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Self-Hosted LLM: Essential TCO Guide for Llama Costs

This story is from 2026-09-25. It is preserved in the archive; the latest stories are on the live feed.

A self-hosted LLM can deliver stronger data control and predictable operating costs—but only when utilization justifies the infrastructure. Cloud APIs minimize upfront investment, while private Llama deployments shift spending toward hardware, engineering, power, and lifecycle management. The right…

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  1. 2026-09-25 17:04 · DEV Community — AI
    Self-Hosted LLM: Essential TCO Guide for Llama Costs

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  1. Qwen3.8-Flash-Next on 12GB VRAM - 65 tokens per second — r/LocalLLaMA
  2. model : add Ling 3.0 VL support by aetherbird · Pull Request #29151 · ggml-org/llama.cpp — r/LocalLLaMA
  3. Jev) Mica 4B vs Laya on Tetris: same seed, same prompt, 0 output tokens, running locally on llama.cpp — r/LocalLLM
  4. Qwen3-Coder 30B on RTX 3080 20GB — KV cache stability and context tuning — r/LocalLLM
  5. PSA: llama.cpp -cram should be increased for agentic workflows (default is 8192) — r/LocalLLaMA
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  7. My foray into local ai. Two BC-250 ex mining apus running Qwen3.6-35B-A3B Q4_K_M at 60 tok/s with 64k context — r/LocalLLaMA
  8. 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

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