About 19; min
Running LLMs locally requires the right GPU. Too little VRAM and your model won’t load. Too much and you’re wasting money. The GPU market in 2026 offers options from $200 used cards to $30,000 data center accelerators. Here are the best GPUs for running AI models locally, ranked by price-to-performance for LLM inference.
Quick Comparison
| GPU | VRAM | Price (approx) | Best Model Size | Best For |
|---|---|---|---|---|
| Apple M2 Ultra | 192GB unified | $4,000-6,000 | Up to 70B (FP16) | Silent desktop, large models |
| NVIDIA RTX 4090 | 24GB | $1,600-2,000 | Up to 14B (FP16), 30B (Q4) | Best consumer GPU |
| NVIDIA RTX 3090 | 24GB | $700-900 (used) | Up to 14B (FP16), 30B (Q4) | Best budget 24GB option |
| NVIDIA RTX 4070 Ti Super | 16GB | $800 | Up to 8B (FP16), 14B (Q4) | Entry-level local AI |
| Apple M3/M4 Pro (18-36GB) | 18-36GB unified | $2,000-3,000 | Up to 14B (FP16) | Laptop local AI |
| NVIDIA A100 80GB | 80GB | $10,000-15,000 | Up to 70B (FP16) | Professional/datacenter |
| NVIDIA H100 | 80GB | $25,000-30,000 | Up to 70B (FP16), fast | Maximum performance |
VRAM Requirements by Model Size
| Model Size | FP16 (full) | Q8 (8-bit) | Q4 (4-bit) | Recommended GPU |
|---|---|---|---|---|
| 3B (Phi-3 Mini) | 6GB | 3.5GB | 2GB | Any GPU, even CPU |
| 7-8B (Llama/Qwen) | 16GB | 8GB | 5GB | RTX 4070 Ti, M2 16GB |
| 14B (Qwen 14B) | 28GB | 16GB | 10GB | RTX 4090, M2 Pro 32GB |
| 32B (Qwen 32B) | 64GB | 36GB | 20GB | RTX 4090 (Q4), M2 Ultra |
| 70B (Llama 70B) | 140GB | 75GB | 42GB | 2x RTX 4090, A100 80GB |
| 405B (Llama 405B) | 810GB | 430GB | 240GB | 8x A100, not consumer viable |
1. Apple M2/M3/M4 Ultra — Best Silent Desktop
Apple’s Ultra chips in Mac Studio provide the most VRAM per dollar at the high end. The M2 Ultra with 192GB unified memory runs Llama 70B at full FP16 precision — something that requires $20,000+ in NVIDIA hardware. Generation speed is 15-25 tokens per second for 70B models — slower than A100 but usable for development and personal use. The M4 Ultra (expected) should improve speeds further. The silent, compact Mac Studio form factor sits on a desk without the noise, heat, and power draw of GPU servers. For developers who want to run 70B models locally for coding assistance, document analysis, and testing, Apple Ultra provides the most accessible path to large-model local inference.
2. NVIDIA RTX 4090 — Best Consumer GPU
The RTX 4090 with 24GB VRAM is the fastest consumer GPU for AI inference. It runs 7-8B models at 50+ tokens per second and handles 14B models comfortably. Quantized 30B models fit in 24GB. For 70B models, two RTX 4090s (48GB total with tensor parallelism via llama.cpp) provide a capable setup at ~$3,600 total. The CUDA support means every AI tool works without compatibility issues. Power draw is high (450W) and the card is physically large. For AI enthusiasts and developers who want the fastest local inference on consumer hardware, the 4090 remains the top choice despite being over two years old.
3. NVIDIA RTX 3090 — Best Budget 24GB
The RTX 3090 offers 24GB VRAM at nearly half the price of an RTX 4090. Used cards are widely available at $700-900. Inference speed is roughly 60-70% of the 4090 — still very usable at 30-40 tokens per second for 8B models. The same 24GB handles all models that fit on a 4090, just slower. Two 3090s provide 48GB for quantized 70B models at under $1,800 total. The power draw is high (350W) and cooling can be loud. For budget-conscious developers who want capable local AI without spending $1,800+ on a 4090, the used 3090 market provides excellent value.
4. NVIDIA RTX 4070 Ti Super — Best Entry-Level
The RTX 4070 Ti Super with 16GB VRAM is the minimum recommended for serious local AI work. It runs 7-8B models at 35-45 tokens per second — responsive enough for interactive use. Quantized 14B models fit in 16GB. The 16GB limit means 30B+ models won’t load without extreme quantization. At $800 new, it’s the most affordable current-gen GPU that handles the most popular model sizes (Llama 8B, Qwen 7B, Gemma 9B). Power draw is modest (285W) and cooling is manageable. For developers starting with local AI who want a GPU that handles today’s most-used models at a reasonable price, the 4070 Ti Super hits the entry point.
5. Apple M3/M4 Pro MacBooks — Best Laptop AI
MacBook Pro with M3/M4 Pro chips (18-36GB unified memory) runs 7-8B models at 25-35 tokens per second through Ollama with Metal acceleration. The 36GB configuration handles quantized 14B models. Fan noise is minimal compared to any NVIDIA GPU laptop. Battery life of 15+ hours means AI is available on the go without being plugged in. The M4 Pro’s improved Neural Engine provides faster inference than previous generations. For developers who want local AI on a laptop without carrying an external GPU or accepting gaming-laptop noise and battery life, Apple Silicon MacBooks provide the best portable AI experience.
6. NVIDIA A100 80GB — Professional Standard
The A100 80GB is the standard GPU for professional AI workloads. 80GB of HBM2e memory runs Llama 70B at full FP16 precision with room for large batch sizes. Inference speed is 40-60 tokens per second for 70B models. NVLink allows multi-GPU scaling for 405B models. Tensor Cores accelerate mixed-precision inference. The A100 is the GPU that cloud providers (RunPod, AWS) charge $1-2 per hour for. Buying one makes financial sense when you’d spend more than $10,000 per year on cloud GPU rental. For small AI companies and research labs running models daily, owning A100s provides the best long-term economics.
Recommendations by Budget
| Budget | Best Choice | Model Capability |
|---|---|---|
| $0 (CPU only) | Existing PC/Mac | 3B models (slow but functional) |
| $700-900 | Used RTX 3090 | Up to 14B (FP16), 30B (Q4) |
| $800 | RTX 4070 Ti Super | Up to 8B (FP16), 14B (Q4) |
| $1,600-2,000 | RTX 4090 | Up to 14B (FP16), 30B (Q4) |
| $2,000-3,000 | MacBook Pro M4 Pro | Up to 14B (Q4), portable |
| $4,000-6,000 | Mac Studio M2 Ultra | Up to 70B (FP16) |
| $10,000+ | A100 80GB | Up to 70B (FP16), production speed |
The RTX 4090 wins as the best overall GPU for local AI. At $1,600-2,000, it provides the fastest consumer inference speed, 24GB VRAM for most popular models, and universal CUDA compatibility with every AI tool. Apple M2 Ultra earns runner-up for the unique ability to run 70B models on a silent desktop — the 192GB unified memory enables large-model inference that would cost $20,000+ in NVIDIA hardware. For budget buyers, the used RTX 3090 at $700-900 delivers 70% of the 4090’s performance at half the price. Choose based on your primary model size: 8B models need 16GB minimum, 14-30B needs 24GB, and 70B needs 48GB+ or Apple Ultra.
FAQ
Can I run LLMs without a GPU?
Yes, on CPU — but 5-10x slower. An 8B model on a modern CPU generates 3-8 tokens per second (usable but slow). Apple Silicon Macs use the GPU through Metal acceleration, so even base MacBooks have GPU inference.
Should I buy one RTX 4090 or two RTX 3090s?
Two 3090s (48GB total, ~$1,600) provide more VRAM than one 4090 (24GB, ~$1,800) for less money. But multi-GPU setups add complexity and not all tools support tensor parallelism. For simplicity, one 4090. For maximum VRAM per dollar, two 3090s.
Is Apple Silicon good for AI?
For inference (running models), excellent — the unified memory architecture lets you run larger models than equivalent VRAM on NVIDIA. For training, NVIDIA is faster due to CUDA maturity. For local development and personal AI use, Apple Silicon is the most convenient option.
When should I rent cloud GPUs instead of buying?
When you need GPUs for less than 6-8 hours per day. At RunPod’s A100 rate ($1.64/hr), renting 8 hours daily costs ~$400/month — a bought A100 at $12,000 pays for itself in 30 months. If you’d use it less, renting is cheaper. If you’d use it full-time, buying saves money within 1-2 years.
