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
Table of contents
Wait…why would I host my own LLM again?Why a single machine?Which Benchmarks Actually Matter?QuantizingHardwareModelsDeploymentZero switch costs?How much is this going to cost?Wrapping things upSort: