Person
Jordan Nanos
SemiAnalysis researcher
Ep. 033 - ClusterMAX 3.0 Is Here! Neoclouds Ranked (Neoclouds, GPUs) | Sam Harshe, Pratt Bhatt, Jordan Nanos
SemiAnalysis’ ClusterMAX 3.0 update ranks 77 GPU cloud providers and expands its market view to 323 providers, with a heavy focus on reliability, networking, security, and financing risk. The episode argues that managed GPU clouds are mostly a software and operations problem layered on scarce hardware, while hosted training and RL introduce even harder infrastructure and correctness challenges.
Ep. 030 - Long Live the Short King: Why 4-HI HBM Wins (Memory) | Myron Xie, Jordan Nanos
This episode argues that 4-high HBM is becoming the preferred configuration for many accelerators because it preserves bandwidth while reducing scarce memory capacity. The hosts tie Nvidia’s Rubin Ultra reset from a 1TB concept to 192GB at launch primarily to DRAM/HBM supply constraints, not a performance shortfall.
Ep. 029 - Modular Data Centers Cut Build Time to 12 Months (Datacenter, Energy) | Nico Bontigui, Jordan Nanos, Nigel Chiang, Eric Wen
SemiAnalysis’ panel argues modular data centers compress schedules by moving more work into factories and parallelizing site prep with MEP integration. The real drivers are labor scarcity, time to power, and execution certainty, while the main risks are logistics, commissioning, and factory capacity.
Ep. 026 - PJM's $12B Modeling Mistake Is Hitting Ratepayers Again (Datacenter, Energy) | Robert Boswall, Jordan Nanos
PJM’s capacity auction design is portrayed as a structural modeling failure that overcharged ratepayers by an estimated $12 billion across the last two auctions. Robert Boswell and Jordan Nanos argue the grid underprices winter reliability, overstates demand, and is now repeating the same mistakes in an emergency procurement process tied to data-center growth.
Ep. 017 - DeepSeek V4 and Huawei Ascend NPU Performance (InferenceX) | Kimbo Chen, Cam Quilici, Bryan Shan, Jordan Nanos
DeepSeek V4’s big leap is 1M context via aggressive sparse-attention and KV-cache compression, paired with a mega-MOE/mega-kernel approach to speed expert computation. The episode also compares day-zero support across Nvidia, Huawei Ascend, and AMD, highlighting how early access, kernel fusion, and tooling maturity shape real inference performance.
Ep. 016 - What Unitree's Evolution Means For Robotics (Robotics) | Jordan Nanos, Reyk Knuhtsen, Niko Ciminelli
The episode argues that Unitree’s real moat is not just robot quality, but China’s manufacturing density, fast iteration, and supply-chain depth. The guests think humanoid robotics is still early and messy, yet low-cost robots that are “good enough” for a few useful tasks could create real demand quickly.
Ep. 015 - DG Matrix Explains 800V DC vs Legacy AC Distribution (Datacenter, Energy) | Jordan Nanos, Jeremie Eliahou Ontiveros, Nicolas Bontigui, Haroon Inam
Haroon Inam of DG Matrix argues that 800V DC is becoming necessary as GPU rack power rises beyond what legacy AC distribution can deliver economically. The episode focuses on why voltage, not current, is the key constraint, and how multiport solid-state transformers could make datacenters more flexible, modular, and future-proof.
Ep. 014 - Finding Miscompiles For Fun, Not Profit (AI Infrastructure) | Justin Lebar & Jordan Nanos
Justin Lebar and Jordan Nanos discuss how Lebar found compiler miscompiles using both classic fuzzing and LLM-assisted code review. The episode emphasizes severe x86 and AMDGPU bugs, the difficulty of scaling fuzzers, and the surprising effectiveness—but real cost—of using agents to scan compiler code.