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The second round was led by Jane Street after it tested Etched’s hardware and installed the first rack in its own data center.
So what did Jane Street see?
Etched began with Sohu, a Transformer-only ASIC that promised more than 500,000 tokens per second on Llama 70B. By 2026, Sohu and that claim had disappeared. Etched now sells a complete inference cluster and says it can run Transformers, MoEs, and even Mamba.
We explain how that shift is possible, what Low Voltage Inference and Cluster Scale Memory actually mean, and how this still tiny company can hurt giant NVIDIA.
And the question I want you to keep from this episode: The GPU once found the winning middle ground between flexibility and specialization. Has Etched found the next one?
*Watch it.*
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Attention Span is here to show you AI isn’t magic. Sometimes the decisive question is how much flexibility we are still willing to pay for.
*Sources:*
Etched, From Zero to One
Etched, Accelerating Inference and Frontier Inference Clusters
Etched’s 2026 architecture-agnostic and Mamba claims
Reuters on the $21 billion financing and Jane Street deployment
The Wall Street Journal on Etched’s team and NVIDIA recruiting
TechCrunch on the original Transformer-only Sohu pitch
Etched patent on model-specific ASIC compilation and configurable execution
Mamba-2 and Structured State Space Duality
Jane Street on its machine-learning infrastructure
Jane Street on microsecond-scale performance engineering
CoreWeave and Jane Street’s $6 billion cloud agreement
The founders on Etched’s supply-chain choices
NVIDIA on Vera Rubin and Groq 3 LPX
NVIDIA Q1 FY2027 results
Taalas on model-specific silicon
#AttentionSpan #Etched #JaneStreet #AIChips #AIInference #NVIDIA #Semiconductors #Mamba #TuringPost
