Dev tools · alternatives · 2026

24 alternatives to VMetal – run a GPU cloud on bare metal without OpenStack

Hi everyone — looking for feedback on a new infrastructure project we launched called vMetal. It's a bare metal management platform for GPU clusters that handles machine discovery, PXE booting, and lifecycle management, without the OpenStack complexity. Built around Kubernetes-native workflows so you can hand it off to teams or drop it into an existing platform. A lot of the infra platforms used for this today were designed 20 years ago (VMware, OpenStack, NVIDIA BCM, MAAS, etc.), while newer tools usually solve only a small piece of the stack. Neither were built with modern GPU cluster ops…

VMetal is a bare metal management platform designed for GPU clusters that handles machine discovery, PXE booting, and lifecycle management without OpenStack. Built around Kubernetes-native workflows, it lets teams… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Cerebrium▲300
If you haven’t noticed, GPUs are scarce right now. The global boom in inference has demand growing faster than supply can keep up, and at Cerebrium ...
2024 · cerebrium.ai · its alternatives →
2WM
We made glhf.chat – run almost any open-source LLM, including 405B▲161
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 · its alternatives →
3
Dstack – an open-source engine for running GPU workloads▲101
dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
2024 · dstack.ai · its alternatives →
4
Dockerized GPU Deep Learning Solution (Code and Blog and TensorFlow Demo)▲80
An on-premises, bare-metal solution for deploying GPU-powered applications in containers - emergingstack/es-dev-stack
2016 · github.com · its alternatives →
5
Parallax by Gradient▲334
Host LLMs across devices sharing GPU to make your AI go brrr
Oct 2025 · github.com · its alternatives →
6
Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr▲193
Greetings HN! This is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! https://oblivus.com/cloud - What sets Oblivus Cloud apart? At the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be…
2023 · oblivus.com · its alternatives →
7
Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPU▲395
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 · its alternatives →
8
Slim – Build and run tiny VMs from Dockerfiles▲389
Build and run tiny vms from Dockerfiles. Small and sleek. - ottomatica/slim
2019 · github.com · its alternatives →
9TC
TensorDock Core GPU Cloud – GPU servers from $0.29/hr▲147
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 · its alternatives →
10CG
Cloud GPUs for Deep Learning – At 1/3 the Cost of AWS/GCP▲168
vEdge is a GPU cloud compute platform making computing affordable and accessible.
2021 · gpu.land · its alternatives →
11
Katakate – Dozens of VMs per node for safe code exec▲124
I've built this to make it easy to host your own infra for lightweight VMs at large scale. Intended for exec of AI-generated code, for CICD runners, or for off-chain AI DApps. Mainly to avoid Docker-in-Docker dangers and mess. Super easy to use with CLI / Python SDK, friendly to AI engs who usually don't like to mess with VM orchestration and networking too much. Defense-in-depth philosophy. Would love to get feedback (and contributors: clear & exciting roadmap!), thx
2025 · github.com · its alternatives →
12
A 10KiB kernel for cloud apps▲68
BareMetal for the private/public cloud. Contribute to ReturnInfinity/BareMetal-Cloud development by creating an account on GitHub.
Jan 2026 · github.com · its alternatives →
13
Run globally distributed full-stack apps on high-performance MicroVMs▲98
Hi HN! We’re Yann, Edouard, and Bastien from Koyeb (https://www.koyeb.com/). We’re building a platform to let you deploy full-stack apps on high-performance hardware around the world, with zero configuration. We provide a “global serverless feeling”, without the hassle of re-writing all your apps or managing k8s complexity [1]. We built Scaleway, a cloud service provider where we designed ARM servers and provided them as cloud servers. During our time there, we saw customers struggle with the same issues while trying to deploy full-stack applications and APIs resiliently. As…
2023 · koyeb.com · its alternatives →
14
IOMETE▲85
The First Cloud-Prem Data Lakehouse
2023 · iomete.com · its alternatives →
15
RunInfra▲156
Describe the AI model you need and get an optimized AI
Jul 2026 · runinfra.ai · its alternatives →
16
Luminal – Open-source, search-based GPU compiler▲153
Hi HN, I’m Joe. My friends Matthew, Jake and I are building Luminal (https://luminalai.com/), a GPU compiler for automatically generating fast GPU kernels for AI models. It uses search-based compilation to achieve high performance. We take high level model code, like you'd have in PyTorch, and generate very fast GPU code. We do that without using LLMs or AI - rather, we pose it as a search problem. Our compiler builds a search space, generates millions of possible kernels, and then searches through it to minimize runtime. You can try out a demo in `demos/matmul` on mac to…
2025 · github.com · its alternatives →
17
Local fine tuning for Mistral and SDXL, GPU mem/latency optimization▲36
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 · its alternatives →
18
cuTile Rust: Safe, data-race-free GPU kernels in Rust▲106
cuTile Rust provides a safe, tile-based kernel programming DSL for the Rust programming language. It features a safe host-side API for passing tensors to asynchronously executed kernel functions. - NVlabs/cutile-rs
Jun 2026 · github.com · its alternatives →
19
Utilyze – an open source GPU monitoring tool more accurate than nvtop▲128
The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…
Apr 2026 · systalyze.com · its alternatives →
20
Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload▲22
Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…
Jul 2026 · github.com · its alternatives →
21
Open-source real time data framework for LLM applications▲92
Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai · its alternatives →
22PO
PlaidML, open source deep learning for any GPU▲52
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 · its alternatives →
23
Koyeb – Simple serverless processing workflows, on any cloud▲66
Deploy intensive applications across GPUs, CPUs, and Accelerators in minutes - scale in 50+ locations.
2020 · koyeb.com · its alternatives →
24
krunvm – Create and run lightweight VMs from OCI images▲82
Create microVMs from OCI images. Contribute to slp/krunvm development by creating an account on GitHub.
2021 · github.com · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →