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title: Self-Hosting Your First LLM | daily.dev
description: A practical guide to self-hosting LLMs for agent workloads on a single GPU machine. Covers which benchmarks matter for agentic tasks (BFCL, τ-bench, SWE-bench,...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Self-Hosting Your First LLM

**[Towards Data Science](https://daily.dev/sources/tds)** · 21 min read · 1 upvotes · 0 comments

## Summary

A practical guide to self-hosting LLMs for agent workloads on a single GPU machine. Covers which benchmarks matter for agentic tasks (BFCL, τ-bench, SWE-bench, IFEval), quantization formats (BF16, GPTQ, AWQ, GGUF/K-quants) and their performance tradeoffs, GPU selection across AWS/GCP/Azure with pricing, KV cache sizing, and recommended models (Qwen3.5-27B, GLM-4.7 Flash, GPT-OSS-20B). Deployment patterns include Ollama for evaluation and vLLM for production with PagedAttention. Also covers zero-switch-cost migration from OpenAI and Anthropic APIs using vLLM's OpenAI-compatible endpoint and LiteLLM proxy, plus cost analysis showing self-hosting breaks even at roughly 40–100M tokens/month.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/self-hosting-your-first-llm/>

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---

Tags: [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#self-hosting](https://daily.dev/tags/self-hosting), [#vllm](https://daily.dev/tags/vllm)

[View this post on daily.dev](https://daily.dev/posts/self-hosting-your-first-llm-13jpheaxf)

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