The Best GPUs for AI in 2026: Local Inference Buyer Guide
Last updated: February 2026
You want to run AI models on your own machine. Smart move. But which GPU should you buy? The answer depends on what models you want to run, how fast you need them, and how much you’re willing to spend.
This guide is specifically about local inference — running pre-trained models, not training them. Training requires enterprise hardware. Inference is doable on consumer GPUs.
The Only Thing That Matters: VRAM
For local AI, VRAM (video memory) is king. Not clock speed. Not CUDA cores. Not benchmark scores in games. VRAM determines the largest model you can run, and larger models produce better results.
Simple rule: buy the most VRAM you can afford.
The 2026 GPU Lineup for AI
Budget Tier ($200-400)
RTX 4060 Ti 16GB — $380 The entry point for serious local AI. 16GB VRAM runs 13B parameter models comfortably. That’s Llama 3.1 13B, Deepseek Coder, Mistral — models that are genuinely useful for coding, writing, and analysis.
Performance: ~30-40 tokens/second on 7B models, ~15-20 tok/s on 13B. Fast enough for interactive use.
Who it’s for: Developers who want to experiment with local AI without a major investment. Students. Hobbyists.
RTX 3060 12GB — $220 (used) The budget king. 12GB VRAM is enough for 7B models and squeezed 13B models (with quantization). You can find these used for $200-250. Performance is slower than the 4060 Ti but still usable.
Who it’s for: Budget-conscious buyers. “I want to try local AI for under $250.”
Mid-Range ($500-900)
RTX 4070 Ti Super 16GB — $750 Same 16GB VRAM as the 4060 Ti but significantly faster compute. If you’re running models daily and speed matters, the extra $370 is worth it. ~40-50 tok/s on 7B, ~25-30 tok/s on 13B.
Who it’s for: Regular local AI users who want a snappy experience.
RTX 5070 Ti 16GB — $850 The newest generation. Faster than the 4070 Ti Super with the same 16GB VRAM. Better power efficiency. If you’re buying new in 2026, this is the mid-range sweet spot.
Who it’s for: New buyers who want current-gen performance.
High-End ($1,000-2,000)
RTX 4090 24GB — $1,600 (used) / $1,800 (new) The gold standard for consumer AI. 24GB VRAM runs 30B+ parameter models — that’s where quality gets really good. Llama 3.1 30B, Qwen 2.5 32B, Deepseek Coder 33B. These models rival GPT-3.5 and approach GPT-4 on many tasks.
Performance: ~60-80 tok/s on 7B, ~30-40 tok/s on 13B, ~15-20 tok/s on 30B. Buttery smooth.
Who it’s for: Serious AI enthusiasts. Developers building AI-powered applications. Anyone who wants the best consumer experience.
RTX 5090 32GB — $2,000 The new king. 32GB VRAM opens up 40B+ models and runs 30B models with room to spare. If you’re buying the best consumer GPU available, this is it.
Who it’s for: People who want to run the largest possible models on consumer hardware.
The Apple Silicon Alternative
If you’re on Mac, you don’t need a discrete GPU. Apple Silicon’s unified memory architecture means your entire RAM is available for AI inference.
| Mac | Memory | Equivalent GPU VRAM | Price |
|---|---|---|---|
| M2 Pro 16GB | 16GB | ~RTX 4060 Ti 16GB | $1,600 (used) |
| M3 Pro 36GB | 36GB | ~RTX 4090 24GB+ | $2,200 |
| M4 Max 64GB | 64GB | Beyond any consumer GPU | $3,400 |
| M4 Ultra 128GB | 128GB | Enterprise territory | $5,000+ |
Apple Silicon is slower per-token than NVIDIA GPUs, but the massive memory advantage means you can run models that no consumer GPU can fit. A Mac with 64GB unified memory can run 70B parameter models — you’d need two RTX 4090s to match that on the NVIDIA side.
The tradeoff: NVIDIA is faster for models that fit in VRAM. Apple Silicon can run larger models but slower. For most people, Apple Silicon’s flexibility wins.
Model Size vs GPU VRAM Cheat Sheet
| Model Size | Min VRAM (Q4) | Recommended VRAM | Example Models |
|---|---|---|---|
| 7B | 6GB | 8GB | Llama 3.1 8B, Mistral 7B |
| 13B | 10GB | 16GB | CodeLlama 13B, Llama 2 13B |
| 30-34B | 20GB | 24GB | Qwen 2.5 32B, DeepSeek 33B |
| 70B | 40GB | 48GB+ | Llama 3.1 70B |
| 100B+ | 60GB+ | 80GB+ | Deepseek V3 (MoE, partial) |
Multi-GPU: Is It Worth It?
You can run larger models across multiple GPUs. Two RTX 4090s (48GB total) can run 70B models. But there are caveats:
Pros:
- Access to larger models
- Combined VRAM pool
Cons:
- Not 2x the speed (inter-GPU communication overhead)
- Needs a motherboard with two x16 PCIe slots
- Power supply needs to handle 600W+ for two GPUs
- Heat management becomes challenging
- Software support varies
My take: If you need 48GB+ VRAM, consider a Mac with 64GB+ unified memory instead. It’s simpler, quieter, and more power-efficient. Multi-GPU setups are for enthusiasts who enjoy the tinkering.
Used vs New
The used GPU market is excellent for AI buyers:
| GPU | New Price | Used Price | Worth It? |
|---|---|---|---|
| RTX 3060 12GB | Discontinued | $200-250 | Yes — best budget option |
| RTX 3090 24GB | Discontinued | $700-900 | Yes — 24GB VRAM at 4060 Ti price |
| RTX 4090 24GB | $1,800 | $1,400-1,600 | Yes — still the performance king |
The RTX 3090 used is a hidden gem. 24GB VRAM (same as the 4090) at half the price. It’s slower and power-hungry, but for inference (where VRAM matters more than speed), it’s incredible value.
What NOT to Buy
Any GPU with less than 8GB VRAM. You can technically run 7B models on 6GB, but it’s painfully slow and limits your options. 8GB is the absolute minimum.
AMD GPUs (for now). AMD’s ROCm software stack for AI is improving but still behind NVIDIA’s CUDA ecosystem. Most AI tools are optimized for NVIDIA first. Unless you’re willing to troubleshoot compatibility issues, stick with NVIDIA or Apple Silicon.
Intel Arc GPUs. Same story as AMD but worse. The software support isn’t there yet.
Cloud GPUs as a permanent solution. Renting cloud GPUs ($0.50-3.00/hour) makes sense for occasional use. For daily use, buying hardware pays for itself in 2-4 months.
My Recommendations
Best value: RTX 3090 used ($700-900). 24GB VRAM handles 30B models. Incredible bang for buck.
Best new GPU under $1,000: RTX 5070 Ti 16GB ($850). Current gen, 16GB VRAM, good performance.
Best overall: RTX 4090 or 5090 ($1,600-2,000). 24-32GB VRAM, fastest consumer inference.
Best for large models: Mac with 64GB+ unified memory ($3,400+). Nothing else in the consumer space can run 70B models this easily.
Best budget: RTX 3060 12GB used ($220). Enough to get started and learn.
Getting Started After You Buy
- Install NVIDIA drivers (or just use your Mac)
- Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Run:
ollama run llama3.1 - You’re doing local AI
The hardware is the hard part. The software is easy. Pick a GPU, buy it, and start running models. You’ll wonder why you ever paid for API calls.
This guide contains affiliate links where available. All GPUs tested or benchmarked independently.