Browse Models

Qwen3.5-9B-Uncensored-HauhauCS-Aggressive

HauhauCS9B16 GB RAM

9B open-weight model from HauhauCS for local AI inference.

gemma-4-26B-A4B-it-qat-GGUF

Google26B24 GB RAM

26B open-weight model from Google for local AI inference.

gpt-oss-20b-GGUF

unsloth20B24 GB RAM

20B open-weight model from unsloth for local AI inference.

Qwen3-VL-8B-Instruct-abliterated-GGUF

Alibaba8B16 GB RAM

Qwen3-VL-8B-Instruct-abliterated-GGUF is an 8-billion parameter vision-language model developed by Alibaba that has been fully abliterated to remove proprietary constraints while retaining core capabilities. This model excels at handling complex visual reasoning and multi-step tasks within a conversational framework, making it ideal for open-ended analysis and creative generation in the US region. Running this GGUF quantized version locally is highly practical for users with mid-range GPUs, offering fast inference speeds without requiring specialized enterprise hardware.

Flux2-Klein-9B-True-V2

wikeeyang9B16 GB RAM

9B open-weight model from wikeeyang for local AI inference.

Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

DavidAU27B24 GB RAM

The Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF is a 27-billion parameter large language model created by DavidAU that combines multiple fine-tuning techniques including Unsloth and Heretic methods. This model excels at generating uncensored, highly creative, and unrestricted content across diverse topics, making it ideal for users seeking an abliterated assistant capable of multi-stage tuned responses without safety filters. Running this GGUF quantized version locally requires a substantial GPU with ample VRAM to handle the 27B parameter load efficiently, offering best performance on systems equipped with high-end hardware for fast inference speeds.

Qwen3-Coder-30B-A3B-Instruct-GGUF

Alibaba30B24 GB RAM

Qwen3-Coder-30B-A3B-Instruct-GGUF is a 30-billion parameter coding model developed by Alibaba that integrates advanced instruction tuning for specialized tasks. It excels at complex code generation, debugging, and conversational programming assistance, making it ideal for developers seeking high-performance solutions in the US region. Running this model locally requires substantial GPU memory to handle its large parameter count, so it is best suited for users with powerful hardware who prioritize raw coding capability over speed.

gemma-4-31B-it-GGUF

Google31B24 GB RAM

31B open-weight model from Google for local AI inference.

Gemma-4-E4B-Uncensored-HauhauCS-Aggressive

HauhauCS4B8 GB RAM

Gemma-4-E4B-Uncensored-HauhauCS-Aggressive is a 4-billion parameter multimodal model developed by HauhauCS that integrates advanced capabilities from Gemma 4 with ablated safety filters. This aggressive variant excels at unrestricted text generation, vision analysis, and audio processing, making it ideal for creative tasks requiring raw output without content moderation. Running this model locally demands significant GPU memory to handle its multimodal inputs, and users should expect high computational costs when processing complex audio or video streams.

Qwopus3.6-27B-Coder-Compat-MTP-GGUF

Jackrong27B24 GB RAM

27B open-weight model from Jackrong for local AI inference.

UI-TARS-1.5-7B-GGUF

mradermacher7B8 GB RAM

7B open-weight model from mradermacher for local AI inference.

Huihui-DeepSeek-V4-Flash-abliterated-ds4-GGUF

huihui-aiUnknown8 GB RAM

Unknown open-weight model from huihui-ai for local AI inference.

gemma-4-12B-it-QAT-GGUF

Google12B16 GB RAM

12B open-weight model from Google for local AI inference.

Ternary-Bonsai-27B-gguf

prism-ml27B24 GB RAM

27B open-weight model from prism-ml for local AI inference.

Gemmable-4-12B-MTP-GGUF

Mia-AiLab12B16 GB RAM

12B open-weight model from Mia-AiLab for local AI inference.

Qwopus3.6-35B-A3B-Coder-MTP-GGUF

Jackrong35B48 GB RAM

35B open-weight model from Jackrong for local AI inference.

Qwen3.6-35B-A3B-MTP-GGUF

Alibaba35B48 GB RAM

35B open-weight model from Alibaba for local AI inference.

gemma-4-12b-it-GGUF

Google12B16 GB RAM

12B open-weight model from Google for local AI inference.

Qwen3-VL-30B-A3B-Instruct-GGUF

Qwen30B24 GB RAM

30B open-weight model from Qwen for local AI inference.

models-moved

ggml-orgUnknown8 GB RAM

The models-moved collection is provided by ggml-org and currently lists an unknown parameter count for its various model files. These models excel at general-purpose tasks within the US region and are best suited for users seeking accessible inference options from this specific provider. Running them locally requires downloading the appropriate GGUF quantization files, with performance varying significantly based on your GPU or CPU hardware capabilities.

cohere-transcribe-03-2026-gguf

handy-computerUnknown8 GB RAM

Unknown open-weight model from handy-computer for local AI inference.

Qwen-AgentWorld-35B-A3B-GGUF

Alibaba35B48 GB RAM

35B open-weight model from Alibaba for local AI inference.

vntl-llama3-8b-v2-gguf

lmg-anon8B16 GB RAM

The vntl-llama3-8b-v2-gguf is an 8B parameter language model developed by lmg-anon that specializes in high-quality translation and conversational tasks. It excels at processing the VNTL-v5-1k dataset to deliver fluent responses, making it ideal for multilingual chat applications and localized content generation. Running this model locally requires a GPU with sufficient VRAM to handle its 8B parameter weight efficiently, ensuring responsive performance for real-time dialogue.

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