The Memory Bandwidth Gap Is 49x and Growing — Why Local LLMs Hit a Ceiling

The Wall I Hit on an RTX 4060 Was a Bandwidth Wall Running Qwen3.5-9B on an RTX 4060 8GB gets you about 40 tok/s. Perfectly usable for a reasoning model. But scale up the model size and the numbers crater. 27B drops to 15 tok/s. 32B at Q4 quantization barely holds 10 tok/s. The bottleneck isn't GPU compute. It's memory bandwidth . LLM inference — especially the token generation phase — is rate-limited by how fast model weights can be read out of VRAM. The RTX 4060's GDDR6 bandwidth is 272 GB/s. A 4.1GB model can theoretically be read 66 times per second, but a 9GB model only 30 times, and 18GB only 15 times. Real-world numbers beat theoretical thanks to caching effects, but the fundamental structure — bandwidth sets the ceiling — doesn't change. The real problem is that this ceiling is moving at completely different speeds for datacenters and consumers. Datacenter Side: The HBM3→HBM3E→HBM4 Bandwidth Explosion Here's the datacenter GPU memory bandwidth progression. [HBM Memory Bandwidth