From Nvidia's Flaws to OpenAI Jalapeño: A Deep Dive into Redesigning Inference Chips
thehiphopswami · x · 2026-08-30
This article provides a deep dive into the architectural flaws of Nvidia GPUs in inference scenarios, such as E2E latency bottlenecks and KVCache costs. Starting from first principles, it proposes a design philosophy for inference-native chips, detailing the Core Slice architecture, memory subsystem, on-chip network, and software stack. It also conceptualizes a chip named "OpenAI Jalapeño," compares it with traditional GPGPU architectures, and outlines the future roadmap of AI infrastructure.
More from Infra
- 40-nm Memristor Chip Turns Conductance Drift Into a Feature, Beats A100 by 50-480x — maier_ak · 2026-09-01
- 40nm Neural-Dynamics Chip Uses Conductance Drift for 2.12ms Iteration Latency — maier_ak · 2026-09-01
- Qwen3.8 Flash hits 415 tok/s on dual DGX Sparks — NVIDIAAI · 2026-09-01
- OpenAI's 'Jalapeno' Chip Revealed: 1500 Tokens/s Throughput — firstadopter · 2026-09-01
- TensorSharp vs llama.cpp: Qwen 3.8 Flash Next Benchmarks — fuzhongkai · 2026-09-01
- Tencent Hunyuan AngelSlim: Compressing Hy4 Model to 214GB with Heterogeneous Inference — 腾讯混元 · 2026-09-01