Google12BQ4_K_M 下约 8 GB 显存

Gemma 4 12B (Unified) — 显存、速度与本地部署

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

作者： Jakub Rusinowski · 最后更新： 2026年6月3日

Released June 3, 2026 as a separate follow-up to the March launch, the 12B 'Unified' model uses a novel encoder-free architecture that feeds image, audio, and video directly into the LLM backbone — no bolt-on vision/audio encoders. Google's headline claim is that it runs entirely on a typical 16 GB laptop, making it the most capable Gemma 4 tier most people can actually run. Apache 2.0 licensed.

Gemma 4 12B (Unified) 在 Q4_K_M 下约需 8 GB 显存——量化权重加框架开销，不含 KV 缓存。在 Apple Silicon 上，这部分来自统一内存。

逻辑86

创意82

编程83

按量化级别的显存与速度

计算基准：NVIDIA RTX 4090 (24 GB)。仅含权重与开销：该模型架构未公开，因此未计入 KV 缓存。

量化	显存	速度（估算）	适配
Q2_K 2.63 bpw	4.7 GB	~120 tok/s	可运行
Q3_K_M 3.41 bpw	5.9 GB	~101 tok/s	可运行
Q4_K_M 4.83 bpw	8 GB	~79 tok/s	可运行
Q5_K_M 5.67 bpw	9.3 GB	~70 tok/s	可运行
Q6_K 6.56 bpw	10.6 GB	~62 tok/s	可运行
Q8_0 8.50 bpw	13.6 GB	~50 tok/s	可运行
F16 16.00 bpw	24.8 GB	~4 tok/s	需卸载

黑色标记 = NVIDIA RTX 4090 (24 GB) 上的可用显存。估算来自内存带宽屋顶线模型，详见方法说明页. Gemma 4 12B (Unified) 显存计算器 →

运行 Gemma 4 12B (Unified)

目录中能运行 Gemma 4 12B (Unified) 的最便宜 GPU 是 Intel Arc B570 (10 GB).

购买此硬件 Intel Arc B570 10GB — 10 GB VRAM · 150 W board power 立即云端部署 RunPod 上的 RTX 4090 — 低至 $0.34/小时 · 价格核实于 2026-07

或在 Vast.ai 比较，低至 $0.35/小时 (typical low · varies)

作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。

联盟营销声明: 本页部分链接为联盟推广链接——如果你通过它们购买，LLM Configurator 可能会获得佣金，而你无需支付任何额外费用。作为亚马逊联盟成员（Amazon Associate），LLM Configurator 会从符合条件的购买中获得收益。

Intel Arc B570 10GB

10 GB VRAM · 150 W board power

2026年价格波动较大——请以当前商品页价格为准。

在亚马逊查看价格

如何运行 Gemma 4 12B (Unified)

安装 Ollama，然后运行：

$ollama run gemma4:12b

Hugging Face 上的权重： google/gemma-4-12B-it ↗

规格

Preview — The model is released, but these specs are thin or rest on a single source. Individual fields may be wrong.

参数量: 12 Billion
上下文窗口: 128,000
架构: Encoder-free Unified Multimodal Transformer (text + image + audio + video)
提供商: Google
许可证: Apache 2.0
规格量化: Q4_K_M
系统内存: 32 GB
记录更新于: 2026-06-03

许可证Apache-2.0允许商业使用

Commercial use permitted. No usage restrictions beyond attribution.

质量与使用场景

评分由模型作者或独立评测方发布——衡量质量而非吞吐量，并非我们实测。

最适合multimodalvideo analysisaudio transcriptionlaptopcoding

我的 GPU 能运行 Gemma 4 12B (Unified) 吗？

Gemma 4 的其他尺寸

Gemma 4 12B (Unified) — 常见问题

How much VRAM does Gemma 4 12B (Unified) need?

About 8 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Gemma 4 12B (Unified) run on an RTX 4090 (24 GB)?

Yes. Gemma 4 12B (Unified) needs about 8 GB at Q4_K_M, inside a 24 GB card, at an estimated 79 tokens/sec.

How do I run Gemma 4 12B (Unified) locally?

Install Ollama and run `ollama run gemma4:12b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Gemma 4 come in?

Gemma 4 E2B (4 GB), Gemma 4 E4B (6 GB), Gemma 4 26B-A4B (16 GB), Gemma 4 31B (20 GB), Gemma 4 12B (Unified) (8 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.