AI Accelerator Deployment Can Take Four Months or More

Enterprises are finding that deploying AI accelerators is no easy task. In a recent survey, nearly 62% of semiconductor buyers report that it takes at least four months to get from purchasing an accelerator system to generating their first production AI token.

The finding comes from The Futurum Group’s “1H 2026 Data Center Semiconductor Decision Maker Survey Report,” which surveyed 824 data center semiconductor decision-makers at enterprise organizations, AI consumers and data center operators during the first quarter of 2026.

According to the survey, 38.5% of respondents said it takes four to six months to bring a new AI accelerator cluster online. Another 16.4% reported a six-to-nine-month deployment cycle, while 4.4% require nine to 12 months and 2.5% said it took more than a year.

Only 6.2% of organizations said they can deploy a new accelerator cluster and produce their first production token in less than two months. The research did not delve into why or how these organizations were able to deploy so quickly.

The results show that even though hardware makers have tried to make deployment a plug and go experience, reality is anything but. Futurum Research Director Brendan Burke said the difficult part is on the software side of things.

“The challenge with new accelerators is not buying them, it is bringing them up,” Burke said in the report. “First production token arrives only after teams mature the software stack, validate kernels, and integrate the silicon into existing orchestration. That work is where AI infrastructure budgets quietly overrun. Vendors that treat bring-up as a first-class problem, hardening compilers, libraries, and support around real workloads, hold a defensible position.”

The survey also points to a shift in how data center operators measure AI infrastructure performance, and that is token generation. Only 6.2% said they do not track token volume. Futurum said the numbers indicate that token generation has moved beyond being an AI workload metric and is becoming an operational measure of infrastructure productivity.

The survey also found that 60.3% of respondents are targeting more than 500,000 tokens per second per megawatt. Within that group, 23.2% are targeting 501,000 to one million tokens/sec/MW, 21.8% are targeting one million to two million, 10.9% are targeting two million to four million, and 4.4% are targeting more than four million tokens/sec/MW.

Futurum has also found that hardware utilization, traditionally an important measure of IT equipment efficiency, has fallen significantly out of favor as a metric for AI infrastructure productivity. Hardware utilization is considered a top metric by just 13.8% of survey respondents.

Futurum’s findings point to a broader challenge facing the rapidly expanding AI infrastructure industry: as organizations spend billions of dollars on accelerators, the challenge of getting the hardware up and running quickly will become a significant issue and no doubt a key point of differentiation between competitors.