6 Startup Opportunities in AI Networking
Where are the gaps, what are the needs, and where are the opportunities
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I wrote a post last week about how networking is the hidden bottleneck of AI. It generated a good amount of discussion. On one hand, networking is a real issue for AI. But on the other hand, there are very few startups building anything new in networking. And with good reason. It’s a very time and capital intensive endeavor.
Networking infrastructure for AI presents an interesting opportunity for founders. It's one of the biggest and most underappreciated problems to solve in AI. Getting customers to use your new networking product is much harder than getting them to use your new AI app. So I decided to explore what parts of the networking stack are inefficient enough for a startup to come in and make an impact.
It's a deeply technical corner that requires overlapping expertise in Networking and AI. Not many builders will approach it. And that presents a rare opportunity for founders.
Despite all the hype around AI models, most people miss the point that you can’t just throw GPUs together and hope for breakthrough performance. So what can startups do about it? Here are 6 opportunities I've been thinking about:
Network-as-a-Service (NaaS) for AI Workloads
We need a product that provides Network as a Service (NaaS) for AI Workloads. This is a networking solution that's specifically built for AI training and is on-demand. Products that offer NaaS optimized for high-bandwidth and low-latency connections will enable faster model training.
Another topic to keep in mind is Federated Learning. It's a beast that needs a proper network leash. As models become decentralized, there's an urgent need for startups to build services that efficiently and securely manage edge-to-cloud communication. Those who can optimize for latency, security, and edge device bandwidth constraints will own this space.
We need SDN tools tailored for AI, thereby allowing real-time changes in network setup to meet fluctuating training demands.
Low-latency is going to be key here. The moment your GPU is waiting on a data packet, your efficiency tanks. This is where AI-driven traffic management solutions step in. Startups can develop systems that intelligently reroute and prioritize data flows to optimize bandwidth and latency in real-time. AI training shouldn't be sitting in a traffic jam. It should be cruising down an open highway at top speed.
We need to ensure network security for sensitive AI training. AI workloads increasingly involve sensitive data, and current network security measures are falling short. Startups need to step up with AI-focused security protocols while ensuring that sensitive data can be safely transferred between nodes, models, and training sites.
Large-scale AI training that respects data privacy is the need of the hour. Developing tools that offer privacy-preserving techniques like differential privacy or homomorphic encryption will make a startup a key player in this space.
Verticalized Network Hardware
Today's network hardware is too generic for AI workloads. The future of AI requires niche network hardware that's purpose-built. Startups can go after this space by building specialized hardware optimized for distributed training. Think programmable switches or Network Interface Cards (NICs) with AI-native features. We should aim to build networking hardware that's laser-focused on squeezing every ounce of performance out of those GPUs.
Another avenue is accelerated RDMA (Remote Direct Memory Access) solutions. RDMA is a networking technology that allows one computer to directly access the memory of another computer without involving a CPU. It's a needle-mover for AI, but the current solutions are far from optimal. Startups that can deliver faster and more efficient RDMA engines for intra-node communication will stand out. Why bother doing it? Because we want to maximize data transfer speed between GPUs, whether within a data center or across the globe.
This network hardware needs to be power-efficient. AI is power-hungry and so is networking. Startups that can develop networking gear that balances high performance with low power consumption have a huge market opportunity. Data centers need to keep their AI workloads green and power-efficient networking hardware will be in high demand. By managing networking parameters based on real-time demand, startups can reduce power consumption during off-peak times without slowing down training.
Verticalized Networking Software
Networking covers an enormous surface area in technology. It's used across so many sectors, industries, and use cases. But different areas have different networking needs. For example, text processing and image processing have vastly different networking needs. Building networking solutions that are optimized for the unique data transfer patterns of specific models could be interesting.
Another example is the type of model that's being used. Let's consider Graph Neural Networks (GNNs). They are taking off big time and they require different networking infrastructure than standard neural networks. There's a gap here that startups can fill by developing networking solutions tailored to the specific needs of GNNs.
Networking Middleware
We need middleware that optimizes AI training protocols. This is dominated by Nvidia Collective Communication Library (NCCL). So if you can develop a better NCCL (one that isn't tied to specific hardware or ecosystem), then you may be onto something.
Another operation that comes to mind is AllReduce. It is a collective operation that combines data from multiple processing units into a global result and then distributes it back to all processing units include. If you can build a faster AllReduce, it could lead to the next generation of distributed training.
Current networking protocols aren't built for AI. They're built for general use, which is why they fall short in model training environments. Creating new protocols might be a steep hill to climb, but it's what we need. We need a way to do better prioritization of packets, enhanced data compression, and a reduction in packet loss.
Telemetry Tools
AI networks don’t just need data. They need real-time insights and they need them fast. We need a better telemetry solution to not only monitor but also automatically optimize network performance. Think of it as the autopilot for network traffic, keeping GPUs fully utilized and training running at top speed. And when things go wrong, you need precise diagnostics.
If your network slows down, then your whole training pipeline suffers. A diagnostic tool to pinpoint and resolve AI-specific network issues is very valuable. Startups that can deliver intelligent diagnostics with specific fixes would be a great solution to have.
Topology and Network Architecture Tools
AI training relies heavily on efficient network topologies. But the real game-changer is how you optimize them. We need to build software that can design, manage, and continually tweak topologies to achieve maximum efficiency. Congestion should be rare and every GPU should have a clear path to the data it needs. And the issue is that static network setups simply don't make the cut.
What AI training demands is dynamic orchestration that adjusts the network layout based on the task at hand. If your tool isn't capable of adapting to varying workloads, you're just dead weight.
If you're a founder or an investor who has been thinking about this, I'd love to hear from you. I’m at prateek at moxxie dot vc.
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