RTX 4090 for AI: The Ultimate Homelab GPU

The RTX 4090 offers an exceptional combination of 24GB VRAM, Ada Lovelace architecture, and consumer pricing (~$1,600-2,000). It handles workloads that previously required datacenter hardware.

Key specs: 16,384 CUDA cores, 24GB GDDR6X @ 1TB/s bandwidth, 450W TDP, 82.6 TFLOPS FP32.

Inference: 70B models at Q4 quantization, 34B at Q8, 13B at FP16.

Fine-tuning: 7B-13B models with QLoRA, 7B full fine-tune with gradient checkpointing.

Training: Small models from scratch, LoRA adapters for larger models.

Image generation: SDXL, Flux at full resolution with fast generation times.

The 450W TDP demands serious cooling. Stock coolers work but run hot and loud.

Aftermarket options: Deshroud and add Noctua fans, or use AIO liquid cooling.

Case airflow: Ensure strong front-to-back airflow. Mesh front panels help.

Target temps: Keep under 80°C for longevity. Thermal throttling starts at 83°C.

Minimum PSU: 850W for single card, 1200W+ for dual cards.

Use quality cables: 12VHPWR connector or 3x8-pin adapters. Avoid daisy-chaining.

Power limiting: nvidia-smi -pl 350 reduces power with ~10% performance loss.

Undervolting: Can reduce power 20-30% with minimal performance impact.

Install latest CUDA toolkit and cuDNN for best performance.

Use Flash Attention 2 for transformer inference.

Enable TF32 for training: torch.backends.cuda.matmul.allow_tf32 = True

Consider bitsandbytes for 8-bit optimizers during fine-tuning.

Use vLLM or TGI for production inference serving.