HIGH NOON TRADER

Rough real-world LLM sizes you can run

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Happy Harry Huckleberry
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🧠 Rough real-world LLM sizes you can run

🟡 With 24GB unified memory

You can comfortably run:

✅ Very smooth

  • 7B models (any variant)
    • Llama 3 8B
    • Mistral 7B
    • Qwen 7B
  • Runs fast, good context size, low risk of swapping

⚠️ Usable but tighter

  • 13B models (quantized 4-bit / 5-bit)
    • Works fine, but:
      • slower responses
      • less room for large context windows
      • multitasking becomes limited

❌ Not practical

  • 30B+ models (will hit memory pressure / swap heavily)

🟢 With 32GB unified memory

This is a noticeably better tier for AI work:

✅ Comfortable

  • 7B–13B models (very smooth)
  • Larger context windows (8K–32K depending on model)

✅ Very usable (this is the big upgrade)

  • 20B–34B quantized models
    • Examples:
      • Mixtral 8x7B (partially feasible depending on quantization)
      • Llama 3 70B (VERY heavily quantized, slower, but possible in constrained form)

⚠️ Borderline but possible

  • 40B–70B models
    • Only with:
      • aggressive 4-bit quantization
      • small context windows
      • slower inference
    • Not “daily driver fast,” but can load for testing

🧠 Key concept most people miss

It’s not just model size—it’s:

1. Model weights

2. Context window (how much it “remembers”)

3. macOS overhead (~4–6GB baseline)

4. GPU/Metal memory sharing

So a “32GB Mac” is really more like:

~26–28GB usable for LLMs in practice

⚡ Simple breakdown

RAMSweet spotMax practicalExperience
24GB7B–13B~13BSmooth, entry AI dev
32GB7B–34Bup to 70B (limited)Serious local AI work

🧠 What actually feels different day-to-day

24GB:

  • Mostly 7B–13B models
  • Occasional slowdown with big prompts
  • Less multitasking (IDE + model + browser)

32GB:

  • Feels “unrestricted” for most open-source AI work
  • You can experiment without constantly worrying about memory
  • Much better for running tools like:
    • local RAG systems
    • multiple models at once
    • long-context chat setups

🏁 Bottom line

If your goal is an “AI machine” for learning + building + experimenting:

👉 32GB = real AI dev experience
👉 24GB = entry-level local LLM experimentation

If you want, I can map this to:

  • specific apps you’d use (Ollama, LM Studio, MLX)
  • exact models you should download first
  • or whether you’d actually be better off spending the difference on a cloud GPU instead of RAM upgrades