Hello, builders! 👋 If you're working on LLM inference using frameworks like vLLM, TGI, or Triton, you already know that inference is memory-bandwidth bound, not compute bound.
We just published a massive technical breakdown on the Leo Servers blog detailing why standard cloud VMs actively sabotage transformer attention mechanisms.
Technical highlights from the post:
Continuous Batching Jitter: How cloud hypervisor memory ballooning directly interferes with PagedAttention, causing catastrophic OOM errors or throughput degradation.
Kernel-Level Bottlenecks: FlashAttention minimizes HBM reads/writes by tiling compute within SRAM. Virtualized GPU environments introduce driver-level overhead that negates these gains. Bare metal preserves it.
NVLink vs. PCIe: Why tensor parallelism for 70B+ models absolutely requires the 900 GB/s bidirectional bandwidth of NVLink 4.0, and why cloud network abstraction slows down all-reduce operations.
If you're deploying in production, you need exclusive hardware access. We break down the exact VRAM floors for models (7B to 400B+) and how to choose the right cluster.
For more details, read more and visit the blog link: [https://www.leoservers.com/blogs/category/why/llms-require-bare-metal-gpus/]
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