Beyond the VM: Why vLLM and FlashAttention need Bare Metal GPUs 🚀

Dev.to / 4/8/2026

💬 OpinionDeveloper Stack & InfrastructureIdeas & Deep AnalysisTools & Practical Usage

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Key Points

The article argues that LLM inference frameworks like vLLM, TGI, and Triton are typically memory-bandwidth bound, so hardware virtualization can significantly harm performance.
It explains how cloud VM mechanisms—such as hypervisor memory ballooning—can interfere with PagedAttention, leading to OOM failures or major throughput degradation during continuous batching.
It contends that FlashAttention’s benefits (reducing HBM reads/writes through SRAM tiling) can be offset by driver-level overhead in virtualized GPU environments, making bare metal preferable.
The post emphasizes that large tensor-parallel deployments (e.g., 70B+ and beyond) often require fast interconnect bandwidth (NVLink 4.0 vs PCIe) to keep all-reduce operations efficient.
It advises production teams to use exclusive/bare-metal GPU access and provides guidance on required VRAM “floors” across model sizes (7B to 400B+).

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/]