Contents
- 0.1 gemma-4-26B-A4B-it-qat-GGUF
- 0.2 gpt-oss-20b-GGUF
- 0.3 Qwen3-VL-8B-Instruct-abliterated-GGUF
- 0.4 Flux2-Klein-9B-True-V2
- 0.5 Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
- 0.6 Qwen3-Coder-30B-A3B-Instruct-GGUF
- 0.7 gemma-4-31B-it-GGUF
- 0.8 Qwopus3.6-27B-Coder-Compat-MTP-GGUF
- 0.9 gemma-4-12B-it-QAT-GGUF
- 0.10 Ternary-Bonsai-27B-gguf
- 0.11 Gemmable-4-12B-MTP-GGUF
- 0.12 gemma-4-12b-it-GGUF
- 0.13 Qwen3-VL-30B-A3B-Instruct-GGUF
- 0.14 vntl-llama3-8b-v2-gguf
- 0.15 Qwen3.6-27B-GGUF
- 0.16 Qwen3.5-9B-GGUF
- 0.17 gemma-4-26B-A4B-it-GGUF
- 0.18 Bonsai-27B-gguf
- 0.19 Qwythos-9B-Claude-Mythos-5-1M-GGUF
- 0.20 Qwen3.6-27B-MTP-GGUF
- 0.21 Ornith-1.0-9B-GGUF
- 1 Hello, Nice to meet you!
gemma-4-26B-A4B-it-qat-GGUF
26B open-weight model from Google for local AI inference.
Qwen3-VL-8B-Instruct-abliterated-GGUF
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
9B open-weight model from wikeeyang for local AI inference.
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
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
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.
Qwopus3.6-27B-Coder-Compat-MTP-GGUF
27B open-weight model from Jackrong for local AI inference.
Ternary-Bonsai-27B-gguf
27B open-weight model from prism-ml for local AI inference.
Gemmable-4-12B-MTP-GGUF
12B open-weight model from Mia-AiLab for local AI inference.
Qwen3-VL-30B-A3B-Instruct-GGUF
30B open-weight model from Qwen for local AI inference.
vntl-llama3-8b-v2-gguf
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.
Qwen3.6-27B-GGUF
Qwen3.6-27B-GGUF is a 27-billion parameter large language model developed by Alibaba that supports advanced conversational AI and image-to-text capabilities. It excels at complex reasoning tasks, multilingual communication, and visual analysis, making it ideal for professional applications requiring high accuracy in text generation and understanding. Running this model locally typically demands substantial GPU memory and a powerful processor to handle its 27B parameters efficiently, so it is best suited for users with dedicated hardware or access to cloud instances.
Qwen3.5-9B-GGUF
Qwen3.5-9B-GGUF is a compact large language model developed by Alibaba containing 9 billion parameters. It excels at conversational tasks and image-to-text conversion, making it ideal for lightweight applications that require efficient region US deployment. Running this model locally is practical on consumer-grade hardware thanks to its small footprint, offering fast inference speeds even on modest GPUs when paired with optimization libraries like Unsloth.
gemma-4-26B-A4B-it-GGUF
The gemma-4-26B-A4B-it-GGUF is a 26-billion parameter large language model developed by Google that leverages advanced instruction tuning for high-performance reasoning. This model excels at complex text generation and multimodal tasks, making it ideal for applications requiring deep contextual understanding and precise instruction following. Running this model locally demands substantial GPU memory and significant compute power, so it is best suited for users with high-end hardware or those utilizing optimized inference frameworks like Unsloth to manage its resource requirements.
Bonsai-27B-gguf
Bonsai-27B-gguf is a compact 27-billion parameter language model developed by prism-ml that utilizes quantization for efficient deployment. It excels at conversational tasks and general reasoning while running smoothly on llama.cpp with support for both CPU and CUDA hardware acceleration. Users can run this model locally on modest hardware, though performance will vary depending on whether they utilize 1-bit quantization or have access to a GPU.
Qwythos-9B-Claude-Mythos-5-1M-GGUF
9B open-weight model from empero-ai for local AI inference.
Qwen3.6-27B-MTP-GGUF
Qwen3.6-27B-MTP-GGUF is a 27-billion parameter large language model developed by Alibaba that supports advanced conversational tasks and image-to-text conversion. It excels at handling complex reasoning and multi-turn dialogues, making it ideal for applications requiring deep contextual understanding and visual analysis. Running this model locally typically demands high-end GPU hardware to manage its substantial memory footprint, though quantized GGUF versions can offer a practical balance between speed and performance on consumer-grade systems.
Ornith-1.0-9B-GGUF
Ornith-1.0-9B-GGUF is a 9-billion parameter language model developed by ornith-ai designed for conversational interactions within the US region. It excels at generating natural dialogue and handling casual chat tasks, making it ideal for lightweight virtual assistants or simple customer support bots. Running this model locally requires modest hardware resources, allowing it to operate quickly on consumer-grade GPUs without needing massive data centers.
