JevK5 — alibiserikbay 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
基于 Qwen3.5-4B 的 4B 决策模型，带有 TypeSafe 风格的服务器，并提供 GGUF 版本，可在 NVIDIA、AMD、Intel、Apple GPU 或 CPU 上通过 llama.cpp 运行。
变体
JevK5 最小的变体在 Q4_K_M 下约需 4 GB 显存——量化权重加框架开销，不含 KV 缓存。
| 模型 | Q4 下显存 | 显存 | 上下文 | 运行 |
|---|---|---|---|---|
| JevK5 4B → 4B | ~3.6 GB | 16K (prompt limit) | jevk5-serve :8090 |
显存为 Q4_K_M 下的量化权重加开销，与 GPU 与显存检测器使用同一引擎计算。
如何在本地运行 JevK5
安装 Ollama，然后拉取标签。
Served by jevk5-serve (default port 8090). This model does not run in Ollama.
在上方选择一个尺寸，查看它自己的显存、速度估算和安装命令。
许可证
Commercial use permitted. No usage restrictions beyond attribution.
适用于: JevK5 4B推荐 GPU
目录中能在本地运行 JevK5（至少 4 GB 显存）的最便宜 GPU 是 Intel Arc B570 (10 GB).
JevK5 — 常见问题
How much VRAM does JevK5 need?
JevK5 needs about 4 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: JevK5 4B (4 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run JevK5 on an RTX 4090 (24 GB)?
Yes — JevK5 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for JevK5?
Q4_K_M is the best balance of quality and VRAM for JevK5 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run JevK5 locally?
JevK5 is a decision model: it is called over an HTTP endpoint (/v1/systemone), not chatted with. Where a variant is available in Ollama 0.35 or newer, pull it with `ollama pull` and send requests to the local server; the others ship their own server. Each variant page shows the exact commands for that model.