Upstage's Solar 31B Hits 2,000 Tokens per Second on Cerebras Hardware

Korea's Upstage AI and Cerebras Systems have announced a collaboration that puts Solar 31B -- Upstage's flagship language model -- on Cerebras inference hardware, hitting speeds of up to 2,000 tokens per second. To put that in perspective, Cerebras demonstrated the model completing a deep research query across 246 sources. That kind of multi-step, multi-source workload is exactly where raw inference speed changes the user experience from "go grab a coffee" to "watch it happen in real time."

Two companies with a lot to prove

Upstage was founded in 2020 by Sung Kim, a former professor at the Hong Kong University of Science and Technology who previously led Naver's Clova AI team. The company has been on a remarkable trajectory: it recently crossed the $1 billion valuation threshold to become the first generative AI company in Korea to achieve unicorn status, with total cumulative funding reaching approximately $279.7M. More significantly, Korea's financial authorities approved a 560 billion-won ($380.6 million) investment in Upstage -- a direct signal that Seoul sees the company as a national AI champion.

Cerebras, founded in 2016, built its reputation on a genuinely different approach to chip design. Its core product, the Wafer-Scale Engine, is substantially larger than standard GPUs -- rather than cutting a silicon wafer into hundreds of individual chips, Cerebras uses the entire wafer as one massive processor. That architecture is the reason for the speed: Cerebras solves the memory bandwidth bottleneck by building the largest chip in the world and storing the entire model on-chip, integrating 44GB of SRAM on a single chip and eliminating the need for external memory and the slow lanes linking external memory to compute.

Why 2,000 tokens/sec actually matters

For a single-turn chat, speed above ~100 tokens/sec is mostly invisible to a human reader. But deep research is a fundamentally different workload. The best research agents don't just summarize individual documents -- they follow citation trails, cross-reference findings across papers, identify contradictions in the literature, and produce structured reports that would take a human researcher days to compile. Each of those steps is a separate inference call, and the calls chain together.