AINewsnow

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

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 checked against the docs o…

Read the full story at DEV Community — Machine Learning ↗

Timeline · 1 report

  1. 2026-10-21 03:25 · DEV Community — Machine Learning
    Ollama vs LM Studio vs llama.cpp vs vLLM: which to pick, and how much VRAM each needs

More stories

  1. DeepSeek V4.1 Flash is now available in Token Pass — Vercel Blog
  2. Qwen3.8-Flash-Next 125B Q2_0 on a 24GB M4 Mac mini: ~8.1 tok/s with Strata — r/LocalLLM
  3. How to squeeze the Max Token from M1 Max 64gb? — r/LocalLLaMA
  4. Installing qwen model on my smartphone — r/LocalLLM
  5. Eagle-4 Released: Faster than DFlash2 & DSpark — r/LocalLLaMA
  6. Intel Arc B580 now beats an RTX 5060 Ti on llama.cpp prompt processing (Llama 2 7B), plus 1.5x faster long chats — r/LocalLLM
  7. Qwen3.6-35B-A3B (22 GB, 4-bit) on a 16 GB Mac mini M4 through stock llama.cpp: speed, a 4-agent crew, and executed code scores — r/LocalLLM
  8. Geezuz kernel: Qwen3.8-27B at 190 tok/s (620~ tok/s at 8 requests) on two 3090s — r/LocalLLaMA

Get the daily brief of stories like this at 6:30 every morning →