Qwen-3-Coder-Next is an 80 billion parameter model that requires 159.4GB of RAM to run. Techniques exist to reduce the size of large language models by 4x and increase their speed by 2x.
ngrok.com
26 min
4d ago
Hypura is a storage-tier-aware LLM inference scheduler designed for Apple Silicon, allowing users to run large models that exceed their Mac's memory. It optimally distributes model tensors across GPU, RAM, and NVMe storage based on access patterns and hardware capabilities to prevent system crashes.
github.com
6 min
5d ago
Qwen-3-Coder-Next is an 80 billion parameter model that requires 159.4GB of RAM to run. Techniques exist to reduce the size of large language models by 4x and increase their speed by 2x.
ngrok.com
26 min
4d ago
Hypura is a storage-tier-aware LLM inference scheduler designed for Apple Silicon, allowing users to run large models that exceed their Mac's memory. It optimally distributes model tensors across GPU, RAM, and NVMe storage based on access patterns and hardware capabilities to prevent system crashes.
github.com
6 min
5d ago
Qwen-3-Coder-Next is an 80 billion parameter model that requires 159.4GB of RAM to run. Techniques exist to reduce the size of large language models by 4x and increase their speed by 2x.
ngrok.com
26 min
4d ago
Hypura is a storage-tier-aware LLM inference scheduler designed for Apple Silicon, allowing users to run large models that exceed their Mac's memory. It optimally distributes model tensors across GPU, RAM, and NVMe storage based on access patterns and hardware capabilities to prevent system crashes.
github.com
6 min
5d ago
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