Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor. Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon.

Imagine if everyone had the tools to shape intelligence itself. That day is today. Today we launch the Trajectory platform - the easiest and most intuitive tool to post-train models of your own. The foundation for continual learning. With just a few agent prompts, you get access to a full creative suite to train, eval, and deploy trillion parameter models. On top of that, we believe in a future where you don’t just shape your model once, it also keeps learning on its own from the world around it. We’re releasing two research previews. 1. Continual grading scales your taste, so your model learns what good looks like from far more than you could ever grade by hand. 2. GS1, a new algorithm that lets models learn directly from what they do in production. We’ve now raised 60M, with a new round led by @Sequoia at a 300M valuation to democratize the most powerful technology of today - training models - so that the lawyers, doctors, and engineers of the world can own their own intelligence. You know what good looks like, now go build it. Own your own value function with Trajectory.

1/4 New writeup: you can fingerprint the exact GPU you're running on with a default torch.index_add_ call, even among identical GPUs. This works because floating-point maths depends on order, and every GPU has its own. This fingerprint survives reboots and can't be faked.

inference engines should be bending over backwards more to get cache hits really interesting direction: > An Internet for the KV Cache - sending a KV cache across the Internet can now be faster and cheaper than computing it again.

I'm doing DP. I'm doing TP. I'm doing SP. I'm doing PP. I'm doing ZP. I'm doing FSDP. I'm doing 1F1B. I'm doing CP. I'm doing ETP. I'm doing DPA. I'm doing VPP. I'm doing P/D. I'm doing ZBPP. I'm doing DPA. I'm doing HSDP. I'm doing DCP. I'm doing DWDP. I'm doing AFD.

💭WHY IS DEEPINFER NOT ON INFERENCEX? DeepSeek dropped DeepInfer a few days ago, but the code is missing key components that is crucial for inference serving, and building them adds too much assumptions. (1/5)🧵

Training the model to detect when it is in training vs. deployment works too! The model can even generalize from vLLM deployment training to SGLang deployment. We do this by recording activations from layer 34 in Hugging Face Trainer's training step and in vLLM's compiled serving. Then, we swap in the attention and residual stream from the Hugging Face copy and SFT the model to predict "Yes," and to predict "No" when we swap in from vLLM instead. The model learns how its training vs. deployment environment affects its first 34 layers.

Dimuon is a new optimizer we built for LoRA fine-tuning. In our SFT tests, it often reaches lower loss in fewer steps than AdamW. We're still experimenting with Dimuon; you can Tinker with it today and share your feedback! See our optimizers guide at

Tinkerers have been busy scaling up long-context RL! We’ve made significant improvements to Tinker’s efficiency to support those, and are passing these on with price cuts up to 70%. GLM-5.3-Flash and DeepSeek-v4.1-Flash are also live for cost-efficient long-context work.

Guys, did you see the inference price war for open weight models in openrouter? It’s bloody war to compete on the lowest price. Reminds me of electricity. I keep hearing inference is huge margin but then it seems it’s a war to zero at openrouter nowadays. I finally understand why openrouter charges 5.5%. I keep hearing stories of data centers in Finland, Philippines and Southeast Asia that are just cheap, cheaper and cheapest? I wonder how US neoclouds cope with this? I mean enterprise is a good market I’m sure.