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Self-Hosted LLM: Essential Llama vs Cloud TCO Guide

This story is from 2026-09-24. It is preserved in the archive; the latest stories are on the live feed.

Usage-based cloud APIs make experimentation easy, but costs can rise quickly once an application reaches production scale. A self-hosted LLM replaces per-token fees with infrastructure, operations, and energy expenses. The correct choice depends on utilization, latency, privacy requirements, and th…

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  1. 2026-09-24 02:29 · DEV Community — AI
    Self-Hosted LLM: Essential Llama vs Cloud TCO Guide

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  1. Transformers now runs llama.cpp quants — Hugging Face Blog
  2. yandex/AliceAI-Foundation-80B-A3B-Base: Russian-developed competitor to Qwen 35B and DeepSeek V4 Flash — r/LocalLLaMA
  3. I trained a 360M-param Python model from scratch on two workstation GPUs and wrote up every step, including the bugs — r/learnmachinelearning
  4. I turned Qwen3.8-27B Q2_64 + llama.cpp into a fully TypeSafe AI-compatible Jev-like system. OpenAI API still intact! World’s first Vision-enabled Jev-like model! <10 GB VRAM, 170 ms on an RTX 3090 and ~140 tok/s in chat. 76% vs. 88% Jev-1.13 Acc. on a diverse 22,000-request typed-decision benchmark — r/LocalLLaMA
  5. GGUFs in transformers natively! — r/LocalLLaMA
  6. 2× Tesla P100 (2016 cards) in 2026: 110 tok/s on a 30B MoE, 16 tok/s at 1M context — r/LocalLLM
  7. Dual B60 24GB Performance — r/LocalLLM
  8. My contribution to the local AI community: 9 abliterated models, 99 GGUF quantizations in progress — r/huggingface

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