Summary of Reddit Comments on the Best GPU for Local Inference

NVIDIA GPUs

• RTX 3090:
  • Pros:
    • Good performance for a variety of models like LLaMA, Mistral, and LM Studio.
    • Cost-effective option for AI computation.
  • Cons:
    • Limited availability and higher power consumption.
• RTX 4090:
  • Pros:
    • Faster inference on certain models compared to RTX 3090.
  • Cons:
    • Expensive and not a significant upgrade over RTX 3090 for some metrics.
• RTX 5090:
  • Pros:
    • Large VRAM capacity and potentially good performance.
  • Cons:
    • Expensive unless obtained at MSRP.
• RTX 4070:
  • Pros:
    • Small form factor with good GPU performance.
  • Cons:
    • Limited GPU selection and potential noise from power supply.

AMD GPUs

• Ryzen 395:
  • Pros:
    • Potential performance benefit.
  • Cons:
    • Uncertain impact in certain use cases.

Other Considerations

• CPUs like Intel Ultra 185HS or AMD HX370 may affect performance in EGPU setups.
• Be cautious of NPU benefits being neglected in some AI computations.
• Framework Desktop or SFF PCs like Fractal Ridge with suitable GPUs can offer performance and flexibility.
• Consider the anticipated use case, budget, and upcoming product announcements before making a purchase.
• Efficient LLM and diffusion models are crucial for AI scalability and resource efficiency.
• Focus on optimization and efficiency in AI computations to address environmental impact and scalability challenges.

Overall, the choice of the best GPU for local inference depends on the specific use case, budget, anticipated performance requirements, and future scalability considerations.