The AI Compute Bottleneck: Why Bare Metal GPUs Crush Cloud VMs

In the world of decentralized tech, compute is the new oil. But for AI and Large Language Models, where that compute lives matters immensely.

At Leo Servers, we deploy high-end bare metal infrastructure, and we've mapped out exactly why traditional cloud VMs fail hard under the weight of AI workloads.

The Math Behind the Bottleneck
Over 80% of LLM FLOPs are dense matrix multiplications. But inference is overwhelmingly memory-bandwidth bound, not compute bound.

VRAM Walls: A Llama 3 70B model requires ~140GB of parameters loaded into memory just to generate one token. Cloud virtualization (MIG) slices up VRAM, destroying throughput.

The Hypervisor Tax: Cloud VMs add a 5-15% CPU overhead. When you are running custom CUDA kernels like FlashAttention, this virtualization strips away the hardware-level speed you are paying for.

TCO (Total Cost of Ownership): High-utilization AI pipelines running on the cloud burn through OpEx fast. Bare metal fixes your costs and eliminates data gravity egress fees, saving an average of 40%.

If you're building heavy AI infrastructure, you need direct access to the metal.

For more details, read more and visit the blog link: [https://www.leoservers.com/blogs/category/why/llms-require-bare-metal-gpus/]