EXAONE 4.5 33B — 显存、速度与本地部署
作者： Jakub Rusinowski · 最后更新：
LG's first open vision-language model: the EXAONE 4.0 language model with a 1.2B vision encoder, 33B parameters in all, and a 262,144-token context. LG reports it strongest on document understanding and Korean (vendor-reported). Research and education use only. LG publishes official GGUF (Q4_K_M 20.0 GB plus a 2.6 GB vision projector), FP8 and AWQ builds.
EXAONE 4.5 33B 在 Q4_K_M 下约需 21 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。
按量化级别的显存与速度
计算基准：NVIDIA RTX 4090 (24 GB)。包含 8K 上下文的 KV 缓存，因此数值高于上方的主数字。
| 量化 | 显存 | 显存 | 速度（估算） | 适配 |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 13.5 GB | ~56 tok/s | 可运行 | |
| Q3_K_M 3.41 bpw | 16.9 GB | ~45 tok/s | 可运行 | |
| Q4_K_M 4.83 bpw | 22.3 GB | ~33 tok/s | 勉强 | |
| Q5_K_M 5.67 bpw | 26.6 GB | ~4 tok/s | 需卸载 | |
| Q6_K 6.56 bpw | 30.4 GB | ~4 tok/s | 需卸载 | |
| Q8_0 8.50 bpw | 37.4 GB | ~3 tok/s | 需卸载 | |
| F16 16.00 bpw | 70.9 GB | — | 放不下 |
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. EXAONE 4.5 33B 显存计算器 →
运行 EXAONE 4.5 33B
目录中能运行 EXAONE 4.5 33B 的最便宜 GPU 是 AMD Radeon RX 7900 XTX (24 GB).
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。
如何运行 EXAONE 4.5 33B
安装 Ollama，然后运行：
ollama run exaone-4规格
Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.
- 参数量
- 33 Billion (incl. 1.2B vision encoder)
- 上下文窗口
- 262,144
- 架构
- Dense transformer, 3:1 sliding-window (4,096) to global attention, MTP layer, vision encoder
- 提供商
- LG AI Research
- 许可证
- EXAONE AI Model License 1.2 - NC
- Availability
- Weights downloadable
- Practical deployment
- Single consumer GPU
- 规格量化
- Q4_K_M
- Vision projector
- +2.6 GB when you send images (not included above)
- 系统内存
- 32 GB
- 记录更新于
- 2026-11-11
Research / non-commercial only — this licence does NOT permit shipping a commercial product.
另请参阅 EXAONE 4.0 32B
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。
EXAONE 4 的其他尺寸
EXAONE 4.5 33B — 常见问题
How much VRAM does EXAONE 4.5 33B need?
About 21 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 EXAONE 4.5 33B run on an RTX 4090 (24 GB)?
Yes. EXAONE 4.5 33B needs about 21 GB at Q4_K_M, inside a 24 GB card, at an estimated 33 tokens/sec.
How do I run EXAONE 4.5 33B locally?
Install Ollama and run `ollama run exaone-4`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does EXAONE 4 come in?
EXAONE 4.5 33B (21 GB), EXAONE 4.0 32B (20 GB), EXAONE 4.0 1.2B (2 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.