Why the Future of AI Inference Runs on Modular Data Centers and Custom Silicon

From fraud detection and recommendation engines to industrial automation and sovereign defense systems, inference is where AI earns its keep. It’s fast, frequent, and everywhere. But there's a major infrastructure problem hiding in plain sight. 

🔹 The Real Problem: You Don’t Know What Inference Workloads Are Coming Next 

Today’s inference landscape is unpredictable: 

• One customer may bring 12kW air-cooled custom silicon. 

• Another might need 60kW liquid-cooled GPU clusters. 

• The next wave could demand rack-scale compute with CXL fabric or streaming inference. 

  
  

Inference silicon is evolving every 12–24 months—and each workload has unique cooling, power, interconnect, and latency needs. Most traditional data centers can’t reconfigure fast enough to support this shift. 

🔁 Modular Data Centers (MDCs) are built for this uncertainty. 

🔹 Why Modular AI Data Centers Are Built for Inference 

Modular Data Centers are prefabricated, high-density, and highly adaptable. They’re designed to support silicon change-outs, rack-by-rack cooling differences, and fast deployments in both core and edge locations. 

Key benefits: 

• 3–9 month deployment timelines (vs. 18–24 for traditional builds) 

• Support for air or liquid cooling—per rack, not per site 

• Silicon-agnostic design (GPU, ASIC, FPGA, custom silicon) 

• Built-in infrastructure for CXL, PCIe 5.0/6.0, and high-bandwidth fabrics 

In a world where inference is highly location- and latency-sensitive, MDCs offer infrastructure that moves as fast as your silicon roadmap. 

🔹 Inference Throughput: Why Custom Silicon is Changing the Game 

Let’s look at a real-world business example comparing a traditional GPU (NVIDIA H100) to a high-throughput custom inference chip (Groq LPU). 

🧠 Scenario: Inference-as-a-Service 

You're charging $2 per 1M tokens processed. 

Metric	NVIDIA H100 (GPU)	Groq LPU (Custom Silicon)
Tokens/sec (realistic load)	~2,000 tokens/sec	~100,000 tokens/sec
Utilization	90%	90%
Daily Tokens	155M	7.78B
Daily Revenue	$310	$15,552
Power Consumption	~700W	~300W
Cooling Required	Liquid preferred	Air cooled

✅ One Groq node can produce 50x+ the revenue of a GPU node, while consuming less power and simplifying infrastructure. 

And Groq isn’t alone. Other inference-optimized chips like AWS Inferentia2, AMD MI300X, SambaNova SN40, and Tenstorrent are also pushing new performance and efficiency frontiers. 

🔹 Why This Matters: You Can’t Build Around One Silicon Type Anymore 

This isn’t about whether a data center supports 30kW or 60kW per rack. It’s about whether your infrastructure is agile enough to handle: 

• Different cooling needs 

• Rapid silicon swaps 

• Model tuning shifts 

• Throughput-based business models 

Inference is no longer one-size-fits-all. And your next customer’s architecture will not look like your last one’s. 

🔹 MDCs + Custom Silicon = Inference-Optimized Infrastructure 

Metric	Legacy DCs (GPU-Centric)	Modular DCs (Custom-Ready)
Deployment Time	18–24 months	3–9 months
Silicon Refresh Cycle Support	Slow / cost-prohibitive	Fast / modular
Cooling Flexibility	Liquid-focused	Air & Liquid per rack
Token Throughput	~2,000/sec	100,000+ (Groq, etc.)
Revenue Potential	~$310/day/node	$15,552/day/node

🔹 Top Use Cases for AI Inference-Optimized MDCs 

• Edge AI deployments in manufacturing, logistics, and energy 

• Inference clusters near RAN/telecom aggregation points 

• Low-latency GenAI serving retail, finance, and customer support 

• Defense and sovereign AI systems requiring mobility and air-gapped operations 

• Content and streaming AI where real-time recommendations drive monetization 

✅ Bottom Line: The Inference Economy Needs Infrastructure Built to Flex 

Inference is no longer niche. It’s the mainstream revenue engine of AI. But it's fast-moving, silicon-diverse, and operationally demanding. 

If your infrastructure is rigid, overbuilt for the wrong assumptions, or takes years to adapt—you're already behind. 

Modular Data Centers give you the agility to say “yes” to any inference customer—no matter their chip, workload, or location. 

📞 Ready to move at the speed of inference? 

Let’s talk about how our Modular AI Data Centers can help you deploy faster, adapt smarter, and monetize inference wherever it lives. 

👉 [Contact us] to get started.