Mac Studio M5 Ultra 512GB has 512GB of unified memory and is a strong fit for frontier-class local model experiments. These recommendations are generated from the current LocalClaw catalogue and filtered for realistic memory headroom.
Apple schedules the 512GB configuration for late October; exact Amazon availability is unverified.
Yes. With 512GB of unified memory, Mac Studio M5 Ultra 512GB fits 248 current LocalClaw catalogue models under the conservative 8k-context memory filter. Start with MiMo-V2.6-Flash-RL. This desktop generation launched September 22, 2026. Apple schedules the 512GB configuration for late October; exact Amazon availability is unverified. This is memory-fit guidance, not a hands-on speed benchmark.
Hardware facts come from Apple. LocalClaw separately calculates catalogue compatibility from unified memory and model requirements. No unreleased Mac performance result is inferred from chip specifications.
Yes. With 512GB of unified memory, Mac Studio M5 Ultra 512GB fits 248 current LocalClaw catalogue models under the conservative 8k-context memory filter. Start with MiMo-V2.6-Flash-RL. This desktop generation launched September 22, 2026. Apple schedules the 512GB configuration for late October; exact Amazon availability is unverified. This is memory-fit guidance, not a hands-on speed benchmark.
This configuration has 512GB of unified memory. Apple lists up to 512GB for the M5 Ultra family represented here. LocalClaw reserves memory for macOS, the runtime and an 8k context before marking a model compatible.
The generation launched September 22, 2026. Apple schedules the 512GB configuration for late October; exact Amazon availability is unverified.
No. The compatibility count and ranking are calculated from 512GB of unified memory and the current LocalClaw catalogue. They are not measured tokens-per-second results or hands-on benchmarks.
Start with MiMo-V2.6-Flash-RL on this Mac. LocalClaw has not hands-on validated runtime speed. Model fit is calculated conservatively from Apple-confirmed unified memory. A tight fit can still work, but close other apps, reduce context length when needed, and prefer the listed quantization.
Official Xiaomi MiMo MIT-licensed sparse MoE with 309B total / 15B active parameters, native text-image-video-audio support, 1M-token context and ggml-org GGUF artifacts for llama.cpp-class workstation experiments.
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 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.
Liquid AI LFM2.5 reasoning MoE built for private on-device agents. The official LFM 1.0 release has 24B total / 2.45B active parameters, 131K context, official GGUF and MLX artifacts, and a 14.5GB Q4_K_M GGUF for laptop/workstation local use.
Z.ai flagship open model for long-horizon coding, reasoning and agentic work. 744B total, 40B active, 1M-token context, MIT license. Unsloth Dynamic GGUF makes it technically local, but it needs workstation/server-class memory: ~245GB total memory for 2-bit and 372GB+ for 4-bit.
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.
This guide is about local AI fit, not live pricing. Prices and availability change. An Amazon link may be an affiliate link that supports LocalClaw at no extra cost. Confirm the exact memory and storage on Amazon before ordering.