The Information Machine
Concluded·Day 5·first covered 27 Aug 2026·6 sources·updated 31 Aug 2026

OpenAI's Jalapeño inference chip

The gist

OpenAI's Jalapeño Chip Claims Efficiency and Latency Lead Over Nvidia

Jalapeño would reduce OpenAI's dependence on Nvidia for inference workloads. Google, Amazon, Microsoft, and Anthropic are also building custom chips toward the same goal.

The full picture

OpenAI published benchmark results for Jalapeño, its first custom AI inference chip, on August 25, 2026, claiming 1.5 to 1.9 times more AI work per watt and 1.7 to 3.6 times lower end-to-end latency than leading commercially available AI systems, with 2.1 to 4.1 times higher performance on highly interactive workloads. At a matched DeepSeek R1 decoding speed of 169.41 tokens per second per user, Jalapeño produced 12,258 mixed tokens per second per kilowatt versus 118 for Nvidia's GB300. Semianalysis reports the chip achieves approximately 11 million tokens per second per megawatt at around 100 tokens per second per user, roughly twice the efficiency of the best Blackwell-class GPU configurations, and notes that Jalapeño's benchmarks use single-token prediction without speculative decoding while competing chips use multi-token prediction in their best-performing configurations. The chip is inference-only, built with Broadcom and fabricated by TSMC, and deployment within OpenAI's compute infrastructure is planned by end of 2026, with two additional chip generations in development.

How it developed
31 August 2026

A second Semianalysis report published August 30 put Jalapeño's efficiency at approximately 11 million tokens per second per megawatt at around 100 tokens per second per user, roughly twice the best Blackwell-class GPU configurations at comparable interactivity.

The report also disclosed that Jalapeño's published benchmarks use single-token prediction without speculative decoding, while competing chips in those comparisons run with multi-token prediction enabled in their best-performing configurations.

28 August 2026

Coverage on August 27 of OpenAI's August 25 Jalapeño benchmarks specified performance gains for highly interactive workloads at 2.1 to 4.1 times, giving a lower bound absent from earlier reporting.

The same coverage introduced a safety argument: an OpenAI executive identified as roon contended that inference at this speed creates threat models where misaligned frontier models could infiltrate systems faster than human responders can act, requiring autonomous detection and shutdown rather than monitoring alone.

27 August 2026

Coverage following the August 25 benchmark release confirmed Jalapeño was built in partnership with Broadcom and fabricated by TSMC.

The chip handles inference only, leaving OpenAI still dependent on Nvidia for training workloads; a small batch of Jalapeño-powered systems is planned for 2026, more in 2027, and two additional chip generations are in development.

First citedThe NeuronSemafor Technology
26 August 2026

SemiAnalysis published an analysis August 26 extending OpenAI's Jalapeño chip benchmarks, released August 25, to Nvidia's upcoming Vera Rubin generation, finding Jalapeño beats Vera Rubin's published output-token throughput per megawatt.

SemiAnalysis identified electricity as OpenAI's primary design constraint, described a networking architecture making thousands of chips function as a single inference machine, and suggested OpenAI may be designing for models with tens of trillions of parameters or million-token context windows.

19 August 2026

Supply chain checks show Nvidia holding over 80% of AI server market, with ASIC-based servers at 8-11% of shipments

Sources
Semafor Technology
1 more source
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