We deploy heterogenous compute
San Francisco
Joined May 2026
- Tweets118
- Following31
- Followers1.1K
- Likes259
Pinned Tweet
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
General Compute retweeted
We're scaling with @general_compute . GC is purchasing @positron_ai Atlas and committing to our next gen Titan over a multi year agreement, expanding its inference offering and bringing Positron to more cloud customers. Atlas available through GC in Q1 2027. A big growth milestone for both teams. Ty @FPuklowski @GoodisonJason - this team is special.
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
General Compute retweeted
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
General Compute retweeted
🚨 @general_compute is partnering with @positron_ai.
Hella excited to announce a multi-year cluster scale deployment of @positron_ai silicon alongside @nvidia to serve even more fast inference in Q1 2027.
Prefill on GPUs, decode on silicon built for it. Packaged up for inference clouds to get more fast tokens - serving open source - into developers' hands.
Here are four reasons we're betting on Positron.
1/ The team
@trsohmers was a Thiel Fellow who taped out his first chip as a teenager. He built REX Computing before co-founding Positron. (recommend you watch Thomas on 20VC podcast that went our last month!)
@mitesh711 spent seven years at Lambda as COO and a founding team member, scaling its GPU cloud to incredible heights. He is a commercial machine ❤️.
We've spent a lot of time with both. They will do whatever it takes to make Positron win, and they won't stop until it does.
2/ The technology
We've gone deep on their roadmap. Atlas is what we are deploying today as disaggregated compute, Titan, built on their Asimov silicon, is where we will be 10x-ing our deployments together.
We believe that all 'memory heavy silicon' with the right fundamentals will be sold out for at least a decade. Because it's the only architecture that can supply fast inference at scale. Positron has built a memory-first architecture that we think is a winner. Asimov is built around around the problem without depending on scarce HBM.
We think it's the right formula for faster, cheaper tokens.
3/ The investors
Look at the cap table. Positron just closed an $875M Series C at a $5B valuation.@gavinsbaker and Atreides co-led, and Gavin joined the board. He's been one of the sharpest voices on AI compute for years.
Alongside them: NEA, Valor, @dylan522p 's SemiAnalysis Capital, Jim Clark, QIA, Arm, Jump Trading, Hudson River Trading and Cisco.
When the people who know this market best write checks this size, we pay attention.
4/ Fast inference is still a luxury - only available on open ai's ultrafast tier
We want to see fast tokens make it to the opensource market. We want to see builders and founders build with fast inference and open up entirely new product categories.
Positron is a key unlock for this happening.
xitter.cf/general_compute/status…
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
Compute & Cocktails 🍸
How do you finance AI infra? @medshelton_ (@GeneralCompute) + Stanley Lee (Liquid Compute) on underwriting compute, getting past prepay and the right debt/equity mix.
📍 Dawn Club, SF · Wed, Oct 28
Request to join → luma.com/g9h2d9ai
S/o to @GoodisonJason for killing it in Singapore last week 🇸🇬
Thanks to @GoldenGateVC and @vlauria for having him at Global Capital Corridors 🤝
xitter.cf/GoodisonJason/status/2…
Center stage for @GoldenGateVC's panel in Singapore last weekend. Thanks for having me @vlauria. Great to be a part of F1 Founders Rally!
AI is the biggest capital deployment in history, and it’s still capital constrained. For alternative silicon, it’s even tighter.
Demand for fast tokens has gone exponential in 6 months. Capital is following it to alternative accelerators. @general_compute is in pole position to deliver this.
ALT image
"Could you imagine if there was only one database, invented in 1970, and nobody built Redis or Kafka?" — @JasonGoodison
Packed room at Fenwick for The Future of Inference. Takeaway: workloads diverge, so silicon should too.
Thanks @Fenwick, @GillVerdon (Extropic), @SambaNovaAI 🍻 #SFTechWeek
$200m per MW is wild
SemiAnalysis numbers imply OpenAI is selling Cerebras-powered Ultrafast inference at ~$200M per megawatt per year.
