Thrilled to announce the general availability of @positron_ai's Atlas Inference Appliance on @OracleCloud, making Positron the first AI chip startup to be deployed at scale with a hyperscaler. Look out for more announcements this week at Oracle Cloud World! blogs.oracle.com/cloud-infra…

@dreamlabs_ai is hiring! We have great opportunities across research, engineering and operations. I'm focused on hiring for two roles now: (1) robot learning researcher and (2) security & IT systems engineer. If interested please reach out directly or careers@dreamlabs.ai.

There is an absolute barrage of inference providers popping up that get to $1M/mo extremely quickly. This is the rough economics of inference providers: Even if you rent on-demand B300s at $8/GPU-hr, many open source models (GLM 5.3, Kimi K3, DeepSeek 4.1 Flash / Pro) can get you >$10-20/GPU-hr at reasonable throughputs (100tok/s). DeepInfer from DeepSeek directly shows they can get $38/GPU-hr on off-peak hours. Every chip / model has a different throughput / interactivity curve, but there is a lot of arbitrage between even high rental spot prices and token prices. New chips (Vera Rubins) also change this meaningfully. Your ability to be making gross margin relies on your utilization and how good your stack is. Gateways like OpenRouter, Vercel and more help solve that problem by aggregating demand to you. May inference providers discount their tokens to get longer term commits too. I've even heard stories of selling back Chinese model tokens to Chinese labs! One thorn in the economics of these players is that many Chinese open source model labs are now asking for rev share if you're at a certain scale, espp for pre-release access to their models for day 1 support. That further pushes the threshold to be in the money. The barrier to entry seems low and falling. There are even stories of smaller ones who can "vibe code" a way to stand up OSS models on a cluster they rent. And as a result, we're seeing 10s of such companies pop up almost every week and immediately skyrocketing revenue at low-ish gross margin and raising rounds on the back of that. Unsurprisingly, because the actual service provided seems commodity, this eventually drives up compute prices and drives down token prices. In theory, it would seem like neoclouds who have the lowest cost of compute and themselves have started inference businesses would win the race to the bottom (Crusoe Coreweave Nebius and GMI all have inference businesses), with larger players (Modal Fireworks Baseten) being second because they are well capitalized to make bigger compute commits cheaply and have demand (high utilization), but the reality is the pie is growing so quickly that even upstarts are absolutely crushing it and growing aggressively (inference dot net, DeepInfra, Wafer, Relace, Morph). Strategies vary across the board: neoclouds want to serve inference, inference players want to be neoclouds and do higher margin reserved instances / post training, and some want to go heterogeneous hardware or creatively flexible compute constructs. The risks are: do open source models continue to be competitive? Will their creators rent seek more? Will their shape change compute needs? How do different chips change economics? Who gets compute in a supply crunch? Below is a aggressive sketch of how gross margin scales with utilization for various models / chip sets. For now, there are many inference providers and they’re all ripping!

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We accelerated one of the most optimized NVFP4 decode kernels while also making it more accurate Fewer bits typically mean more speed but less accuracy. We did the opposite: more bits, but up to 10% faster generation Already used in Ampersand’s RL and Boba serving

No one changes the world alone Introducing Ampersand - the first model built for us. Ampersand is a collaborator trained to help people achieve their individual and collective goals. In multi-month simulations, users collaborating with Ampersand accomplished more than with Fable and other frontier models Available to everyone through our API

We accelerated one of the most optimized NVFP4 decode kernels while also making it more accurate Fewer bits typically mean more speed but less accuracy. We did the opposite: more bits, but up to 10% faster generation Already used in Ampersand’s RL and Boba serving

This is pretty nice, our kernel increases both accuracy and speed at the same time and is open source available freely for everyone to use - our way of giving thanks to the open community that we have benefited from ♥️

We accelerated one of the most optimized NVFP4 decode kernels while also making it more accurate Fewer bits typically mean more speed but less accuracy. We did the opposite: more bits, but up to 10% faster generation Already used in Ampersand’s RL and Boba serving

We accelerated one of the most optimized NVFP4 decode kernels while also making it more accurate Fewer bits typically mean more speed but less accuracy. We did the opposite: more bits, but up to 10% faster generation Already used in Ampersand’s RL and Boba serving

98%+ Tensor Core utilization btw

We accelerated one of the most optimized NVFP4 decode kernels while also making it more accurate Fewer bits typically mean more speed but less accuracy. We did the opposite: more bits, but up to 10% faster generation Already used in Ampersand’s RL and Boba serving

Introducing Boba - the social intelligence that understands your goals and your community. Text Boba today or message it online at boba.app

We built Boba because we believe that people change the world together. Boba is powered by Ampersand, our model trained to help people achieve their goals. By practicing over millions of simulated days and interacting with thousands of simulated people, it learns to navigate goals, disagreements, and changing plans boba.humansand.ai/blog

Introducing Boba - the social intelligence that understands your goals and your community. Text Boba today or message it online at boba.app