**VisionPsy-Nano-460M-Flash-GGUFs** provides GGUF weights of VisionPsy-Nano-460M-Flash for fast, fully on-device multimodal inference with our patched **llama.cpp** fork (not stock upstream). Flash is the **efficiency-optimized** sibling of VisionPsy-Nano-460M: same ~460M nanoVLM architecture, but it processes each image with **far fewer visual tokens (as few as 64 at 512x512)**, so it responds an order of magnitude faster on real phones while giving up only a small amount of accuracy. Unquantized **FP32** and **BF16 GGUFs** are included alongside a family of quantization formats, from near-lossless **8-bit** through a high-quality **5-bit** option down to ultra-compact **3-bit**. All sub-8-bit variants (except the legacy `Q4_0`) use **importance-matrix (imatrix) calibration**. If VisionPsy-Nano-460M-GGUF is our best-quality ~0.5B GGUF, **Flash-GGUF is the one you ship when time-to-first-token, battery, and RAM are the binding constraints.** > Each GGUF is a **vision-language** model: alongside the text weights (`*.gguf`) you also need the > multimodal projector (`mmproj-*.gguf`, the SigLIP2 vision encoder) to run image inputs. See > Usage.

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VisionPsy-Nano-460M-GGUFs

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**VisionPsy-Nano-460M-GGUFs** provides GGUF weights of VisionPsy-Nano-460M for fast, fully on-device multimodal inference with our patched **llama.cpp** fork (not stock upstream). Requires the patched llama.cpp. VisionPsy-Nano-460M is a compact (~460M parameter) vision-language model that **compares favourably with other models in its ~0.5B weight class on 16 of 17 public benchmarks**; these GGUF builds bring that quality to phones, tablets, and edge hardware. We release some **GGUF variants**, ranging from unquantized **FP32** and **BF16** references to lossless **8-bit**, high-quality **5-bit**, and ultra-compact **3-bit** formats. This range supports deployment across diverse hardware settings, from high-end workstations to resource-constrained on-device environments. All sub-8-bit variants (except the legacy Q4_0) use **importance-matrix (imatrix) calibration**, which consistently reduces quality degradation. > Each GGUF is a **vision-language** model: alongside the text weights (`*.gguf`) you also need the > multimodal projector (`mmproj-*.gguf`, the SigLIP2 vision encoder) to run image inputs. See > Usage.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 13

fabric-llm-finetune-bitnet

MedPsy-1.7B

MedPsy-4B

MedPsy-4B-GGUF

TranscribePsy-250M-EU-sub-gguf

TranslatePsy-AfriSLM-0.8B

TranslatePsy-AfriSLM-0.8B-Q4-GGUF

TranslatePsy-AfriSLM-0.8B-Q8-GGUF

TranslatePsy-AfriSLM-4B

VisionPsy-Nano-460M

VisionPsy-Nano-460M-Flash

VisionPsy-Nano-460M-Flash-GGUFs

VisionPsy-Nano-460M-GGUFs

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。