Mac Studio M3 Ultra 512GB has 512GB of unified memory and is a strong fit for server-scale local AI on Apple silicon. These recommendations are generated from the current LocalClaw catalogue and filtered for realistic memory headroom.
Recommendations updated September 28, 2026
Start with DeepSeek V4 Flash Vision Exp on this Mac. A comfortable or good fit leaves useful memory for macOS and your local runtime. A tight fit can still work, but close other apps, reduce context length when needed, and prefer the listed quantization.
Official MIT DeepSeek V4 Flash multimodal experiment with image understanding, 1M context and Unsloth Dynamic GGUF artifacts. The lightest practical GGUF is roughly 82-97GB, while higher-quality Q4/Q8 builds are about 155-162GB, so this belongs on large-memory workstations.
Official MIT DeepSeek V4 Flash successor release with stronger agentic coding, DSpark speculative decoding support and a practical Unsloth Dynamic GGUF path. Still a large workstation/server local model: Q4 is about 155GB and Q8 is about 162GB.
Official MIT DeepSeek V4.1 Flash release with CED architecture, CSA2 attention, multimodal input and 1M-token context. Community GGUF artifacts include split Q2_K/Q3/Q4 builds plus a llama.cpp patch path; use only on 256GB+ workstations, with 384GB+ safer.
Official China Telecom XingChen-AGI Apache 2.0 MoE release with 29B total parameters, 4B active parameters, 256K native context and an official IQ4_NL GGUF path for llama.cpp-class local inference on 24GB GPU workstations.
Accio Lab Apache 2.0 co-work model post-trained from Qwen3.6-35B-A3B for long-horizon tools, files, code and business workflows. Official GGUF Q4_K_M is 19.7GiB with llama.cpp validation evidence.
Official Nex-AGI Apache 2.0 multimodal agent model for computer use, web browsing, coding and tool calling. Community Q4_K_M GGUF is about 21.3GB with documented llama.cpp text and vision smoke tests.
Official Qwen sparse multimodal MoE preview with 125B model parameters plus 51B n-gram embeddings, about 6B active parameters, Qwen Community 1.0 licensing, 262K native context and local Q4_K_M GGUF paths for llama.cpp, Ollama and LM Studio-class runtimes.
Official MIT 35B MoE reasoning model from Ornith AI with about 3B active parameters, 262K context, strong agentic-coding positioning and official Q4_K_M GGUF plus MLX/Ollama/llama.cpp local paths for larger workstations.
Meta Superintelligence Lab local agent model with text+image input, 131K context, Apache 2.0 weights and official GGUF/ExecuTorch artifacts. The K-Quant 17GB build targets 24GB machines; 32GB is safer for vision and long-context sessions.
The shared LocalClaw engine first rejects hosted-only, excluded and oversized records. It reserves system and 8k-context headroom, labels comfortable, good and tight fits, then ranks the remaining models by hardware fit, use case, catalogue capability ratings, runtime and freshness. Community stars are never included. This is practical guidance, not a standardized third-party benchmark.
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