- Etched reached a $10.3 billion valuation after securing fresh SK Hynix backing
- The startup built custom processors specifically for demanding AI inference workloads
- Etched says conventional GPUs waste power during many inference tasks today
Etched, a US-based AI chip startup, has secured fresh backing from SK Hynix as it hits a $10.3 billion valuation while pursuing processors built specifically for inference workloads.
The company argues that conventional GPUs deliver more computational capability than many inference tasks require, yet offer insufficient memory for increasingly demanding AI models.
Its latest funding will support the production of rack-scale inference systems designed around custom chips, shared memory, and lower power consumption, rather than general-purpose graphics processors.
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Etched bets custom inference chips can outperform traditional GPUs
Founded by former Harvard students Gavin Uberti, Robert Wachen, and Chris Zhu, Etched focuses exclusively on AI inference rather than training large language models.
The startup says its systems combine Low Voltage Inference (LVI) technology with Cluster Scale Memory (CSM), allowing processors to access substantially larger shared memory pools than conventional GPUs.
“Today, AI chips can’t scale FLOPs without thermal throttling. As FLOPs utilization increases, AI chips draw more power and downregulate clock speed…,” said Chris Zhu
We’ve designed a new architecture to run our chip’s math blocks at under half the voltage of most AI chips. This enables multiple times the FLOPs density of AI chips today.”
The researchers claim that existing processors struggle to increase floating-point performance because higher utilisation raises power consumption and eventually reduces operating clock speeds through thermal limits.
Processors using High Bandwidth Memory (HBM) cannot achieve SRAM-level decoding speeds because memory subsystems and interconnects introduce additional latency during inference workloads.
They therefore created a shared low-latency memory pool connected through what it describes as a proprietary ultra-low-latency, high-bandwidth interconnect to enable faster memory access
“Our HBM/SRAM hybrid design solves both memory capacity and mem2mem latency, enabling high throughput and interactivity simultaneously.
“CSM improves latency and avoids today’s cost, reliability, yield, thermal, and compute tradeoffs of SRAM-only chips, 3D DRAM chips, or optics.”
The company also claims many AI models waste time moving data between chips, memory, and networking hardware before processing can continue.
According to Etched, its CSM architecture reduces those delays by minimising additional memory layers during data transfers.
Funding surge accelerates production and global expansion
Etched has attracted funding at a rapid pace, raising $5.4 million in its 2023 seed round, $120 million in 2024, $500 million in 2025, and $300 million in 2026.
Those investments bring total funding to approximately $925.4 million, while the latest financing doubled the company’s valuation from $5 billion to $10.3 billion.
The C-round included Sequoia Capital, Andreessen Horowitz, Jane Street, Diffusion, Argo, and SK Hynix, signalling continued investor confidence despite increasing competition within AI hardware markets.
Robert Wachen said, “This round accelerates production of our inference clusters,” while confirming an 80,000 sq ft, 10 MW facility opened near Milpitas.
The company now employs more than 400 people, reports customer demand exceeding $1 billion, and has established manufacturing operations in Taiwan.
Etched systems will support conventional large language models, mixture-of-experts architectures, and alternatives including Mamba.
Via Blocksandfiles
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