Alternatives
Products that do what VMetal – run a GPU cloud on bare metal without OpenStack does
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…
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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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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
Oct 2025 · github.com
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2017 · github.com
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I wanted to see how fast an isolated code sandbox could start if I never had to boot a fresh VM. So instead of launching a new microVM per execution, I boot Firecracker once with Python and numpy already loaded, then snapshot the full VM state. Every execution after that creates a new KVM VM backed by a `MAP_PRIVATE` mapping of the snapshot memory, so Linux gives me copy-on-write pages automatically. That means each sandbox starts from an already-running Python process inside a real VM, runs the code, and exits. These are real KVM VMs, not containers: separate guest kernel, separate guest…
Mar 2026 · github.com
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Jun 2026 · github.com
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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
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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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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
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I've long wanted to harness QEMU/KVM for my desktop virtual machines, but I'm befuddled by virt-manager's lack of support for working NVIDIA 3D acceleration, dogmatic embrace of ugly XML, and the puzzling UI decision of having to click what seems like 15 buttons to attach an ISO to a VM image. When I further learned that NVIDIA's broken 3D acceleration is the fault of libvirt as opposed to QEMU's virtio driver, I had an idea... Behold, vm-curator! A fast and friendly VM management TUI written in Rust. You can create, configure, organize, and manage VMs directly with QEMU. No libvert. No…
Jan 2026 · github.com
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I got tired of libvirt XML and Vagrant's Ruby/reload dance for single-host VM stacks, so I built a compose-style runtime directly on QEMU/KVM. What's there: GPU passthrough as a first-class primitive (VFIO, OVMF, per-instance EFI vars), healthchecks that gate depends_on over SSH, socket-multicast L2 between VMs with no root and no bridge config, cloud-init wired through the YAML, Dockerfile support for provisioning. What it's not: Kubernetes. No clustering, no live migration, no control plane. Single host. Prototype, but I'm running it on real hardware. Curious what breaks for…
Apr 2026 · github.com
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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
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2017 · github.com
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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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A fast, rootless sandbox and virtual resource runtime for any workload, including untrusted and AI-generated code. Daemonless: a real, kernel-enforced container in ~3.5 ms from an OCI image, out of one static binary - getkern/kern
13d ago · github.com
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Hello HN! We've been working on a new hypervisor https://kwarantine.xyz that can run strongly isolated containers. This is still a WIP, but we wanted to give the community an idea about our approach, its benefits, and various use cases it unlocks. Today, VMs are used to host containers, and make up for the lack of strong security as well as kernel isolation in containers. This work adds this missing security piece in containers. We plan on launching a free private beta soon. Meanwhile, we'd deeply appreciate any feedback, and happy to answer any questions here or on our slack…
2021
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Jul 2026 · github.com
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Hi, I’m Nabeel. In August I released RunMat as an open-source runtime for MATLAB code that was already much faster than GNU Octave on the workloads I tried. https://news.ycombinator.com/item?id=44972919 Since then, I’ve taken it further with RunMat Accelerate: the runtime now automatically fuses operations and routes work between CPU and GPU. You write MATLAB-style code, and RunMat runs your computation across CPUs and GPUs for speed. No CUDA, no kernel code. Under the hood, it builds a graph of your array math, fuses long chains into a few kernels, keeps data on the GPU when…
Dec 2025 · github.com
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Bhatti spins up Linux VMs on any box with KVM — Pi 5, Hetzner AX, cloud VM with nested virt. - Each VM has its own kernel, filesystem, and IP - Idle VMs pause their CPUs and snapshot themselves to disk; the next request wakes them in 3.7ms warm or 360ms cold (p50, Hetzner AX102) - Publish any port → public URL with auto-wake on first hit - Pull any OCI/Docker image as a rootfs, or save a running sandbox as one - Multi-tenant from day one — per-user bridges, encrypted secrets, rate limits - Single Go binary, Apache 2.0 The decisions page is the most fun read on the site: vsock state…
May 2026 · bhatti.sh
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Over the past few months, as we scaled our internal AI Agents, we hit a dead end: Running LLM-generated arbitrary code in Docker is basically running naked on security due to container escape risks. But using full traditional VMs takes minutes to boot and eats too much memory to support high-density concurrency. We loved the developer experience of SaaS sandboxes on the market, but they are closed-source, expensive, and have too high a barrier to entry for self-hosting. So, our team decided to build our own. After months of grinding, using RustVMM and KVM, we built a blazing-fast,…
Apr 2026 · github.com
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