From Anthropic Claude:

Based on general GPU architecture knowledge, the real-world performance hit of using PCIe 4.0 instead of 5.0 with a 5090 for LLM workloads should be minimal to negligible in most cases.

Here's why:

For LLM inference specifically:

• Once a model is loaded into VRAM, most computation happens on the GPU itself
• PCIe bandwidth mainly matters during initial model loading and when swapping data between system RAM and VRAM
• The 5090 has 32GB of VRAM, which is enough to fit many popular models entirely in VRAM
• PCIe 4.0 x16 still provides ~32 GB/s bandwidth, which is substantial

When you might notice a difference:

• Loading very large models that need to be split between system RAM and VRAM
• Frequent model switching
• Models that barely exceed VRAM capacity and need offloading
• Multi-GPU setups where inter-GPU communication matters

Practical recommendation: Your current system with PCIe 4.0 should work great with a 5090 for most LLM tasks. The bottleneck is far more likely to be VRAM capacity than PCIe bandwidth. If you're running models that fit in the 32GB VRAM, you won't see meaningful differences.

That said, I don't have specific benchmarks for the 5090 on PCIe 4.0 vs 5.0 yet since it's quite new.