Understanding Model Sizes in 2025: A Practical Guide for Local LLMs

August 13, 2025

Choosing the “right size” model has changed a lot since last year. In 2025, training recipes, data quality, and smarter architectures often matter more than raw parameter count. This guide updates our most-read piece with the latest facts, practical device picks, and a simple decision flow you can use today.

What “size” means now

  • Dense parameters vs. active parameters: Many 2025 models use Mixture‑of‑Experts (MoE) or efficiency tricks like MatFormer. A model might have 30B total parameters but only activate ~3B per token. That’s why some “big” models feel fast and light at runtime.
  • Data > size: High‑quality data and better training can let a 3.8B model hit scores that once required 100× more parameters. By 2024, small models (~3.8B) reached >60% on MMLU, which previously took 540B‑scale models.
  • Context is a capability: 128K contexts are now common, and some families push to ultra‑long windows (even multi‑million tokens in specialized setups). Choose size for quality, but choose the model family for context and modality.

References: scientific findings on smaller‑better‑cheaper models; MoE adoption across leading 2025 families; widely reported long‑context releases.

Architectures to know in 2025

  • MoE (Mixture‑of‑Experts): Scales capacity without activating every parameter each token. Helps large models stay efficient at inference while improving specialization.
  • MatFormer / “E4B” style efficiency: Designs where only a subset of layers/heads are active per step, e.g., Gemma 3n runs as ~4B “effective” even though the checkpoint is larger.
  • Hybrid/Reasoning variants: Distilled reasoning models (like DeepSeek R1 distilled) bring step‑by‑step thinking to smaller footprints.
  • Compression & quantization: 4‑bit and even ternary (≈1.58‑bit) weight formats push models onto phones and thin‑and‑light laptops with minimal quality loss in chat/writing tasks.

Device‑based quick picks

iPhone (and most iPads)

  • Best sizes: 0.5B–4B effective parameters
  • Great defaults:
    • Gemma 3n (E4B effective) for multimodal on‑device use
    • Phi‑4 mini/flash‑style ~3.8B for fast, general chat and reasoning
    • Qwen 2.5/3 in the 1–3B range for multilingual tasks
  • Notes: 7B dense is still a stretch for most phones (thermals/battery). Prefer efficient models with partial activation and 4‑bit quantization.

Mac (Apple Silicon)

  • 8–16GB RAM: 7B (Q4/Q5) for chat/coding basics
  • 16–32GB RAM: 7B–14B (Q4/Q5) for higher‑quality writing, stronger coding
  • 32–64GB RAM: 14B–32B (Q4/Q5) for reasoning, long context, codebases
  • 128GB+ RAM: 70B class (Q4) for maximum local capability

Approx storage (gguf, typical): 7B Q4 ≈ 4–5GB, 14B Q4 ≈ 8–10GB, 32B Q4 ≈ 18–22GB, 70B Q4 ≈ 40–45GB. Runtime memory needs are higher than file size due to KV cache and buffers.

How to choose by task

  • General chat, email, drafting: 7B good; 13–14B better for nuance/consistency
  • Coding: 14B+ helps with larger repos and multi‑file edits; distilled reasoning 14–32B shines
  • Math/logic: Reasoning‑tuned 14–32B (e.g., R1‑style distilled variants)
  • Multilingual: Qwen‑family across sizes (1–32B) is a safe pick
  • Vision/multimodal: Gemma 3n on mobile; Llama/Qwen multimodal or Gemma on Mac
  • Very long documents: Prefer families with long contexts (128K–1M+) and retrieval tools
  • Open‑weight models from majors: OpenAI’s smaller open‑weight family (e.g., GPT‑OSS 20B/120B) targets efficiency—20B optimized for edge/server with ~16GB memory footprints, 120B runnable on a single 80GB GPU.
  • MoE goes mainstream: Llama 4‑class and similar MoE releases emphasize high total capacity with manageable active parameters.
  • Small models get smart: Microsoft’s Phi‑4 mini/flash‑reasoning emphasizes speed and reliability in ~3.8B footprints.
  • Ultra‑long context: Modern families advertise 128K by default and multi‑million‑token variants for specialized workloads.

Quantization cheat‑sheet (local use)

  • Q3_K (or 3‑bit): Maximum compression for phones; quality dip on harder tasks
  • Q4_K_M (4‑bit): Best overall balance for iPhone and most Mac use
  • Q5_K_M (5‑bit): Noticeably better quality; needs more RAM
  • 8‑bit/16‑bit: Highest quality; use when you have memory to spare
  • Ternary / 1.58‑bit: Emerging for select models; great footprint, still maturing

Tip: For coding/reasoning, try Q5 if your device allows; for everyday chat, Q4 feels great and stays cool.

Model examples by size (2025)

  • 0.5B–4B (mobile sweet spot)
    • Gemma 3n (E4B effective) — multimodal on‑device
    • Phi‑4 mini / flash‑reasoning (~3.8B) — fast, reliable generalist
    • Qwen 2.5/3 1–3B — strong multilingual
  • 7B–14B (balanced desktop)
    • Qwen 3 7B Instruct — well‑rounded, great multilingual
    • Llama 3.x 8B — broad tooling and community
    • Phi‑4 14B — compact yet capable
  • 32B (power users)
    • DeepSeek R1 distilled 32B — step‑by‑step reasoning
    • Qwen 2.5/3 32B — multilingual and code
    • Mistral Medium 3 / Small 3.1 — efficient, long context variants
  • 70B class (workstation‑grade Macs)
    • Llama 3.3/4 family 70B — frontier‑level local performance

Context windows: what’s “enough”?

  • Everyday chat/writing: 8K–32K is fine
  • Research & long docs: 128K+ saves time
  • Codebases: Bigger helps, but retrieval is essential; mix embeddings with long context
  • Ultra‑long (1M–10M tokens): Niche today; useful for deep research and multi‑doc analysis if you have the hardware

A simple decision flow

  1. Start with your device: iPhone → 0.5–4B; Mac → 7–14B
  2. Pick for the task: writing/chat → smaller; coding/reasoning → bigger
  3. Choose the family for modality/context: need vision or massive context? pick accordingly
  4. Quantize for comfort: Q4 for most, Q5 if you can afford it
  5. Iterate: try a 7B first; move up only if you feel limited

Using these models in Enclave AI

Enclave AI is optimized for on‑device privacy and performance on iPhone and Mac. You can select from efficient small models for mobile and scale up to 32B/70B on high‑memory Macs—all without sending your data to the cloud.

  • Download Enclave AI on the App Store and try: Gemma 3n on iPhone; Qwen 3 7B or a distilled R1 14B/32B on Mac for coding/reasoning.
  • Prefer hybrid? Enclave also supports connecting to cloud endpoints when you need them, while keeping everyday work local.

Why this matters

The 2025 bottom line: model choice is about fit, not size. With MoE, better data, and modern quantization, smaller footprints deliver outsized capability—especially on Apple Silicon. Start small, stay private, and only scale up when your tasks demand it.

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Further reading and context:

  • Smaller‑better‑cheaper models and MMLU trends (Scientific American)
  • MoE and modern architecture releases across major families (Llama 4‑class, Qwen3)
  • Open‑weight releases like OpenAI’s GPT‑OSS (20B/120B) targeting efficient deployment
  • Microsoft Phi‑4 mini/flash‑reasoning for fast small‑model performance