Life & fun · alternatives · 2026
24 alternatives to Software for Remote GPU-over-IP
We built installable software for Windows & Linux that makes any remote Nvidia GPU accessible to, and shareable across, any number of remote clients running local applications, all over standard networking.
This software allows Windows and Linux users to access and share remote Nvidia GPUs across multiple client machines running local applications over standard networks. The installable tool bridges remote graphics… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Software for Remote GPU-over-IP launched in 2022; newer entries below may have overtaken it.
- 1
We developed a tool to trick your computer into thinking it’s attached to a GPU which actually sits across a network. This allows you to switch the number or type of GPUs you’re using with a single command.
2024 · thundercompute.com · its alternatives →
- 2
Insights on GPU and AI infrastructure from Arc Compute. Read hardware deep-dives, deployment playbooks, and analysis of where the AI compute market is heading.
2021 · arccompute.com · its alternatives →
- 3
Dear HN, I’m excited to show case a personal project. It has helped me quite a bit with my home lab, I hope it can help you with yours too! ffmpeg-over-ip has two components, a server and a client. You can run the server in an environment with access to a GPU and a locally installed version of ffmpeg, the client only needs network access to the server and no GPU or ffmpeg locally. Both and client and the server need a shared filesystem for this to work (so the server can write output to it, and client can read from it). In my usecase, smb works well if your (GPU) server is a windows machine,…
2024 · github.com · its alternatives →
- 4GS
Recently I was inspired by exe.dev to finally finish a project I'd been procrastinating on for half a year. You can simply `ssh gpu.st new` and instantly create instances with a variety of GPUs attached. Instances boot a custom image with PyTorch, Jupyter, and the CUDA toolkit installed by default. Currently we give access to 3090s and 4090s for free users; paid users get access to all GPUs.
Jan 2026 · gpu.st · its alternatives →
- 5
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation. - ymcrcat/rgpu
15d ago · github.com · its alternatives →
- 6
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 →
- 7
Compare GPU rental prices across cloud providers
5d ago · gpu.context.dev · its alternatives →
- 8TC
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 →
- 9
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 →
- 10
CUDA is NVIDIA's language for GPU programming, allowing you to mix write CPU and GPU code in C++ in one file. By chaining a few projects that compile CUDA to OpenCL, then Vulkan, then WebGPU, you can experiment with this GPGPU language on any hardware.
2025 · hipscript.lights0123.com · its alternatives →
- 11
Back in the old days, people used to do general-purpose GPU programming by using shaders like GLSL. This is what inspired NVIDIA (and other companies) to eventually create CUDA (and friends). This is an implementation of GPT-2 using WebGL and shaders. Enjoy!
2025 · github.com · its alternatives →
- 12
[Experimental] Deploy and stream games/apps in the cloud. Use our GPUs or bring your own. - nestrilabs/nestri
2024 · github.com · its alternatives →
- 13
Demo of agent based model on GPU with CUDA and OpenGL (Windows/Linux) Agent instances on GPU memory Uses SSBO for instanced objects (with GLSL 450 shaders) CUDA OpenGL interops Renders with GLFW3 window manager Dynamic camera views in OpenGL (pan,zoom with mouse) Libraries installed using vcpkg (https://github.com/KienTTran/ABMGPU)
2023 · github.com · its alternatives →
- 14
Hi HN, we are Ed, Zach, and Ronald, creators of Shadeform (https://www.shadeform.ai/), a GPU marketplace to see live availability and prices across the GPU market, as well as to deploy and reserve on-demand instances. We have aggregated 8+ GPU providers into a single platform and API, so you can easily provision instances like A100s and H100s where they are available. From our experience working at AWS and Azure, we believe that cloud could evolve from all-encompassing hyperscalers (AWS, Azure, GCP) to specialized clouds for high-performance use cases. After the launch of…
2023 · shadeform.ai · its alternatives →
- 15
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 →
- 16KG
Borrow GPU compute from another device, with permission
Sep 2026 · kanverse-borrow.kanthrfp.workers.dev · its alternatives →
- 17
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 →
- 18
🖥️ P2P Remote Desktop - Portable peer-to-peer remote desktop with no installation required. - miroslavpejic85/p2p
2021 · github.com · its alternatives →
- 19
Janus is a API router for AI models written in Go and has a Vulkan Model runner - Vibra-Ingenn/Janus
20d ago · github.com · its alternatives →
- 20
I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal", "A100") cuda_device = machine.device("cuda") x = torch.randn(1000, 1000, device=cuda_device) y = torch.randn(1000, 1000).to(cuda_device) I made it reasonably performant by having most operations dispatch asynchronously whenever possible. For cases where slow performance is…
2025 · github.com · its alternatives →
- 21
gpu share, free, runsnack
Aug 2026 · runsnack.com · its alternatives →
- 22
CPU & GPU with Zero Hassle Built with NVIDIA Warp, the same code runs seamlessly on both CPU and GPU — no need to deal with CUDA setup, driver issues, or device-specific kernels. Just flip one config line. Learn Modern Graphics the Easy Way Explore core concepts in differentiable rendering and parallel graphics programming — no need for expensive GPUs or thousands of lines of boilerplate. Minimalist & Educational This isn’t another massive codebase. It’s a clean, hackable implementation built for clarity — perfect for study, prototyping, or teaching yourself how Gaussian Splatting works.
2025 · github.com · its alternatives →
- 23GB
2016 · paperspace.com · its alternatives →
- 24
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 →
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- Gpu.st – GPU over SSH alternatives
- Rgpu – a PyTorch device whose tensors live on a remote GPU 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 →