Tensorant @tensorant
Your gateway to open-weight model training and inference. tensorant.io Joined October 2026-
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A lot of "expensive" fine-tunes are just a run that died at hour 2 while the pod kept billing till morning 💀 On Tensorant every run gets a time limit and an hourly price cap, and the GPU shuts down the moment it ends, fails or gets cancelled. Hard caps should be the default.
IBM's CEO says open-weight models now do 90% of the job at 10% of the cost. The training bill should follow the same math. On Tensorant your GPUs run in your own RunPod, Lambda or AWS account at their price, with no markup from us.
Most bad fine-tunes I've seen trace back to the dataset, not the training config. On Tensorant every generated example cites the passage it came from. Missing evidence, repeats and personal data get flagged, and you approve each one beside its source before a GPU starts.
GRPO is only as good as the checker you give it. That's why RL on Tensorant uses rule-based rewards you can actually read, and the tuned model gets scored against the base on the same held-out set before you trust it. A vague reward just teaches the model to game it.
A lot of fine-tuned models get a handful of calls a day. Keeping a GPU on 24/7 for that is how a small part of me dies. On Tensorant you can publish a model as wake-on-request. It costs nothing while asleep and wakes up when a call comes in. Same OpenAI-compatible API.
@rotaruzaurul @gpelluet27 Nice, would love to see what you built. My DMs are open.
BOOM! I trained my own judge on the Jev datasets. It beat Jev 1.14 🔥 Open 4B, 22 min on one NVIDIA H200 on Hugging Face Jobs, $1.87 4,035 held-out RAG + safety calls: Jev 1.14: 82.2% my 4B: 89.4% Twist: on PubMed answers it never saw, Jev wins. Unless it learns from Jev 👀
Every fine-tuning platform should let you walk away with your weights. On Tensorant the adapter downloads as a standard LoRA file that loads with PEFT, or goes to your own Hugging Face account, private by default. If you can't export it, you're renting it.
A lot of "expensive fine-tunes" are just a run that crashed at hour 2 while the pod kept billing till morning. On Tensorant the GPU shuts down the moment a run ends, fails or gets cancelled. Every run also gets a time limit and an hourly price cap. Hard spending caps should be the default.
@Shreyansh_327 Love this. Next time, you can use Tensorant for a better overall end-to-end UX.
@WOkparaji74619 Geology is a fun one, so much jargon base models only half know. Are you building the data from internal reports or papers?
@adrian_spataru Would you like to give our platform a try? We are building Tensorant to solve exactly this.
RL is the ultimate final boss.
We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper. It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2
Sometimes the model already knows the answer, it just says it wrong. too long, too rude, wrong format. That's where preference training helps. Same prompt, preferred vs rejected reply. Tensorant now supports DPO. tensorant.io
@Contrasignal_ai Would love to get your thoughts on tensorant.io. It can help you streamline your fine-tuning workflow.
@6ixsinz Do check out our platform, we support RunPod too. You choose the dataset, base model and GPUs and the platform takes care of the rest.
@itsvaruntej Hey, you should checkout our platform. We are solving this exact issue.
Tell us in comments which model you are fine-tuning and for what use-case and we will give out 1 month of Tensorant Pro subscription for FREE! 👇
@tomaarsen And Tensorant is the platform to do so..
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