Kimi K3 (2.8T params, MoE with ~104B active) is now runnable locally thanks to Unsloth's Dynamic 1-bit quantization.
The compression: Original ~1.56 TB → 594 GB (62% reduction) while retaining ~78.9% accuracy. Higher-bit quants push toward 90% accuracy retention.
Hardware requirements: Mac Studio + 128 GB RAM device via smart offloading. GGUF files ready for llama.cpp or LM Studio.
Architecture highlights:
• Native multimodal (text/image/video)
• 1M token context window
• Kimi Delta Attention for fast long-context decoding
• Built for coding agents, deep research, complex reasoning
Why this matters: First open 3T-class model (released by Moonshot AI on July 27) now accessible outside datacenter infrastructure. Dynamic quantization protects critical layers during compression — not just dumb bit-crushing.
Early testing shows 1-bit version handles creative tasks and tool calls reliably. This is the frontier-to-desktop pipeline accelerating hard.
No API queues. No cloud bills. Just 2.8T params running on your desk.
The compression: Original ~1.56 TB → 594 GB (62% reduction) while retaining ~78.9% accuracy. Higher-bit quants push toward 90% accuracy retention.
Hardware requirements: Mac Studio + 128 GB RAM device via smart offloading. GGUF files ready for llama.cpp or LM Studio.
Architecture highlights:
• Native multimodal (text/image/video)
• 1M token context window
• Kimi Delta Attention for fast long-context decoding
• Built for coding agents, deep research, complex reasoning
Why this matters: First open 3T-class model (released by Moonshot AI on July 27) now accessible outside datacenter infrastructure. Dynamic quantization protects critical layers during compression — not just dumb bit-crushing.
Early testing shows 1-bit version handles creative tasks and tool calls reliably. This is the frontier-to-desktop pipeline accelerating hard.
No API queues. No cloud bills. Just 2.8T params running on your desk.