifications:

ChipM2 Max or M3 Max

Unified Memory24GB

CPU Cores12-16

GPU Cores30-40 GPU cores

Recommended Models:

Llama 4 Maverick 17B

Q4_K_M · 10GB · Decent (10-15 t/s)

DeepSeek V3.2 32B

Q4_K_M · 20GB · Good (20-25 t/s)

Qwen 3.5 14B

Q4_K_M · 9GB · Excellent (40-50 t/s)

Limitations:

70B+ still slow
No CUDA (some tools limited)
No GPU upgrades

M2 Ultra/M3 Max (36GB+ Unified Memory)

$3,500-7,000+

Specifications:

ChipM2 Ultra or M3 Max

Unified Memory36GB-128GB

CPU Cores24-32

GPU Cores60-80 GPU cores

Recommended Models:

Llama 4 Maverick 17B

Q4_K_M · 10GB · Good (20-25 t/s)

Llama 4 Maverick 17B

Q6_K · 15GB · Good (20-25 t/s)

DeepSeek V3.2 67B

Q4_K_M · 40GB · Good (20-25 t/s)

Limitations:

No CUDA (limited tool support)
Very expensive
Cannot upgrade GPU

Apple Silicon Advantages

Great for privacy - runs entirely on device with no internet

Easy to use - no complex GPU setup required

Recommended tools: Ollama, LM Studio, Jan (all have native Apple Silicon support)

Minimum Hardware Requirements by Model

The minimum hardware needed to run popular AI models locally. These are minimums - more is always better.

Model	Min RAM	Min VRAM	Recommended GPU	Notes
Llama 3.2 1B	4GB	None (CPU)	Any	Runs on CPU, GPU not needed
Llama 3.2 3B	6GB	None (CPU)	Any	CPU is fine, GPU helps speed
Llama 4 Scout 8B	8GB	8GB	RTX 3060 / RX 7600	8GB VRAM minimum for Q4 quantization
Llama 4 Maverick 17B	16GB	16GB	RTX 4060 Ti 16GB / RTX 4090 / 48GB system	16GB VRAM for Q4, or 24GB for full context
DeepSeek V3.2 32B	16GB	16GB	RTX 4070 Ti / RX 7900 GRE	16GB VRAM for Q4 quantization
DeepSeek V3.2 67B	32GB	24GB	RTX 4090 / RTX 5090 / 48GB+ Apple	24GB+ VRAM or 64GB RAM with offload
Qwen 3.5 32B	16GB	16GB	RTX 4070 Ti / RX 7900 XTX	16GB VRAM for Q4 quantization
Qwen 3.6 72B	32GB	24GB	RTX 4090 / RTX 5090 / 64GB+ Apple	24GB+ VRAM or 96GB RAM with offload
FLUX.1 Dev	24GB	24GB	RTX 4090 / 36GB+ Apple	24GB VRAM required
SDXL 1.0	8GB	8GB	RTX 3060 / RX 6750	8GB VRAM minimum
Stable Diffusion 3.5	12GB	12GB	RTX 4070 / RX 7800 XT	12GB VRAM recommended

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