AI is heating up—in more ways than one. The recent TechCrunch article "This startup's metal stacks could help solve AI's massive heat problem" highlights a critical pain point in the world of AI infrastructure: racks pulling up to 600 kW of electricity and generating immense heat loads. (TechCrunch)
At Bare-Metal.io, we've been focused on delivering high-performance, dedicated bare metal servers that power data-driven workloads. The cooling and power challenges described by TechCrunch underscore why dedicated, optimized infrastructure matters more than ever for AI, big data, and real-time analytics.
In this post, we'll unpack what the TechCrunch article reveals, why it matters for infrastructure decision-makers, and how Bare-Metal.io is uniquely positioned to help enterprises meet the demands of next-gen AI workloads.
The Challenge: AI at Scale = Joules, Heat and Complexity
Here are some of the key take-aways from the TechCrunch piece:
- When Nvidia announced its Rubin-series GPUs in March, it admitted that racks built around the Ultra version of the chip (expected 2027) could draw up to 600 kW of electricity. (TechCrunch)
- Such high-density racks bring a massive cooling challenge: keeping GPUs and their supporting components (memory, networking, peripheral chips) within safe thermal limits becomes non-trivial. For example, the peripheral chips already account for ~20% of cooling load in a server. (TechCrunch)
- One startup, Alloy Enterprises, is addressing this by producing custom "cold plates" made from copper, using a diffusion-bonding process they call "stack forging." These cold plates reportedly deliver 35% better thermal performance than alternatives. (TechCrunch)
- The bigger message: as AI workloads scale, the old paradigms of data–center racking, cooling, and infrastructure budgeting may no longer suffice.
For enterprises that are deploying or supporting large-scale AI models, inference farms, or real-time analytics pipelines, these developments carry major implications:
- Power provisioning needs to account for extreme densities.
- Cooling systems (liquid cooling, cold plates, etc) may become mandatory instead of optional.
- Infrastructure cost is no longer just compute/rack cost; it becomes power, cooling, and operational overhead.
- Infrastructure design must become more vertically integrated: compute, cooling, networking, and storage cannot be treated in isolation.
Why This is Relevant for Bare Metal Infrastructure
Given those challenges, what does it mean for the infrastructure selection decision? At Bare-Metal.io we see several key take-aways:
1. Dedicated hardware is increasingly strategic for high-density workloads
Virtualized cloud instances are great for elasticity, but when you start looking at racks with 500-600 kW consumption, you're in first‐class data-center territory. Having dedicated servers (with no hypervisor overhead, no "noisy neighbor" nearby) gives you better control of thermal design, cooling pathways, and power budgeting.
2. Predictable infrastructure cost meets predictable operational cost
When you rent compute from large public cloud providers, the cost variables expand as you scale—especially when cooling and power are part of the equation (colocation, data center density, etc). With a bare metal model you own or lease dedicated servers, you know your maximum power draw and cooling envelope in advance. At Bare-Metal.io, we emphasise predictable pricing and service—with no surprise egress fees or hidden virtualization overhead. (Bare-Metal.io)
3. Optimisation matters when every kW counts
At 600 kW per rack, even a 5–10% improvement in cooling efficiency or power draw can translate into significant cost savings and higher density. The Alloy example (35% better thermal performance) is the kind of improvement that makes a difference. When you're using bare metal infrastructure, you have the freedom to optimise hardware configurations, rack layouts, and cooling zones more aggressively than a general-purpose cloud environment.
4. Workload alignment: real-time analytics, AI inference, big data
Many of our clients at Bare-Metal.io are running demanding workloads—real-time analytics, ClickHouse or Druid clusters, large storage volumes (MinIO, S3-compatible object stores) and emerging AI/ML inference workloads. (Bare-Metal.io) These workloads benefit from the infrastructure certainty, high performance, and architectural freedom that dedicated bare metal provides.
How Bare-Metal.io Helps You Rise to the AI Infrastructure Challenge
Here's how we position Bare-Metal.io to meet the needs of next-gen AI-driven infrastructure:
High-performance dedicated servers
We offer large-scale dedicated server configurations (multi-hundred cores, terabytes of RAM, NVMe RAID) with unlimited bandwidth and managed firewall. (Bare-Metal.io)
Strategic data-centres optimised for performance and cost
Our facilities in Denver and Seattle (with others coming soon) offer low-latency network paths, high-density power and cooling setups, and colocation environments designed for high-performance workloads. (Bare-Metal.io)
Architectural flexibility for hybrid & big-data centric workloads
We support architectures where high-density computing happens on dedicated hardware while cloud services handle burst or elastic components. As the article shows, when compute density goes up (and heat/complexity increases), splitting workloads into dedicated infrastructure + cloud may be a smart strategy. (Bare-Metal.io)
Cost transparency and freedom from vendor lock-in
Because you get a dedicated server with known power and cooling requirements, your cost model is simpler and you avoid surprises. This is increasingly important when infrastructure risk and operational cost matter more than ever. (Bare-Metal.io)
Ability to partner and customise for AI/ML and Big Data needs
Whether you're investing in GPU-dense servers, liquid cooling, or custom thermal design to support inference farms, we can work with you. The TechCrunch article indicates that cooling innovations (like the cold plate from Alloy) will be a differentiator — and infrastructure providers that anticipate, support, and optimise for this will win.
Looking Ahead: Infrastructure Trends to Watch
- Liquid cooling becomes mainstream: As racks move past 500 kW and approach 1 MW, the limitation will shift from compute to cooling. Infrastructure providers (and their clients) must plan accordingly.
- Workload shifts to edge and hybrid deployments: Latency-sensitive AI inference and real-time analytics may co-locate near compute density hubs rather than live in generic cloud regions.
- Thermal & power efficiency are competitive differentiators: Reducing PUE (Power Usage Effectiveness), optimising flow, and reducing cooling footprint will influence infrastructure cost per unit of compute much like core price once did.
- Software-hardware co-design will matter: Disaggregated architectures, custom cooling plates, dedicated AI inference servers—all of this will push the discussion from just "how many cores" to "how many cores and how well can we cool them and feed them data".
Final Thoughts
The TechCrunch article shines a spotlight on a lesser-discussed but rapidly evolving problem in the AI infrastructure space: heat, power and cooling at scale. For enterprises planning for large-scale AI inference, analytics or data-intensive workloads, this isn't just a back-office HVAC issue—it's a strategic infrastructure decision.
At Bare-Metal.io, we believe that the shift to high-density, cost-efficient, dedicated bare metal infrastructure is increasingly justified—not just from a performance or cost standpoint, but from a thermal and operational risk standpoint. If your organisation is scaling AI, big data, or real-time analytics, it's time to ask whether your infrastructure strategy truly accounts for power, cooling, density and cost.