Beyond GPUs - Part 2 Multi-Cloud, Multi-Compute in Practice: What Developers Actually Face

This is Part 2 of our series on multi-cloud, multi-compute infrastructure. Part 1 explored why this matters for AI founders. This post dives into the technical realities and how FlexAI makes it manageable.

Running workloads across mixed clouds and hardware sounds simple in theory. In reality, it's a maze of runtimes, drivers, and scheduling quirks that only show up once you hit scale. For developers building AI-native products, this is where multi-cloud, multi-compute stops being an abstract idea and starts eating engineering time.

Ask anyone who has tried to deploy the same model on NVIDIA and AMD accelerators: the runtime never behaves exactly the same.

The problems are subtle but costly:‍

• Kernels that compile fine on CUDA may fail silently on ROCm
• OneAPI or XPU promise portability, but diverge in memory handling and ABI expectations
• Even small version mismatches (CUDA, cuDNN, NCCL) can cause hours of debugging or subtle performance drops

Containerization helps, but it's not a magic fix. Driver bindings and environment variables still leak through. Many teams only discover these problems in production, when a workload runs 30% slower than expected.

At FlexAI, we often run newer CUDA versions than PyTorch officially supports. That means aligning cuDNN, cuBLAS, NCCL, and system drivers with surgical precision. These components are not modular—they must be tuned together or things break. Sometimes silently, only appearing in production.

Scheduling Complexity: Beyond "Pick a GPU"

In homogeneous clusters, scheduling is simple: put the next job on the next available GPU. But in mixed hardware environments, scheduling becomes one of the hardest problems to get right.

Common failure modes we see

• Wrong kernel, wrong place: An inference job lands on hardware missing key CUDA ops, leading to degraded performance
• Throughput mismatch: A latency-sensitive job ends up on a high-throughput cluster, wasting capacity and inflating costs
• Version traps: Training pipelines that silently assume a specific CUDA build fail outright when scheduled onto alternatives

At FlexAI, we've built tooling far beyond default Kubernetes behavior. Our scheduler evaluates:

• Which CUDA or ROCm version is running
• Whether interconnects are supported by the driver
• Whether a workload prioritizes fast startup or sustained throughput

The "Invisible Complexity" Problem

The hardest part of heterogeneous compute is that many issues stay hidden until you push a system into real-world scale. Everything might look fine in testing, but once the workload ramps up, the cracks begin to show.

A kernel that passes unit tests may suddenly fail when distributed across multiple nodes. A library you carefully pinned to a specific version can still behave differently between container builds, leaving you chasing down inconsistencies that aren't documented anywhere.

Even the interconnects—those invisible highways between accelerators—can vary by driver, creating bottlenecks that only reveal themselves as latency spikes under load.

These aren't obscure corner cases. They're the kinds of frustrating, time-consuming problems that developers run into every day while trying to move fast.

Two Philosophies: Control vs. Simplicity

During PyTorch Day at Station F in Paris, vLLM's entry into the Linux Foundation highlighted something important: the ecosystem is splitting into two philosophies.

PyTorch remains tightly integrated with CUDA, shaped by a deeply NVIDIA-centric ecosystem. vLLM, by contrast, is designed with hardware diversity in mind—it already supports AMD, Google TPUs, AWS Trainium and Inferentia, and other emerging accelerators.

This reflects two different mindsets: One built on NVIDIA dominance.  The other recognizing a growing need for flexibility, openness, and efficiency in real-world systems.

Instead of forcing developers to master every runtime and backend, we abstract the complexity so they can focus on what actually matters: deploying models, iterating faster, and shipping products.

Looking Forward: Training vs. Inference Trajectories

Training will remain NVIDIA-first in the near term, but alternatives are gaining ground. If AMD's MI350 lives up to its claims and AWS Trainium continues showing strength in cloud environments, the balance may shift. Compiler ecosystems will play a major role, but they need to close the experience gap quickly.

Inference diversification is well underway. Cost, latency, and energy constraints are pushing deployments to CPUs, TPUs, mobile accelerators, and custom silicon. Apple, Qualcomm, and Google are all driving this forward.

Software remains the bottleneck. This is where FlexAI's schedulers and runtime orchestration systems become critical as the hardware stack fragments.

The Practical Path Forward

Heterogeneous computing isn't a trend—it's a response to real constraints. Diverse workloads demand adaptable infrastructure. The hard part isn't adopting the idea; it's managing the complexity it creates.

At FlexAI, we're not chasing universality. We're building systems that adapt, fail predictably, and scale intentionally across fractured hardware landscapes. We believe the future of AI infrastructure follows Cloud Native principles: observable, composable, resilient, and portable across architectures.

For founders building AI-native companies, the message is clear: plan for multi-cloud, multi-compute early, but don't try to solve it alone. Partner with infrastructure providers who can abstract the complexity, so your team can focus on building products that matter.