7B Modelle

Beste 7B Plaaslike KI Modelle

Kompakte modelle in die 7B-9B-reeks - die ideale keuse vir gebruik op 'n skootrekenaar of 'n enkele verbruikers-GPU.

Llama-3-8B-Instruct-32k-v0.1-GGUF

MaziyarPanahi8B16 GB RAM

8B oopgewigmodel van MaziyarPanahi vir plaaslike KI-inferensie.

Mistral-7B-Instruct-v0.3-GGUF

MaziyarPanahi7B8 GB RAM

7B oopgewigmodel van MaziyarPanahi vir plaaslike KI-inferensie.

Meta-Llama-3-8B-Instruksies-GGUF

MaziyarPanahi8B16 GB RAM

8B oopgewigmodel van MaziyarPanahi vir plaaslike KI-inferensie.

Parable-Granite-4.1-8B-Claude-Fable-5-GGUF

AnkitAI8B16 GB RAM

8B oopgewigmodel van AnkitAI vir plaaslike KI-inferensie.

Ternêre-Bonsai-8B-gguf

prisma-ml8B16 GB RAM

8B oopgewigmodel van prisma-ml vir plaaslike KI-inferensie.

Qwen3.5-9B-DeepSeek-V4-Flash-GGUF

Jackrong9B16 GB RAM

9B oopgewigmodel van Jackrong vir plaaslike KI-inferensie.

Gelykenis-Qwen3-8B-Claude-Fabel-5-GGUF

AnkitAI8B16 GB RAM

8B oopgewigmodel van AnkitAI vir plaaslike KI-inferensie.

Qwen2.5-7B-Instruksie-GGUF

Alibaba7B8 GB RAM

7B oopgewigmodel van Alibaba vir plaaslike KI-inferensie.

Qwen2.5-VL-7B-Instruksies-GGUF

Alibaba7B8 GB RAM

7B oopgewigmodel van Alibaba vir plaaslike KI-inferensie.

Qwen2.5-Koder-7B-Instruksie-GGUF

Qwen7B8 GB RAM

7B oopgewigmodel van Qwen vir plaaslike KI-inferensie.

Qwen3.5-9B-Die-Uitdagende-Fabel-Ongesensureerde-Ketter-NEO-IMATRIX-MAX-MTP-GGUF

DavidAU9B16 GB RAM

9B oopgewigmodel van DavidAU vir plaaslike KI-inferensie.

Meta-Llama-3.1-8B-Instruksies-GGUF

meta8B16 GB RAM

8B oopgewigmodel van Meta vir plaaslike KI-inferensie.

Qwen3-8B-GGUF

Alibaba8B16 GB RAM

8B oopgewigmodel van Alibaba vir plaaslike KI-inferensie.

Qwythos-9B-v2-GGUF

empero-ai9B16 GB RAM

9B oopgewigmodel van empero-ai vir plaaslike KI-inferensie.

Qwen3.5-9B-Ongesensureerd-HauhauCS-Aggressief

HauhauCS9B16 GB RAM

9B oopgewigmodel van HauhauCS vir plaaslike KI-inferensie.

Qwen3-VL-8B-Instruksies-verwyder-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

wykeyang9B16 GB RAM

9B oopgewigmodel van wieeyang vir plaaslike KI-inferensie.

UI-TARS-1.5-7B-GGUF

mradermacher7B8 GB RAM

7B oopgewigmodel van mradermacher vir plaaslike KI-inferensie.

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.

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.

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

empero-ai9B16 GB RAM

9B oopgewigmodel van empero-ai vir plaaslike KI-inferensie.

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.

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