The 5 Best Open-Source LLMs for Local Deployment: A Complete Guide for Single-Machine Servers #

Below is a quick‑reference comparison of the five open‑source LLMs that currently give the best trade‑off between capability, size, and RAM‑footprint for a single‑machine server with 16 – 24 GB of RAM (CPU‑only or modest GPU).
All of them can be run with 4‑bit/8‑bit quantisation (or “GGUF” format) so that the model fits comfortably in memory while still delivering decent throughput for the typical RAG‑/embedding‑/image‑tagging pipelines you described.

Why These Five? #

Practical Deployment Blueprint (16‑24 GB RAM) #

Below is a sample stack that works with any of the five models. Adjust the model name to swap in the one you prefer.

Example docker‑compose.yml (simplified) #

version: "3.9"
services:
  llm:
    image: ghcr.io/vllm-project/vllm:latest
    command: >
      --model /models/phi3-mini-gguf/phi3-mini-4bit.gguf
      --dtype auto
      --max-model-len 8192
      --served-model-name phi3-mini
      --tensor-parallel-size 1
    volumes:
      - ./models:/models:ro
    ports:
      - "8000:8000"
    deploy:
      resources:
        limits:
          memory: 10G   # leaves ~14 GB for DB + other services

  embed:
    image: ghcr.io/huggingface/text-embeddings-inference:latest
    command: >
      --model-id phi3-mini-embedding-gguf
      --dtype auto
    volumes:
      - ./models:/models:ro
    ports:
      - "8100:8100"
    deploy:
      resources:
        limits:
          memory: 5G

  vector-db:
    image: qdrant/qdrant:latest
    ports:
      - "6333:6333"
    volumes:
      - ./qdrant_storage:/qdrant/storage
    deploy:
      resources:
        limits:
          memory: 6G

  api:
    build: ./api
    ports:
      - "8080:8080"
    depends_on:
      - llm
      - embed
      - vector-db
    deploy:
      resources:
        limits:
          memory: 2G

Tip: If you run on a GPU with 8 GB VRAM, you can switch the llm service to vllm with --gpu-memory-utilization 0.9 and keep the same model size; the RAM footprint on the host drops dramatically.

Quick “Which One to Pick?” Decision Tree #

Summary of Strengths & Weaknesses #

Final Recommendations #

1. Start with Phi‑3‑mini if you want the lightest setup and you only need English‑centric RAG + embeddings. It will comfortably run on a 16 GB RAM laptop or a small VPS.

2. Upgrade to Gemma‑2‑7B‑Instruct or Mistral‑7B‑Instruct when you need better reasoning, code generation, or multilingual coverage while still staying under 24 GB RAM.

3. Choose Llama 2 7B‑Chat if you already have pipelines built around Llama‑style tokenizers and you can obtain a commercial licence for production.

4. Pick Qwen‑1.5‑7B‑Chat if your corpus contains a lot of non‑English (especially Chinese) content and you want a single model that handles both generation and embeddings.

All five models can be quantised to 4‑bit GGUF, served via vLLM/llama‑cpp/TGI, and combined with FAISS/Qdrant + LangChain/LlamaIndex to deliver a full‑stack local LLM server that fits comfortably inside a 16‑24 GB RAM machine. Happy building! 🚀