emi @gpuemi
co-founder @wafer_ai -- agents that optimize gpu. math @uchicago wafer.ai/manifesto san francisco, ca Joined December 2015-
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@apsknight_ We work with a lot of inference partners. @wafer_ai in this case
Really fast GLM now live
GLM 5.2 Fast via Wafer is now available exclusively on Vercel AI Gateway. 2x faster token throughput vs other providers in internal benchmarks. 𝚖𝚘𝚍𝚎𝚕: '𝚣𝚊𝚒/𝚐𝚕𝚖-𝟻.𝟸-𝚏𝚊𝚜𝚝' vercel.com/changelog/glm-…
🚨 BREAKING: GLM-5.2 FAST is now live on the @vercel AI Gateway, served by @wafer_ai now the fastest serverless speeds in the world: ⚡️ 150 - 250 tok/s 💲 $3.00 in / $10.25 out / $0.50 cache per 1M tokens 🧠 1M-token context run it on the Vercel AI Gateway: vercel.com/wafer-ai/~/ai-… thanks @rauchg and the @vercel team.
GLM-5.2 Fast is up on the @vercel ai gateway now. 150 - 250 tps. fastest serverless speeds out there give it a try: vercel.com/wafer-ai/~/ai-…
GLM 5.2 Fast via Wafer is now available exclusively on Vercel AI Gateway. 2x faster token throughput vs other providers in internal benchmarks. 𝚖𝚘𝚍𝚎𝚕: '𝚣𝚊𝚒/𝚐𝚕𝚖-𝟻.𝟸-𝚏𝚊𝚜𝚝' vercel.com/changelog/glm-…
Bring your whole team and dozens of AI coding agents into the same chat threads, then let @linzumi_ai keep the fleet coordinated and unblocked. And for a limited time, try state of the art open-weights intelligence free: GLM 5.2 at high speed, via their @wafer_ai partnership. → linzumi.com
I do not get this neuroticism. Your electricity bill from leaving the lights on all the time is like ten dollars a year. It is not 1926.
40% of fatherhood is walking around the house, turning off lights.
in the near term you should probably just use @wafer_ai which seems to be the cheapest provider instead of building your own rig. but the fact you can even consider doing it now is a very good sign for personal on-device AI in the next decade as trillions of dollars go into chip science.
GLM 5.2 costs $1.40/4.40 per Mtok at 40 tok/sec and people seriously consider buying GPU rigs for it
Holy shit 222 tokens per second with GLM 5.2
🚨 BREAKING: wafer now runs the fastest, lowest-latency GLM-5.2 anywhere ranked #1 across every provider on Artificial Analysis: ⚡ 222 output tok/s (next best: 173) ⚡ 12.6s end-to-end response time (next best: 16.9s) try it: app.wafer.ai
Opus-SuperFast
🚨 BREAKING: wafer now runs the fastest, lowest-latency GLM-5.2 anywhere ranked #1 across every provider on Artificial Analysis: ⚡ 222 output tok/s (next best: 173) ⚡ 12.6s end-to-end response time (next best: 16.9s) try it: app.wafer.ai
GLM 5.2 @ 220 tok/s wafer.ai
100% of AI chip startups have slides/“simulated performance data” showing that their chip is way better, but 99% of custom ASICs fail. Why? The MATH isn’t MATH until you realize that AI chips are about Software. It is relatively easy to build a chip and put numbers onto slides; it is hard to build great software. That is why 99% of AI chip startups fail.
🚨 BREAKING: wafer now runs the fastest, lowest-latency GLM-5.2 anywhere ranked #1 across every provider on Artificial Analysis: ⚡ 222 output tok/s (next best: 173) ⚡ 12.6s end-to-end response time (next best: 16.9s) try it: app.wafer.ai
🚨 BREAKING: GLM-5.2 is now on app.wafer.ai and openrouter.ai/provider/wafer It's the top open-weights model right now. #1 open model on the Artificial Analysis Intelligence Index with a score of 51, a 1M-token context window. It’s also the first open-weights model to cross 80% on Terminal-Bench. try it now: app.wafer.ai
computa activate GLM 5.2 on app.wafer.ai
as with most large research projects that concretize in one pretty graph, its hard to gauge the months of work, and hundreds of engineer and scientist hours that went into this result. grateful for the love and care kenny and team put into this and excited to finally share.
We introduce TherapeuticsBench Preclinical Pharmacology (TxBench-PP), a verifiable benchmark for small-molecule preclinical pharmacology and the first focused slice of a broader benchmarking effort across drug-discovery stages and therapeutic modalities. TxBench-PP tests whether
we RECENTLY delivered low latency voice for neon health!! see below to see how we delivered blazing fast speeds.
minimizing time to first token is CRUCIAL for voice AI deployment. optimizing GLM-5.1 for Neon Health took ttft 800ms → 550ms at 25% higher peak load. what we learned: • kv locality is a scheduling primitive (95%+ hit rate) • prefill admission > prefill speed — chunked
minimizing time to first token is CRUCIAL for voice AI deployment. optimizing GLM-5.1 for Neon Health took ttft 800ms → 550ms at 25% higher peak load. what we learned: • kv locality is a scheduling primitive (95%+ hit rate) • prefill admission > prefill speed — chunked prefill • short decode steps beat speculative decoding under bursts • stable first token > max gpu utilization • optimize the client-observed number, not server-side ttft under 2 weeks, under a BAA, US-only residency.
good luck to p26! <3
today is yc demo day. just about a year ago, @gpuemi and i stepped onto that stage and presented wafer (f.k.a. herdora). what felt like the end of a chaotic batch turned out to be the beginning of everything that mattered. for everyone presenting today: enjoy the moment,
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