從 4bit 到 6bit，檔案大小多出約 32%，品質差多少？我把三個 MLX 模型都在同一台 M1 上跑完了，你不用。挑 gemma4:e2b 的量化時，你會看到一排 4bit、5bit、6bit、8bit……每個都標榜「更精確」，但真正該問的不是哪個品質最高，而是從哪一階開始，多付出的下載量跟速度換不到看得出來的品質。
From 4bit to 6bit is a roughly 32% bigger download — how much quality does it buy? I ran all three MLX models on the same M1 so you don't have to. When you pick a gemma4:e2b quant you see a wall of options — 4bit, 5bit, 6bit, 8bit — each advertised as "more accurate." The right question isn't which has the highest quality; it's which step still earns its keep before diminishing returns hit.
為什麼不再是記憶體問題？
過去我們挑選量化，最大的顧慮是「會不會逼近機器的 16GB 記憶體上限、導致吃 swap 變慢」。但 e2b 改變了遊戲規則。
即使是 6bit 的 e2b，載入後也遠遠達不到 16GB 的極限。因此，我們不需要再為了「避免 swap」而妥協。現在的抉擇，純粹是「品質 vs 檔案大小 vs 速度」的報酬遞減分析。
Why isn't memory the issue anymore?
In the past, our biggest concern when picking a quant was "will it hit the 16GB ceiling and trigger swap." e2b changes the game.
Even at 6bit, e2b stays far away from the 16GB limit. We no longer need to compromise just to "avoid swap." The decision is now a pure diminishing returns analysis of "quality vs file size vs speed."
我是怎麼測的？
| 項目 | 規格 |
|---|---|
| 機器 | MacBook Pro 14" M1 Pro 16GB |
| Runtime | mlx-lm |
| 量化 | mlx-community/gemma-4-e2b-it-4bit (以及 5bit、6bit) |
| 提示集 | 5 題子集，從 Gemma 4 e2b M1 評測拿來 |
| 重複次數 | 每題每量化 3 次，取中位數 |
我刻意不測 8bit 或更高，因為對輕量級部署而言，6bit 已經足以探出報酬遞減的邊界。
How did I test?
| Item | Spec |
|---|---|
| Machines | MacBook Pro 14" M1 Pro 16GB |
| Runtime | mlx-lm |
| Quants tested | mlx-community/gemma-4-e2b-it-4bit (and 5bit, 6bit) |
| Prompt set | 5-task subset from the M1 benchmark article |
| Repetitions | 3 runs per (quant × task), median reported |
I deliberately stopped at 6bit because for lightweight deployment, 6bit is enough to find the edge of diminishing returns.
4bit、5bit、6bit 的硬數字差多少？
| 量化 | 檔案大小 (Hugging Face) | 載入後 Peak Memory | tokens/s (生成) | 首 token 延遲 |
|---|---|---|---|---|
| 4bit | 2.1 GB | 2.2 GB | 35.2 | 0.5s |
| 5bit | 3.0 GB | 3.1 GB | 30.5 | 0.5s |
| 6bit | 4.1 GB | 4.2 GB | 25.1 | 0.5s |
檔案大小的影響直接反映在下載等待時間，以及未來如果部署在邊緣設備上的儲存成本。
How far apart are 4bit, 5bit, and 6bit in hard numbers?
| Quant | File size (Hugging Face) | Loaded Peak Memory | tokens/s (gen) | First-token latency |
|---|---|---|---|---|
| 4bit | 2.1 GB | 2.2 GB | 35.2 | 0.5s |
| 5bit | 3.0 GB | 3.1 GB | 30.5 | 0.5s |
| 6bit | 4.1 GB | 4.2 GB | 25.1 | 0.5s |
File size impacts download wait times directly, and matters for future storage costs if deployed on edge devices.
品質差異在哪裡看得出來、哪裡看不出來？
5 題子集驗證出與過去一致的結論：
結構化任務（Python、SQL、JSON）—— 4 / 5 / 6bit 三者輸出幾乎沒差別。這類任務的「正確 vs 錯誤」是離散的，量化等級對它幾乎不影響。
語意敏感任務（繁中問答、reasoning）—— 5bit 相對於 4bit 有時能修正細微用語，但 6bit 再帶來的增益已經難以用肉眼區分。
Where does quality differ — and where doesn't it?
The 5-task subset validates our consistent finding:
Structured tasks (Python, SQL, JSON) — 4 / 5 / 6bit are nearly indistinguishable. Output is discrete (correct vs wrong) and quant level barely moves the needle.
Semantically sensitive tasks (Chinese Q&A, reasoning) — 5bit sometimes fixes minor phrasing over 4bit, but 6bit's additional gain is visually indistinguishable.
你該下載哪個量化？
| 你的情況 | 我建議 | 理由 |
|---|---|---|
| 日常開發 / 寫腳本 | 4bit | 載入最快、檔案最小，結構化任務表現無異 |
| 文本分析 / 繁中對話 | 5bit | 語意精準度好一點，且大小仍在合理範圍 |
| 純做結構化任務（SQL/JSON） | 4bit | 結構化任務上 5/6bit 沒有可見回報 |
So which quant should you download?
| Your situation | I'd pick | Why |
|---|---|---|
| Daily dev / scripting | 4bit | Fastest load, smallest file, no drop on structured tasks |
| Text analysis / Chinese chat | 5bit | Slightly better semantic precision, size still reasonable |
| Pure structured tasks (SQL / JSON) | 4bit | 5/6bit don't pay back on structured output |
MLX 怎麼載入不同版本？
這台機器上，我們不是透過 Ollama，而是直接使用 mlx-lm 的 API。要載入不同 bit 數的 e2b，只要在 Python 程式裡指定：
from mlx_lm import load, generate
# 載入 4bit
model, tokenizer = load("mlx-community/gemma-4-e2b-it-4bit")
# 載入 5bit
# model, tokenizer = load("mlx-community/gemma-4-e2b-it-5bit")記得，e2b 是 thinking 模型，若要確保拿到乾淨輸出，要關閉 enable_thinking。
How do you load different versions in MLX?
On this machine, instead of Ollama, we use the mlx-lm API directly. To load different bit sizes of e2b, just specify it in Python:
from mlx_lm import load, generate
# Load 4bit
model, tokenizer = load("mlx-community/gemma-4-e2b-it-4bit")
# Load 5bit
# model, tokenizer = load("mlx-community/gemma-4-e2b-it-5bit")Remember, e2b is a thinking model. To ensure clean output, turn off enable_thinking.
所以，該裝哪一個？
波形評級：STEADY_PULSE
建議：
- 預設 → 4bit，輕便無壓力
- 追求語意 → 5bit
- 結構化 pipeline → 永遠 4bit
在 Hugging Face 看 gemma-4-e2b-it 系列 →
擂台系列導覽：這篇是「本地模型擂台」系列的其中一戰——e2b 在 M1 上的完整評測、長 context 可靠度，以及更多本地模型捉對實測，都整理在系列總覽頁。
So — which one goes on your machine?
Waveform: STEADY_PULSE
Recommendations:
- Default → 4bit, light and easy.
- Semantic heavy → 5bit.
- Structured pipelines → always 4bit.
See gemma-4-e2b-it series on Hugging Face →
More from the Local Model Arena: this article is one bout in the Local Model Arena series — the full e2b M1 benchmark, long-context reliability, and more head-to-head local-model matchups are collected on the series hub.