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AI · alternatives · 2026

24 alternatives to Smeltcore

Which AI models actually run on your GPU

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    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 →

  2. 2
    RunInfra▲156

    Describe the AI model you need and get an optimized AI

    Jul 2026 · runinfra.ai · its alternatives →

  3. 3

    AI models that run on an inference cloud optimized for speed

    May 2026 · generalcompute.com · its alternatives →

  4. 4IM
  5. 5

    The world’s most powerful chip’ for AI

    2024 · nvidia.com · its alternatives →

  6. 6

    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 →

  7. 7

    Janus is a API router for AI models written in Go and has a Vulkan Model runner - Vibra-Ingenn/Janus

    21d ago · github.com · its alternatives →

  8. 8PO

    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 →

  9. 9WM

    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 →

  10. 10TC

    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 →

  11. 11
    GPU.LAND▲126

    Affordable cloud GPUs for deep learning

    2021 · vedge.cloud · its alternatives →

  12. 12

    Hi HN, I built PantheonGPU because I wanted a better way to answer a simple question: is this GPU actually healthy and performing the way it should? A GPU can show normal temperatures and utilization and still be underperforming, unstable under certain workloads, or have memory, PCIe, or configuration issues. PantheonGPU actively tests the GPU instead of only monitoring telemetry. It currently includes 45+ tests covering compute, tensor workloads, memory, cache, PCIe, thermals, stability, and AI/LLM inference. It supports both NVIDIA CUDA and AMD ROCm. I’m also exploring a larger use…

    Aug 2026 · pantheongpu.com · its alternatives →

  13. 13

    Accelerate Your Workloads with GPUs

    2025 · cyfuture.ai · its alternatives →

  14. 14

    Self-host AI/ML with the world's cheapest GPU cloud

    2025 · thundercompute.com · its alternatives →

  15. 15

    AI-Native Data Infrastructure for Spatial and Physical AI

    Apr 2026 · zibra.ai · its alternatives →

  16. 16

    Calculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization - RahulSChand/gpu_poor

    2023 · github.com · its alternatives →

  17. 17

    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 →

  18. 18

    Run OpenClaw locally on your AMD PC

    Mar 2026 · its alternatives →

  19. 19

    We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…

    2025 · github.com · its alternatives →

  20. 20

    Know which GPU runs your LLM, validated on real hardware

    Aug 2026 · gpu-sizer.com · its alternatives →

  21. 21

    Free tool to check if your GPU can run local LLMs.

    Jul 2026 · llmconfigurator.com · its alternatives →

  22. 22

    Hi there, looking for feedback on my new project "Featherless.AI" The idea is to allow users to run all the models on hugging face instantly. Via the OpenAI API compatible endpoint. Why? Because its a real chore to download models and spin up GPUs, especially if you want to test multiple models. Not to mention GPUs cost multiple dollars an hour to rent. And if we want more people to use open source AI, we got to make it easier for them to try and play with all of them. So what if instead of spinning up dedicated GPUs per model (which is what every provider is doing) We can startup a LLM…

    2024 · featherless.ai · its alternatives →

  23. 23

    Know Your AI Performance Before You Run It.

    Feb 2026 · localops.tech · its alternatives →

  24. 24

    Testing which AI models your hardware really creates

    2025 · keithub.gumroad.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 →