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Ollama, LM Studio, llama.cpp, vLLM: Choosing the Right LLM Runner

By PulseAugur Editorial · [1 sources] ·

This article compares four popular tools for running open-source large language models locally: Ollama, LM Studio, llama.cpp, and vLLM. The choice depends on the user's needs, with LM Studio and Ollama recommended for personal use and Ollama or vLLM for API serving. Memory requirements vary significantly based on model quantization and format, with Qwen3.8 27B needing as little as 17.2 GB for a quantized version or up to 52.8 GB for its original BF16 weights when run with vLLM. AI

IMPACT Helps users select the right tool for local LLM deployment based on hardware and use case, impacting efficient resource utilization.

RANK_REASON The article compares and contrasts different software tools for running LLMs, rather than announcing a new model or research breakthrough.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Ollama, LM Studio, llama.cpp, vLLM: Choosing the Right LLM Runner

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Tool
The article compares and contrasts different software tools for running LLMs, rather than announcing a new model or research breakthrough.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, product
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High
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3 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Gosha ·

    Ollama vs LM Studio vs llama.cpp vs vLLM: which to pick, and how much VRAM each needs

    <p>At home, for yourself, pick LM Studio or Ollama; for an API on a server, pick Ollama for one user and vLLM for many. The choice changes the memory too: Qwen3.8 27B takes 17.2 GB as GGUF Q4_K_M and 52.8 GB as the original BF16 weights that vLLM runs. Below: a feature table chec…

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