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.

ollama pull qwen3-vl-8b-instruct-abliterated:8b

Flux2-Klein-9B-True-V2

wikeeyang9B16 GB RAM

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

ollama pull flux2-klein-9b-true-v2:9b

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.

ollama pull qwen3.6-27b-fable-fusion-711-uncensored-heretic-nm-dau-neo-max-mtp:27b

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.

ollama pull qwen3-coder-30b-a3b:30b

gemma-4-31B-it-GGUF

Google31B24 GB RAM

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

ollama pull gemma-4-31b-it:31b

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.

ollama pull gemma-4-e4b-uncensored-hauhaucs-aggressive:4b

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

Jackrong27B24 GB RAM

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

ollama pull qwopus3.6-27b-coder-compat-mtp:27b

UI-TARS-1.5-7B-GGUF

mradermacher7B8 GB RAM

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

ollama pull ui-tars-1.5-7b:7b

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

huihui-aiUnknown8 GB RAM

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

ollama pull huihui-deepseek-v4-flash-abliterated-ds4:latest

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

Google12B16 GB RAM

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

ollama pull gemma-4-12b-it-qat:12b

Ternary-Bonsai-27B-gguf

prism-ml27B24 GB RAM

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

ollama pull ternary-bonsai-27b:27b

Gemmable-4-12B-MTP-GGUF

Mia-AiLab12B16 GB RAM

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

ollama pull gemmable-4-12b-mtp:12b

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

Jackrong35B48 GB RAM

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

ollama pull qwopus3.6-35b-a3b-coder-mtp:35b

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

Alibaba35B48 GB RAM

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

ollama pull qwen3.6-35b-a3b-mtp:35b

gemma-4-12b-it-GGUF

Google12B16 GB RAM

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

ollama pull gemma-4-12b-it:12b

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

Qwen30B24 GB RAM

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

ollama pull qwen3-vl-30b-a3b:30b

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.

ollama pull models-moved:latest

cohere-transcribe-03-2026-gguf

handy-computerUnknown8 GB RAM

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

ollama pull cohere-transcribe-03-2026:latest

Qwen-AgentWorld-35B-A3B-GGUF

Alibaba35B48 GB RAM

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

ollama pull qwen-agentworld-35b-a3b:35b

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.

ollama pull vntl-llama3-8b-v2:8b

gemma-4-E4B-it-GGUF

Google4B8 GB RAM

The gemma-4-E4B-it-GGUF is a 4-billion parameter language model developed by Google and optimized for Italian using GGUF quantization. It excels at handling complex reasoning tasks within the Gemma 4 architecture while supporting efficient image-to-text conversions through specialized unsloth optimizations. Running this model locally requires a modern GPU to leverage its speed, making it ideal for users seeking high-performance local inference without cloud dependencies.

ollama pull gemma-4-e4b-it:4b

Qwen3.6-27B-GGUF

Alibaba27B24 GB RAM

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.

ollama pull qwen3.6-27b:27b

Qwen3.6-35B-A3B-GGUF

Alibaba35B48 GB RAM

Qwen3.6-35B-A3B-GGUF is a 35-billion parameter large language model developed by Alibaba that features specialized optimizations for image-to-text conversion and multilingual conversation. This model excels at handling complex visual reasoning tasks and maintaining coherent dialogue, making it ideal for applications requiring deep contextual understanding across diverse topics. Running this model locally requires substantial GPU memory to handle its full parameter count, though quantized GGUF versions can offer a practical balance between speed and performance on high-end consumer hardware.

ollama pull qwen3.6-35b-a3b:35b

Hy3-GGUF

vcruz305Unknown8 GB RAM

Unknown open-weight model from vcruz305 for local AI inference.

ollama pull hy3:latest

HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF

rippertnt1.5B4 GB RAM

HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF is a 1.5 billion parameter language model created by rippertnt and optimized for use with llama-cpp. It excels at conversational tasks and general text generation, making it ideal for lightweight chat applications that require efficient inference on standard hardware. Running this model locally is practical even on modest systems due to its quantized format, offering fast response times without the need for high-end GPUs.

ollama pull hyperclovax-seed-text-instruct-1.5b-q4-k-m:1.5b

Qwen3.5-9B-GGUF

Alibaba9B16 GB RAM

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.

ollama pull qwen3.5-9b:9b

Qwen3.5-4B-GGUF

Alibaba4B8 GB RAM

Qwen3.5-4B-GGUF is a compact 4-billion parameter language model developed by Alibaba that supports conversational tasks and image-to-text conversion. It excels at handling lightweight inference workloads while maintaining strong performance in unsloth-optimized environments for text generation. Running this model locally requires minimal hardware resources, making it ideal for users seeking fast, efficient deployment on standard consumer devices.

ollama pull qwen3.5-4b:4b

parakeet-unified-en-0.6b-gguf

handy-computer0.6B2 GB RAM

0.6B open-weight model from handy-computer for local AI inference.

ollama pull parakeet-unified-en-0.6b:0.6b

nemotron-3.5-asr-streaming-0.6b-gguf

handy-computer0.6B2 GB RAM

0.6B open-weight model from handy-computer for local AI inference.

ollama pull nemotron-3.5-asr-streaming-0.6b:0.6b

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

Google26B24 GB RAM

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.

ollama pull gemma-4-26b-a4b-it:26b

Bonsai-27B-gguf

prism-ml27B24 GB RAM

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.

ollama pull bonsai-27b:27b

Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

HauhauCS35B48 GB RAM

Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive is a 35-billion parameter large language model developed by HauhauCS that combines MoE architecture with advanced multimodal and vision capabilities. It excels at complex reasoning, multilingual image-text analysis, and generating uncensored content for aggressive or unrestricted use cases where standard safety filters are undesirable. Running this model locally requires substantial high-end GPU memory to handle its sparse mixture-of-experts structure, making it best suited for powerful workstations or servers with significant VRAM available.

ollama pull qwen3.6-35b-a3b-uncensored-hauhaucs-aggressive:35b

Qwythos-9B-Claude-Mythos-5-1M-GGUF

empero-ai9B16 GB RAM

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

ollama pull qwythos-9b-claude-mythos-5-1m:9b

Ornith-1.0-35B-GGUF

ornith-ai35B48 GB RAM

Ornith-1.0-35B-GGUF is a 35-billion parameter language model developed by ornith-ai specifically for conversational tasks within the US region. It excels at maintaining natural dialogue flows and handling context-aware interactions, making it ideal for chatbots and virtual assistants. Running this model locally requires substantial GPU memory to handle its size efficiently, so it is best suited for users with high-end hardware or those willing to use quantized GGUF versions to reduce resource demands.

ollama pull ornith-1.0-35b:35b

Qwen3.6-27B-MTP-GGUF

Alibaba27B24 GB RAM

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.

ollama pull qwen3.6-27b-mtp:27b

deepseek-v4-gguf

antirezUnknown8 GB RAM

The deepseek-v4-gguf model is a quantized Mixture-of-Experts architecture provided by antirez with unknown parameter counts available in 2-bit and 4-bit formats. It excels at efficient inference for large language tasks while maintaining high performance through its specialized MoE structure, making it ideal for users needing reduced memory footprints without significant capability loss. Running this model locally requires moderate hardware resources to handle the quantized weights effectively, offering a fast and practical solution for deploying advanced AI capabilities on consumer-grade systems.

ollama pull deepseek-v4:latest

Ornith-1.0-9B-GGUF

ornith-ai9B16 GB RAM

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.

ollama pull ornith-1.0-9b:9b

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