Alex Medick

Discover why TensorWave’s MI325X cloud beats NVIDIA-based platforms with 256GB VRAM, ROCm stack, and unmatched AI training performance.

The Best AMD Cloud for AI Training: How TensorWave Outperforms NVIDIA-Based Platforms

When it comes to building and scaling AI infrastructure, most companies default to NVIDIA. But that default is getting expensive, restrictive, and in many cases, outdated.

Enter AMD’s Instinct™ MI325X and a new generation of cloud platforms purpose-built to take advantage of its performance.

At TensorWave, we’ve architected a platform from the ground up to unlock every ounce of capability from AMD GPUs. And the results? Faster training, lower costs, no vendor lock-in, and the kind of performan

When it comes to building and scaling AI infrastructure, most companies default to NVIDIA. But that default is getting expensive, restrictive, and in many cases, outdated.

Enter AMD’s Instinct™ MI325X and a new generation of cloud platforms purpose-built to take advantage of its performance.

At TensorWave, we’ve architected a platform from the ground up to unlock every ounce of capability from AMD GPUs. And the results? Faster training, lower costs, no vendor lock-in, and the kind of performance that leaves traditional NVIDIA-based clouds scrambling to keep up.

Here’s how we do it and why it matters.

Why the Industry Is Looking Beyond NVIDIA

For years, NVIDIA has dominated the AI infrastructure stack. But their lead has created friction:

 * Supply constraints and long waitlists for H100/H200 access
 * Vendor lock-in via CUDA-only tooling
 * Increasing costs per GPU-hour
 * Limited memory (80–120GB max per GPU)

Meanwhile, AMD has quietly leapfrogged some of these limits with the MI325X. This latest Instinct Series GPU comes with with:

 * 256GB of HBM3E memory per GPU
 * 48TB/s memory bandwidth
 * FP8/INT8 support for high-throughput inference
 * Open-source ROCm ecosystem

And the MI325X available now on TensorWave.

MI325X vs H100: The Specs That Matter

On paper, the MI325X delivers similar compute throughput to NVIDIA’s H100 (in FP16/FP32), but it pulls ahead in the places that matter most for modern AI workloads:

The memory advantage is enormous. As you can see, with the MI325X, you can train, fine-tune, and serve larger models without sharding across multiple cards. That means:

 * Simpler code
 * Fewer GPUs required
 * Faster training runs

And in performance benchmarks for inference throughput, AMD’s MI325X has shown up to 30% lower latency than H100 on 70B parameter models.


Why TensorWave Is the Best AMD Cloud for AI Training

Hardware is only part of the equation. How you deploy and scale it is where the real gains show up.


1. Liquid-Cooled MI325X Clusters

We run our MI325X GPUs in custom-designed, liquid-cooled servers, enabling full-throttle performance without thermal throttling. That means consistent throughput at scale, even during sustained training jobs.

🔗 Explore our MI325X Cloud Infrastructure


2. ROCm-Optimized Software Stack

Our platform is built on the open-source ROCm ecosystem, giving you:

 * Native support for PyTorch, TensorFlow, ONNX
 * No CUDA dependency or vendor lock-in
 * Optimized kernels and comms libraries for MI325X

You can port models from NVIDIA to AMD with minimal code changes.


3. Dedicated, High-Memory Clusters

We offer dedicated access to 8-GPU MI325X nodes, each with 2TB of VRAM and 20+ PFLOPS of AI compute.

This lets you:

 * Train trillion-token LLMs without memory constraints
 * Run massive multi-modal models with zero sharding
 * Scale horizontally across nodes with low interconnect overhead

🔗 See how our MI325X compares to NVIDIA


4. White-Glove Support for AI Teams

We work with AI startups, enterprise labs, and research teams to help:

 * Migrate off NVIDIA-based platforms
 * Optimize models for MI325X hardware
 * Reduce infrastructure spend by 30–50%

If you’ve ever waited weeks for an H100 allocation or struggled with CUDA versioning, you know the pain. TensorWave is the cure.

The Bottom Line

NVIDIA still makes great hardware. But it’s no longer the only serious option… And increasingly, it’s not the best one.

With 256GB VRAM, open-source tooling, and a performance profile that matches or beats H100 in key metrics, AMD’s MI325X is the new gold standard.

And if you want the best MI325X cloud in the market, TensorWave delivers:

 * Better memory, faster training
 * No lock-in, fully ROCm-native
 * Dedicated, enterprise-grade clusters

If you're building the next generation of AI models, it's time to stop defaulting to NVIDIA and start outperforming it.

Book a demo and get access to MI325X clusters today.


About TensorWave

TensorWave is the AMD AI cloud purpose-built for performance. Powered exclusively by AMD Instinct™ Series GPUs, we deliver high-bandwidth, memory-optimized infrastructure that scales with your most demanding models—training or inference.

Ready to get started? Connect with a Sales Engineer.

AMD Instinct™ MI355X GPUs Now Available

The Best AMD Cloud for AI Training: How TensorWave Outperforms NVIDIA-Based Platforms

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