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Products that do what VaultLayer does
Reliable AI training on affordable GPUs
- 1HI
I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants. The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole. The whole thing was developed on 2x RTX 4090s in my basement. I'm…
Mar 2026 · dnhkng.github.io
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The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com
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Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
2022 · tensordock.com
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Hi everyone, I'm kinda involved in some retrogaming and with some experiments I ran into the following question: "It would be possible to run transformer models bypassing the cpu/ram, connecting the gpu to the nvme?" This is the result of that question itself and some weekend vibecoding (it has the linked library repository in the readme as well), it seems to work, even on consumer gpus, it should work better on professional ones tho
Feb 2026 · github.com
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Hi HN, YC w24 company here. We just pivoted from drone delivery to build gpudeploy.com, a website that routes on-demand traffic for GPU instances to idle compute resources. The experience is similar to lambda labs, which we’ve really enjoyed for training our robotics models, but their GPUs are never available for on-demand. We are also trying to make it more no-nonsense (no hidden fees, no H100 behind “contact sales”, etc.). The tech to make this work is actually kind of nifty, we may do an in-depth HN post on that soon. Right now, we have H100s, a few RTX 4090s and a GTX 1080 Ti online.…
2024 · gpudeploy.com
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Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
2023 · lepton.ai
- 13TL
Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…
2024 · github.com
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Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s. sllm lets you join a cohort of developers sharing a dedicated node. You reserve a spot with your card, and nobody is charged until the cohort fills. Prices start at $5/mo for smaller models. The LLMs are completely private (we don't log any traffic). The API is OpenAI-compatible (we run vLLM), so you just swap the base URL. Currently offering a few models.
Apr 2026 · sllm.cloud
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Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat
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Hi HN! We’re TensorPool. We help companies access and optimize large scale compute for training foundation models. The Problem It’s been almost a year since we’ve finished YC, and we’ve just crossed 100,000 multinode training GPU hours run on our platform. On those training runs, we’ve seen countless 3am job crashes because of issues like an Xid error from a flaky GPU or an S3 timeout that corrupted a checkpoint save. By the time you wake up and notice, you've lost 8+ hours of compute. You scramble to diagnose the issue, manually restart from the last checkpoint, and hope it doesn't happen…
Jan 2026 · docs.tensorpool.dev
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Our company Vertex.AI has been working on this for a while but this is the first public release. We're starting with using PlaidML to bring OpenCL support to Keras and more frameworks, platforms, etc are coming. Yes, this means you can use use your AMD GPU for deep learning dev. Sorry, no Mac or Windows support yet although the brave can try building from source (it should work). http://vertex.ai/blog/announcing-plaidml https://github.com/plaidml/plaidml
2017
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Tabs, splits, and tmux work fine until you have several projects open with logs, tests, and long-running shells. I kept rebuilding context instead of resuming work. Horizon puts shells on an infinite canvas. You can arrange them into workspaces and reopen later with layout, scrollback, and history intact. Built in 3 days with Claude/Codex, dogfooding the workflow as I went. Feedback and contributions welcome.
Mar 2026 · github.com
- 20OS
Hi I'm Dan from Elodin, making an open source real-time capable flight software simulation. For AI Grand Prix contestants, the wait for the Round 1 virtual qualifier simulation has been grueling. If you’re competing, check out our simulation harness to tide you over, built to match the published competition constraints and message format. It runs against real Betaflight, which we learned requires at least 1000 sensor samples per second to run real-time correctly. The competition warranted introducing a new feature to generate the camera sensor directly in the simulation loop. Typically…
May 2026 · elodin.systems
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100% bootstrapped new startup. It lets you fine tune Mistral-7B and SDXL. In particular, for the LLM fine tuning we implemented a dataprep pipeline that turns websites/pdfs/doc files into question-answer pairs for training the small LLM using an big LLM. It includes a GPU scheduler that can do finegrained GPU memory scheduling (Kubernetes can only do whole-GPU, we do it per-GB of GPU memory to pack both inference and fine tuning jobs into the same fleet) to fit model instances into GPU memory to optimally trade off user facing latency with GPU memory utilization It's a pretty…
2023 · docs.helix.ml
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