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Beirit Harvey

AI inference has a cost problem. NVIDIA dominates the current GPU market, making large-scale inference expensive. Teams believe they must either pay high prices for premium compute or accept slower performance from less expensive alternatives.

TensorWave and Modular: Breaking AI Inference Barriers Beyond NVIDIA

The Challenge

AI inference has a cost problem.

NVIDIA dominates the current GPU market, making large-scale inference expensive. Teams believe they must either pay high prices for premium compute or accept slower performance from less expensive alternatives.

TensorWave recognized this market gap early. As an AI Cloud platform focused exclusively on AMD infrastructure, TensorWave continues to prove that alternative accelerators can deliver enterprise-grade performance at a fundamentally different price point. At the heart of the TensorWave AI stack are the latest accelerators of the AMD ecosystem, AMD Instinct GPUs: the MI300X (air-cooled) and liquid-cooled MI325, MI355, and upcoming MI455X (launching 2026), offering top-tier AI performance.

Our IaaS layer includes versatile compute: bare-metal, GPU clusters, and containers. Storage brings SSD block, object storage, and Weka shared file systems. Our open networking delivers 800G UEC-ready interconnects. We support ROCm, and bring powerful enterprise solutions like ScalarLM, a fully open source, integrated LLM inference and training platform built for AMD ROCm and optimized to run on TensorWave infrastructure.

Further, customers can access AMD AES/AIM, together they give AMD-powered clouds dynamic, workload-aware optimization from training to inference, ensuring flexibility and performance. At the apex we bring reserved inference and partner inference solutions. Customers require software that can extract maximum performance from these chips. That is where Modular comes in.


TensorWave + Modular

Modular's MAX is an inference framework and serving engine built from the ground up for generative AI. MAX delivers high-performance inference across any accelerator, with all the tools developers need to optimize and deploy AI models fast.

Together, TensorWave and Modular have built a deployment path that is both faster and cheaper than most existing inference setups. AMD compute on TensorWave starts at 58% lower hourly cost than NVIDIA H200 on AWS. Performance savings from MAX compound on top of that.

When MAX runs on TensorWave's AMD GPUs, the throughput gains are measurable across popular open models:

 * Meta-Llama-3.1-8B: Up to 23% faster
 * Mistral-Small-24B: Up to 13% faster
 * Gemma-3-12B: Up to 57% faster

Combined with TensorWave's hardware pricing, these gains translate to 60-70% lower cost per million tokens across common workloads. Teams can serve the same traffic for significantly less, or absorb considerably more volume without adding budget.


Modular 26.2: Image Generation at New Economics

The Modular 26.2 release expands MAX's capabilities to include image generation and image editing, starting with Black Forest Labs' FLUX.2 model family. TensorWave customers running MAX get this without any stack changes. Same integration, new endpoint, new workloads.

MAX delivers a 4.1x throughput improvement over torch.compile on NVIDIA B200 across FLUX.2 variants, enabling sub-second image generation at 1024x1024 resolution. The performance carries over to AMD MI355X, and TensorWave's hardware pricing adds another layer of savings on top:

 * AMD MI355X runs at approximately $5.00/hr versus $7.00/hr for B200
 * 4.1x throughput improvement from MAX means 4.1x more images per GPU-hour
 * Combined, the result is a 5.5x total TCO advantage versus torch.compile on B200

MAX on TensorWave's MI355X delivers FLUX.2 images at a fraction of a cent each: 99% cheaper than Nano Banana Pro and 82% cheaper than torch.compile on B200.



ProviderCost per Image (1024x1024)Savings vs. Modular on AMDNano Banana Pro$0.13499.0%torch.compile on B200$0.0077882.1%MAX on B200$0.0019428.4%MAX on MI355X (TensorWave)$0.00139Reference



B200 at $7.00/hr, MI355X at $5.00/hr. torch.compile ~4.0s/image, MAX ~1.0s/image. Nano Banana Pro pricing as of March 2026.

Modular projects up to 7x speedup over standard PyTorch implementations in the coming weeks as additional optimizations ship.


For Image Generation Teams

For teams running image generation in production, the cost-per-output math changes significantly here. Sub-second latency on production AMD hardware at 5.5x lower TCO opens up workflows that were previously too expensive or too slow to build around.

TensorWave's MI355X infrastructure paired with MAX 26.2 delivers production-ready FLUX.2 image generation with no PyTorch porting required, enterprise-grade compliance coverage, and access to Modular's expanding model ecosystem across text, audio, and image modalities.


Get Started

Deploy MAX on TensorWave's AMD infrastructure today. AMD Instinct MI355X GPUs are now generally available, and image generation with MAX 26.2 is ready for production workloads. Whether you're running text inference today or evaluating image generation for the first time, the economics are worth a conversation.

Access AMD MI355X compute and start running MAX in your environment. Book a call with TensorWave

See MAX performance benchmarks, explore the full model catalog, or talk to the team about a custom deployment. Request a demo.


TensorWave AI Cloud: Engineered for AI Velocity

TensorWave is the industry’s premier AMD-exclusive cloud provider, delivering memory optimized AI infrastructure for training, fine-tuning and inference.

Our platform combines the raw computational power of AMD's latest GPU architectures with innovative liquid cooling technology and ultra-high-speed networking, creating an optimized environment for the most demanding AI workloads. From large language model training to real-time inference, TensorWave provides the performance, scalability, and cost-efficiency that modern AI initiatives demand.

Ready to get started? Connect with a sales engineer.


About Modular

Modular is building AI’s unified compute layer, enabling developers to deploy production gen AI systems across any hardware in a matter of minutes. The Modular platform lets you run frontier models at blazing fast speed on AMD or NVIDIA GPUs, without the complexity of fragmented toolchains and with a software stack that just works.

TensorWave and Modular: Breaking AI Inference Barriers Beyond NVIDIA

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