People are re-running the maths hoping for a typo. We're not surprised.
A megawatt is a megawatt. What it earns depends on the silicon inside it.
Here's how we think about it:
Near term, revenue per megawatt wins. Power is the scarcest input in AI, and whoever monetizes each MW best gets the next MW.
Long term, gross margin per megawatt wins. Prices compress, competitors arrive, and only the operators with real cost and pricing advantages keep the spread.
Cerebras is built for both.
Speed is the product. A frontier model at 750 tokens per second is something GPU clouds haven't publicly matched, and customers like Jane Street pay a premium for it. Premium pricing on a contracted, scarce resource is how you protect margin when everyone else is racing to the bottom.
That's why we're extremely bullish on Cerebras, and why we're buying as many megawatts as we can.
Premium inference unlocks the most delightful user experience in AI. No more waiting, waiting, waiting, staring at little thinking animations while the model works in the background. Answers that keep up with your thoughts.
Our customers are even more excited than we are.
That's General Compute.
🚨 BREAKING: Super excited to announce we're deploying the world's fastest inference with @Cerebras.
Talk to any developer and they're excited to build with 20x faster AI... the problem is there's almost no compute available.
We're here to solve that.
Using GPUs for prefill - it's now more affordable than ever too.
Thank you to the whole Cerebras team and excited to grow this partnership.
First tokens live Q127 🚀
General Compute retweeted
We ran some stats on a GPU Cloud vs General Compute
Both on standalone requests and an IRL agent coding session
The TLDR: @general_compute is much much faster on both
This was using @sambanovaAI's last gen SN40 hardware, so just wait to see how fast the SN50 is
Here's the breakdown based on harness and build
Standalone:
TTFT 8,084ms vs 1,136ms -> 7.1x faster
Decode 121 tok/s vs 1,025 -> 8.5x faster
One request end to end, 34.3s vs 2.1s -> 16.3x faster
The bigger bottleneck at some point for long horizon agents becomes CPU operations and storage access. Which colocating with the accelerators will help solve
General Compute retweeted
Speculative decoding is how most production inference already runs
The question i get from clients isn't whether it works
It's whether it works on ASICs
It does, and the gains are bigger than they are on gpus
The question usually carries an assumption underneath it: that software is what keeps gpus ahead, so better software eventually removes the case for dedicated inference silicon
But the same technique runs on the ASIC, on hardware already built for decode
The gap widens, it doesn't close
xitter.cf/thejessezhang/status/2…
One cool technique to improve latency in LLMs is to have a small, fast model generate tokens and a larger, slower model review the output in one pass.
It'd be like having a junior employee write a report, and then having a senior employee review it in one go before it gets sent to a client.
This is called "speculative decoding" and lets you generate faster while maintaining the output quality.
Great write-up from the @DecagonAI team on how we've been doing this:
General Compute retweeted
A great evening at the New York Stock Exchange last Friday as part of our AI Factories series.
John Furrier an insightful panel discussion with Darren Chien, managing director, APAC, at @positron_ai; and Jason Goodison, CTO of @general_compute.
#theCUBE #NYSEWired
General Compute retweeted
1/ It takes a couple of iterations of physical deployment to figure out chip-market fit. We saw this with Trainium. Gen 1 barely got used; gen 2 actually ran some production workloads, gen 3 is prod-ready.
The difference wasn't a smarter chip, it was the years of customer feedback baked into the next one.
sambanova.ai/blog/sn50-premi…
General Compute retweeted
Full house at the @general_compute + @SambaNovaAI + @pipecat_ai voice agents hackathon at @AGIHouseSF. Thank you also to the @GradiumAI team for supporting with speech-to-text and text-to-speech APIs.
Let me get my popcorn
Time to place your bets — how fast do you think M3 is about to run on @SambaNovaAI 🚀