Best Local AI Models for High-VRAM (24GB GPU)
Models for 4090 / 3090 / 7900 XTX class GPUs.
For High-VRAM (24GB GPU) setups, Magnum v4 72B is the definitive choice, offering unmatched performance and capability. If you need a slightly more balanced option, Euryale L3.3 70B v2.3 is a close second.
High-VRAM (24GB GPU) setups are designed for users who demand the absolute best in terms of performance and model capacity. These systems can handle the largest and most complex models, making them ideal for tasks that require deep understanding and high-quality outputs. Local deployment ensures data privacy, reduces latency, and provides full control over the model's capabilities, which is crucial for enterprise and professional applications.
Top picks
- #1
Magnum v4 72B72B · apache-2.0 · min 44.7GB
The ultimate choice for maximum power and capability.
Magnum v4 72B stands out as the top pick for High-VRAM (24GB GPU) due to its massive 72 billion parameters and the ability to push the limits of your GPU. Despite requiring 44.7GB of VRAM, it offers unparalleled performance and quality, making it perfect for advanced NLP tasks and large-scale projects. Licensed under Apache-2.0, it is open-source and suitable for both commercial and non-commercial use. The only caveat is that it may be overkill for simpler tasks, but for those looking to maximize their hardware's potential, Magnum v4 72B is the clear winner.
- #2
Euryale L3.3 70B v2.370B · llama3 · min 40.1GB
A close second with robust performance and a strong community.
Euryale L3.3 70B v2.3 is a formidable runner-up, boasting 70 billion parameters and requiring 40.1GB of VRAM. This model is known for its high-quality outputs and is licensed under LLaMA3, making it a reliable choice for a wide range of applications. It is slightly less resource-intensive than Magnum v4 72B, making it a more practical option for users who want to balance performance with resource management. Its strong community support and frequent updates ensure that it remains a top choice for high-VRAM setups.
- #3
llama-3.1-70b-instruct
High-quality instruction-following with a focus on versatility.
Llama 3.1 70B Instruct is a powerful model with 70 billion parameters and a minimum VRAM requirement of 40.1GB. It excels in instruction-following tasks, making it ideal for applications that require precise and context-aware responses. Licensed under LLaMA3.1, it offers a versatile set of capabilities and is well-suited for both research and production environments. While it is slightly behind Magnum v4 72B and Euryale L3.3 70B v2.3 in raw performance, its specialized instruction-following abilities make it a strong third choice.
- #4
Skyfall 31B v4.231B · other · min 18.2GB
Balanced performance and resource usage for a wide range of tasks.
Skyfall 31B v4.2 is a well-rounded model with 31 billion parameters and a minimum VRAM requirement of 18.2GB. It offers a good balance between performance and resource usage, making it suitable for a wide range of tasks without pushing the limits of your GPU. Licensed under a proprietary license, it is a reliable choice for users who need a high-quality model without the extreme resource demands of larger models. Its versatility and efficiency make it a solid fourth choice for high-VRAM setups.
- #5
Mixtral 8x7B Instruct46.7B · apache-2.0 · min 25.1GB
Efficient and effective for mid-range tasks.
Mixtral 8x7B Instruct is a highly efficient model with 46.7 billion parameters and a minimum VRAM requirement of 25.1GB. It is designed to provide high-quality instruction-following capabilities while being more resource-friendly than the larger models. Licensed under Apache-2.0, it is open-source and suitable for both commercial and non-commercial use. While it may not match the raw power of the top three picks, it offers a compelling combination of performance and efficiency, making it a strong fifth choice for high-VRAM setups.
Hardware guidance
For High-VRAM (24GB GPU) setups, a 4090, 3090, or 7900 XTX class GPU is essential to handle the largest models. Users with 8GB or 12GB VRAM GPUs should consider smaller models like Magnum v4 22B or Dolphin Mistral 24B (Venice Edition). Those with 16GB VRAM can explore models like Skyfall 31B v4.2 or Qwen3 30B-A3B. For 24GB+ VRAM, the top picks like Magnum v4 72B, Euryale L3.3 70B v2.3, and Llama 3.1 70B Instruct are the best choices to fully leverage the available resources.
When to skip local
While local models offer significant advantages, there are scenarios where a hosted API might still be preferable. For example, if you need to scale quickly or handle extremely large datasets, cloud-based solutions like Anthropic's Claude or Google's PaLM API might be more suitable. Additionally, hosted APIs often provide better support and maintenance, which can be crucial for mission-critical applications.
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