The speed of inference has always been the practical limit on AI in B2B sales workflows. GPT-5.6 Sol running at 750 tokens per second on Cerebras, announced July 2026, removes that limit for most commercial use cases.
To understand why that matters, start with what the existing bottlenecks actually cost.
What does 750 tokens per second mean in a real sales workflow?
A typical SDR doing account research manually spends 30 to 45 minutes per account: reading recent news, checking LinkedIn for hiring signals, reviewing press releases, scanning the company blog. A structured AI prompt handles the same task in under two minutes at Terra tier. At Sol on Cerebras, that drops to seconds.
At 750 tokens per second, a 1,000-token account brief generates in roughly one second. A 5,000-token deep research summary, including competitive landscape analysis and recent company news, completes in under seven seconds. For a sales team targeting 50 accounts per week, that is a reclaim of roughly 35 to 40 hours of research time.
How does this change post-event follow-up?
This is the highest-leverage application for most B2B revenue teams. The average webinar generates about 300 registrants with 40 to 50 percent live attendance. Following up the next morning means you are competing with every other vendor who sent a Tuesday follow-up to a Monday event.
Luna-speed AI at 750 tokens per second means follow-up can go out the same day, within hours of the event, personalized to what each attendee asked in the Q&A or chat. At LinkedOtter, 460 to 577 live attendees is a typical event. Getting personalized follow-up to those attendees within three hours of the event closing is now a technical reality, not a capacity challenge.
Response rates in B2B drop roughly 60 percent between same-day and 48-hour follow-up. Speed is pipeline.
Does raw token speed help with prospect scoring?
Yes, and this is the underappreciated use case. Most teams score webinar registrants with simple rule-based filters: job title, company size, attended or no-showed. A Sol-tier model at 750 tokens per second can analyze every registrant against 12 to 20 signals simultaneously: LinkedIn activity in the last 30 days, recent funding news, technology stack signals, whether the company is in an active procurement cycle.
For a 300-registrant webinar, that full scoring run completes in under two minutes. The output is a ranked follow-up list with context notes per account that a sales rep can act on immediately.
Is this speed relevant if you use Clay or Apollo for enrichment?
Yes, as a complement. Clay runs waterfall enrichment across 75-plus data sources to get contact-level data. Cerebras-hosted Sol handles the synthesis layer: taking enriched data and producing natural-language research briefs, follow-up personalization, and priority scoring. The two tools solve different problems.
Apollo feeds you contact data. Clay enriches it. Sol on Cerebras interprets and prioritizes it. For cybersecurity vendors running events at scale, combining all three cuts the gap between event and booked meeting from two weeks to two days.
What does this mean for event-led outbound specifically?
Events create a specific, time-bounded window of buyer attention. A CISO who attended your zero-trust roundtable on Tuesday is warm on Tuesday. By Friday they have fielded four other vendor calls and the conversation has faded. Same-day follow-up, personalized to the roundtable conversation, is the difference between booking the meeting and losing the window.
LinkedOtter's model is built around this timing. Events generate the attendance signal. AI at Cerebras speed processes the scoring and personalization. Sales reps take the meetings that follow. The 43 qualified meetings per 60-day cycle LinkedOtter clients book is a direct function of follow-up speed and precision.