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LLMKube Alternatives
Similar projects and alternatives to LLMKube
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AppSignal
Monitoring that respects your time & budget. APM, error tracking, and dashboards for modern web apps. Ten-minute setup, transparent flat pricing, and support from engineers who actually use the product.
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OGAM
The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. Runs on your CPU, GPU, or NPU. No account, no API key, zero data leaves your device.
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aqueduct
Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure. (by RunLLM)
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triton-server-hpa
Autoscale NVIDIA Triton on Kubernetes by GPU utilisation — DCGM, Prometheus adapter, HPA, GPU time-slicing. Tested end-to-end.
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tensor-fusion
Tensor Fusion is a state-of-the-art GPU virtualization and pooling solution designed to optimize GPU cluster utilization to its fullest potential.
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LLMKube
Discontinued Kubernetes operator for GPU-accelerated LLM inference - air-gapped, edge-native, production-ready [Moved to: https://github.com/defilantech/LLMKube] (by Defilan)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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off-grid-mobile
Discontinued The Swiss Army Knife of Offline AI. Chat, Speak, and Generate Images - Privacy First, Zero Internet. Download an LLM and use it on your mobile device. No data ever leaves your phone. Supports text-to-text, vision, text-to-image [Moved to: https://github.com/alichherawalla/off-grid-mobile-ai]
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infercost
Kubernetes-native cost intelligence for on-premises AI inference. Computes true cost-per-token from GPU amortization, electricity, and real power draw.
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Qwen-3.5-16G-Vram-Local
Discontinued Configs, launchers, benchmarks, and tooling for running Qwen3.5 GGUF models locally with llama.cpp on a 16GB NVIDIA GPU [GET https://api.github.com/repos/willbnu/Qwen-3.5-16G-Vram-Local: 404 - Not Found // See: https://docs.github.com/rest]
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llama-turboquant
TurboQuant for GGML: 4.57x KV Cache Compression with 72K+ Context for Llama-3.3-70B on Consumer GPUs.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better LLMKube alternative or higher similarity.
LLMKube discussion
LLMKube reviews and mentions
Posts with mentions or reviews of LLMKube.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-09-28.
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A weekend with TensorFold on a MacBook: the engine mattered, the quant did not
Then I reviewed all nine by hand, because a green gate is not a review. Two of the nine had real defects the gate could not see. One draft hardcoded a test port, so its tests failed whenever something else on the machine was using it. Another sized a relative file path against the agent's working directory. Each quant ended up writing the version I picked for one issue, and those three are open as pull requests #1928, #1929 and #1930. Three issues is a small sample, and I would not claim the quants are equal on harder work. What I can say is that on real issues in this repo, the choice of quant did not change the outcome, and the choice of engine changed the wait by about 3x.
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Every release makes the harness harder to fool: LLMKube 0.9.19
Full changelog: v0.9.19 on GitHub. Every issue and PR referenced here is public in the repo, task records and all. LLMKube is Apache 2.0.
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One GPU, four ways to share it: ten scenarios, and the one number that inverts on your hardware
So I took two DGX Sparks and ran ten scenarios across every sharing mechanism LLMKube supports. The headline result turned out to be a property of the hardware, not the software, and it reverses on the GPU most of you actually own. That control is the part I would not skip.
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Foreman 101: agentic coding as Kubernetes resources
The docs are at llmkube.com/docs/foreman and the code is on GitHub. Both are Apache 2.0.
- LLMKube – A Kubernetes operator for local LLMs across Nvidia and Mac fleets
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LLMKube Now Deploys Any Inference Engine, Not Just llama.cpp
GitHub: https://github.com/defilantech/llmkube
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I tested speculative decoding on my home GPU cluster. Here's why it didn't help.
LLMKube is open source, Apache 2.0: github.com/defilantech/llmkube
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Google Released Gemma 4 Yesterday. I Had It Fixing Real Bugs by Lunch.
Everything is managed by LLMKube, a Kubernetes operator I built for running llama.cpp inference. One CRD to define the model, one CRD to define the service, the operator handles the rest.
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I Tested TurboQuant KV Cache Compression on Consumer GPUs. Here's What Actually Happened.
All the benchmarking infrastructure is in the LLMKube repo. The operator is open source (Apache 2.0) and handles the full lifecycle: model downloads, GPU scheduling, multi-GPU sharding, health probes, and Prometheus metrics. If you have a GPU cluster and want to test TurboQuant:
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The $0 Problem: Why Every Tool Says Your On-Prem Inference is Free
Companion project: LLMKube (K8s operator for LLM inference)
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A note from our sponsor - SaaSHub
www.saashub.com | 10 Oct 2026
Stats
Basic LLMKube repo stats
12
231
9.9
3 days ago
defilantech/LLMKube is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of LLMKube is Go.
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