LLMOne – Deploy LLMs from bare metal to production in hours
I spent days trying to deploy DeepSeek on a server this year. Install Ubuntu, NVIDIA drivers, CUDA, Docker, configure vLLM, debug memory issues, tune performance settings. Every deployment was different. Every server had its own quirks. Worse still, these issues are more pronounced on non-NVIDIA accelerators, such as Ascend or Intel NPU. So, we made LLMOne, which will automates this. You can use it at bare metal (via BMC) or SSH (coming soon) into an existing server, select models, and it handles everything: OS installation, driver setup, inference engine configuration, model deployment, and…
LLMOne – Deploy LLMs from bare metal to production in hours launched on June 22, 2025 with 5 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Enterprise-grade LLM automated deployment tool that makes AI servers truly "plug-and-play". - EM-GeekLab/LLMOne
In the maker’s words, at launch
I spent days trying to deploy DeepSeek on a server this year. Install Ubuntu, NVIDIA drivers, CUDA, Docker, configure vLLM, debug memory issues, tune performance settings. Every deployment was different. Every server had its own quirks. Worse still, these issues are more pronounced on non-NVIDIA accelerators, such as…
Does a similar job
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Tune LLaMa3.1 on Google Cloud TPUs2024 · github.com · ▲189Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…
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LLMKube – Kubernetes for Local LLMs with GPU AccelerationNov 2025 · github.com · ▲5Hi HN! I built LLMKube, a Kubernetes operator for deploying GPU-accelerated LLMs in production. One command gets you from zero to inference with full observability. Why this exists: Regulated industries (healthcare, defense, finance) need air-gapped LLM deployments, but existing tools are either single-node only (Ollama) or lack GPU optimization and SLO enforcement. LLMKube bridges the gap. What's working: - 17x speedup with NVIDIA GPUs (64 tok/s on Llama 3.2 3B vs 4.6 tok/s CPU) - One command: llmkube deploy llama-3b --gpu (auto CUDA setup, scheduling, layer offloading) -…
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