Do NOT buy a 512GB Mac Studio ‼️ Two 256GB Mac Studio M5 Ultras are ~$700 cheaper and have ~1.7x faster prefill and decode Price: $22,299 vs $21,598 Both are the 36-core CPU / 80-core GPU You can connect 2x 256GB with one Thunderbolt 5 cable + RDMA, which is fast enough to run one model across both Prefill (compute) - 1x 512 does ~135 TFLOPS - 2x 256 does ~215-245 TFLOPS - Apple claims up to 3x for a 4-Mac cluster, which works out to ~1.7x for two Decode (bandwidth) - 1x 512 has 1.2 TB/s - 2x 256 gets ~1.9-2.2 TB/s effective - With tensor parallelism each box reads its half of the weights at the same time, which works out to ~1.7x for two GLM-5.3 goes from ~33-40 tok/s to ~55-70 DeepSeek V4.1 Flash goes from ~45-60 tok/s to ~75-100 Both are 4-bit with spec decode on, estimated from M3 and M5 Ultra benchmarks If you want Qwen3.8-Flash-Next or GLM-5.3-Flash, a single 256 already runs them well and you don’t need either setup. The 512GB question is about DeepSeek V4.1 Flash (552B) and GLM-5.3 (753B), tied as the best open models on Artificial Analysis v5. They need 310-420GB at 4-bit, so neither fits on one 256

I switched my Hermes agent to 100% free local models with Magnitude It’s running Qwen 3.6 35B-A3B at ~60 tok/s on my DGX Spark. It’s free, private, and always on running background tasks Hermes set Magnitude up itself. With the CLI it: - Profiled my hardware and found the best models for it - Walked me through the options and let me decide - Switched itself over From there, models load just in time as the agent works and unload when idle Works with Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline Copy this prompt and send it to your agent: “Set up local models for me with the Magnitude CLI. Install it with `npm i -g @magnitudedev/cli` (or my package manager), then run `magnitude docs onboarding` and follow the instructions” GitHub:

We compared the M5 Ultra Mac Studio 512GB with 4x DGX Spark and 4x AMD Ryzen AI Halo The prices are relatively the same ($15-20k), but the trade-offs are noticeable Compute (prompt processing speed) - Mac: one chip, all the compute is always usable. Quick on normal prompts, will feel slower on long ones - Sparks: boxes link over 200GbE, which is fast enough to combine their compute. Fastest of the three, and native FP4 speeds up quantized models even more - Halos: boxes link over 10GbE, too slow to share work properly. Ends up close to the Mac but somewhat worse Bandwidth (token generation speed) - Mac: 512GB on a single bus, faster than a 4090, ~4x the tokens/sec of Spark or Halos - Sparks: the model splits across boxes and tokens pass through them in sequence, so 4 boxes still generate at 1 box's speed - Halos: same ceiling as Spark, same reason Power - Mac: less than a gaming PC, silent - Sparks: nearly maxes out a wall circuit, will be hot - Halos: about half the Sparks, but somewhat hot The real gap is bandwidth. The Studio will feel noticeably faster. Spark 2 is rumored soon, but it won't matter unless the bandwidth goes way up. Same for AMD, plus better interconnect speeds