Running Gemma 4 on a Modest Machine: Unsloth vs LM Studio vs llama.cpp vs Ollama
The article discusses running Gemma 4 on modest hardware, focusing on four tools: Unsloth, LM Studio, llama.cpp, and Ollama. It highlights how these tools complement each other rather than compete, forming a pipeline for local AI development. The author shares insights on the practicality of fine-tuning and running models on limited resources.
- ▪The author explores running Gemma 4 on a machine with an Intel i5 and 16GB RAM.
- ▪Unsloth allows for cost-effective fine-tuning, making it accessible for smaller experiments.
- ▪LM Studio is recommended as an easy starting point for those new to running local models.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 215472) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Samuel Komfi Posted on May 24 Running Gemma 4 on a Modest Machine: Unsloth vs LM Studio vs llama.cpp vs Ollama #devchallenge #gemmachallenge #gemma Gemma 4 Challenge: Write about Gemma 4 Submission This is a submission for the Gemma 4 Challenge: Write About Gemma 4 When local AI conversations happen online, they tend to sound like this: "I ran the 70B model on my dual-GPU workstation." or "You only need 64GB RAM and a 24GB graphics card." Meanwhile, I'm sitting with an Intel i5, 16GB…
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