Bert Verrycken’s Post

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.

NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs. Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck. The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive. Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks. NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration. The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer. The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds. In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI. Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.