This article outlines patterns for building LLM systems, covering evaluation, retrieval augmented generation, fine tuning, caching, and guardrails.
Publication
llama.cpp guide
This guide explains how to run LLMs locally with llama.cpp across different hardware.
About llama.cpp guide
This guide explains how to run open-source LLMs locally with llama.cpp from scratch across different hardware, including CPU-only systems and different GPU backends. It covers quantization, backends such as CUDA and ROCm, and how model size, memory bandwidth, and hardware affect generation speed.
It also compares llama.cpp with LM Studio for commercial use, less common hardware, closed-source concerns, or access to newer features and models. Performance and response quality vary with hardware and model choice, and CPU-only or mixed CPU/GPU setups are slower than a GPU with enough memory for the model and context.
Key features
- Explains running LLMs locally with llama.cpp from scratch
- Covers CPU-only operation with AVX-supporting processor
- Explains quantization for running models on limited hardware
- Compares GPU backends including CUDA and ROCm
- Discusses performance factors like memory bandwidth and model size
- Notes LM Studio restriction on commercial use
Best for: People who want to run open-source LLMs locally and understand llama.cpp on different hardware.
Good for
- Explains how to run models without a high-end GPU
- Covers performance variables such as model size and memory bandwidth
- Distinguishes llama.cpp use cases from LM Studio limitations
Watch out for
- Requires at least a decent CPU with AVX support
- CPU-only or mixed CPU/GPU setups have much lower performance
- Response quality varies by model and use case
llama.cpp guide at a glance
- Official website
- blog.steelph0enix.dev/posts/llama-cpp-guide
- Type
- Publication
- Platforms
- Web
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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llama.cpp guide FAQ
Do I need a mid/high-end GPU to follow this guide?
No. You can run LLMs with no GPU on reasonably modern hardware with a decent CPU with AVX support, but performance may vary.
What performance can I expect?
It depends on matrix operations performance, memory bandwidth, and model size. A GPU with enough memory for the model and context can give real-time generation, while CPU-only or combined use is much slower but can still be real-time with small models.
What quality of responses can I expect?
It depends heavily on usage and chosen model, with larger models generally giving better responses. You will need to test models yourself.
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