Local AI Hardware Database • Updated for 2026 Models

Laptop & GPU Database: Best Local AI Models

Benchmark data, VRAM limits, memory bandwidth calculations, and optimal model recommendations for every popular laptop GPU, desktop card, and Apple Silicon Mac.

RTX 3060 Laptop (6GB) RTX 4060 Laptop (8GB) RTX 4070 Laptop (8GB) RTX 4080 Laptop (12GB) RTX 4090 24GB (Flagship) RTX 3060 12GB (Budget King) Apple Silicon Unified Memory

🎯 Dedicated GPU & Laptop Sizing Guides

Detailed benchmarks • TGP limits • Ollama setups

RTX 3060 Laptop 6 GB VRAM

Bus: 192-bit • Bandwidth: 336 GB/s

The high-volume mobile workhorse. Learns how to maximize 6GB VRAM, run 7B coders with tight context, and leverage CPU offloading for 14B models.

Top Recommended Models:

DeepSeek R1 1.5B (110 tok/s) • Llama 3.2 3B (85 tok/s) • Qwen 2.5 Coder 7B (45 tok/s)

View RTX 3060 Laptop Guide →

RTX 4060 Laptop 8 GB VRAM

Bus: 128-bit • Bandwidth: 256 GB/s

The mainstream standard for modern gaming laptops. Ada Lovelace 32MB L2 cache enables fast 55 tok/s inference on 7B-8B coding and reasoning models.

Top Recommended Models:

Qwen 2.5 Coder 7B (55 tok/s) • DeepSeek R1 7B (50 tok/s) • Llama 3.1 8B (45 tok/s)

View RTX 4060 Laptop Guide →

RTX 4070 Laptop 8 GB VRAM

Bus: 128-bit • Bandwidth: 256 GB/s

Addresses the popular engineering query: "Can RTX 4070 run Qwen?". Complete breakdown of 7B vs 14B vs 32B models on 8GB Ada Lovelace mobile.

Top Recommended Models:

Qwen 2.5 Coder 7B (62 tok/s) • Qwen 14B Q3_K_M (22 tok/s) • Phi-4 14B

View RTX 4070 Laptop Guide →

RTX 4080 Laptop 12 GB VRAM

Bus: 192-bit • Bandwidth: 432 GB/s

The mobile sweet spot. With 12GB GDDR6 on a 192-bit bus, this laptop runs 14B frontier models (DeepSeek R1 14B and Qwen 14B) 100% in VRAM at 40+ tok/s.

Top Recommended Models:

DeepSeek R1 14B (38 tok/s) • Qwen 2.5 Coder 14B (42 tok/s) • Mistral Small 24B

View RTX 4080 Laptop Guide →

RTX 4090 24GB 24 GB VRAM

Bus: 384-bit • Bandwidth: 1,008 GB/s

The undisputed desktop workstation champion. Runs 32B coding powerhouses and DeepSeek R1 32B with zero latency, plus quantized 70B models.

Top Recommended Models:

DeepSeek R1 32B (35 tok/s) • Qwen 2.5 Coder 32B (38 tok/s) • Llama 3.3 70B (Q2_K)

View RTX 4090 24GB Guide →

RTX 3060 12GB (Desktop) 12 GB VRAM

Bus: 192-bit • Price: ~$280 (Budget King)

The most cost-effective AI card in the world. 12GB VRAM allows 100% offload for 14B models. Outperforms modern 8GB GPUs for AI workloads.

Top Recommended Models:

DeepSeek R1 14B (30 tok/s) • Qwen 2.5 Coder 14B (32 tok/s) • Phi-4 14B

View RTX 3060 12GB Guide →

Apple Silicon Mac 16GB – 192GB UMA

Bus: Up to 800-bit • Bandwidth: Up to 800 GB/s

Unified Memory Architecture (UMA) allows Apple Mac Studio & MacBook Pro to run dense 70B models and 671B MoE locally without multi-GPU clusters.

Top Recommended Models:

Llama 3.3 70B (20 tok/s) • DeepSeek R1 70B • DeepSeek R1 671B MoE (IQ1_S)

View Apple Silicon Guide →

How Memory Bandwidth and VRAM Determine Local AI Performance

When running large language models locally, understanding your hardware architecture is vital. Unlike 3D rendering or rasterized video gaming where raw compute (TFLOPS) dictates framerates, autoregressive token generation is almost exclusively bound by memory bandwidth.

In standard transformer inference, generating each token requires reading the entire neural network weight matrix from VRAM into the GPU's registers and cache. As a rule of thumb:

Max Token Speed (tok/s) ≈ Memory Bandwidth (GB/s) ÷ Model File Size (GB) × Efficiency Factor (0.60–0.75)

This explains why an RTX 3060 12GB (360 GB/s bandwidth) can generate a 9GB 14B model at ~30 tok/s, whereas an RTX 4090 24GB (1,008 GB/s bandwidth) screams at over 38 tok/s on heavy 32B models!