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These three were onboarded on 2026-07-17 and are live in nodes.json, but the docs still described a three-machine fleet (M3 Ultra / M1 Ultra / M4 Pro). Anyone reading CLUSTER.md to decide where a job should land was working from a stale map. Records the M1 Max as a second full gen node (flux_local, hunyuan3d_mlx; NOT trellis - M1 bf16), and the M4 mini / M1 mini as light bg_remove + cpu helpers. Also notes the M1 Max login split: admin is `m1max`, but the checkout and farm run as `studio`, which needs /opt/homebrew/bin on its login PATH. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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MODELBEAST hardware guide — what runs on which Mac
Which Apple Silicon Mac can run which operator, by unified memory (RAM). Numbers below are measured on the M3 Ultra unless marked (est.); peak = peak unified memory during a run.
How to read this
- Unified memory is the gate. CPU and GPU share one pool. A model's peak must fit in
RAM − ~3-5GB(macOS + apps). For comfort without swapping, want peak + 8-12GB headroom. Swapping = usable but slow. - Chip tier = speed, not fit. An M1 Max 64GB runs the same models as an M3 Max 64GB, just slower. Rough per-generation speed: Ultra > Max > Pro > base, and M5 > M4 > M3 > M2 > M1 per core. Our M1 Ultra runs the 3D stack at ~2× the M3 Ultra's time — fine for throughput, not for latency.
netoperators run on any Mac — an 8GB M1 mini is a perfectly good node forfal_*/openrouter_image, because the compute is in the cloud. Same for lightcpuops.- bf16 caveat (M1/M2): M1 (and to a lesser extent M2) lack full hardware bf16.
trellis_mac(torch-MPS, bf16) is unverified on M1/M2 — treat as M3+.hunyuan3d_mlxis fp16 MLX and runs fine on every Apple Silicon Mac with the RAM. - You can push the GPU memory ceiling with
sudo sysctl iogpu.wired_limit_mb=<MB>(e.g. raise a 128GB box's GPU cap toward ~120GB). Helps the largest models on Ultra/Max.
Per-operator memory (sorted light → heavy)
| Operator | Lane | Weights | Peak RAM | Min Mac | Recommended | Notes |
|---|---|---|---|---|---|---|
fal_*, openrouter_image |
net | 0 | ~0 | 8 GB | 8 GB | cloud compute; RAM irrelevant |
ffprobe |
cpu | 0 | <1 GB | 8 GB | 8 GB | trivial |
ffmpeg_frames |
cpu | 0 | <1 GB | 8 GB | 8 GB | frame extraction |
blender_convert |
cpu | 0 | 1–4 GB (est.) | 8 GB | 16 GB | needs Blender installed |
colmap_poses |
cpu | 0 | 2–8 GB (scales w/ #images) | 8 GB | 16 GB | needs COLMAP+GLOMAP |
bg_remove_local (RMBG-2.0) |
gpu | 0.8 GB | ~3 GB (est.) | 8 GB | 16 GB | light; great first-step everywhere |
sf3d |
gpu | 3.7 GB | ~9 GB | 16 GB | 24 GB | ~5s draft mesh |
seedvr2_upscale |
gpu | 6.8 GB | ~8–12 GB (est.) | 16 GB | 24 GB | image upscale |
hunyuan3d_mlx INT4/INT8 (self-convert) |
gpu | 1.6–3 GB | ~4–10 GB | 16 GB | 16–24 GB | fits small Macs; see Hunyuan guide |
flux_local Klein 4B (default) |
gpu | 15 GB | 18.0 GB | 24 GB | 32 GB | 9s image, the volume workhorse |
flux_local schnell-4bit |
gpu | 8.9 GB | 19.1 GB | 24 GB | 32 GB | fast FLUX.1 draft |
hunyuan3d_mlx fp16 (our default, 4096/120k) |
gpu | 13 GB | 20.2 GB | 24 GB (tight) | 32 GB | Stage 1 alone ~10 GB; both stages ~20 GB |
trellis_mac |
gpu | 14 GB | ~15–18 GB | 24 GB | 32 GB | M3+ (bf16); SOTA local mesh |
flux_local FLUX.1 dev |
gpu | 31 GB | 25.1 GB | 32 GB | 48 GB | cinematic, ~2 min |
flux_local Klein 9B |
gpu | 32 GB | 28.4 GB | 36 GB | 48 GB | best object accuracy in the lineup |
mflux_image_edit (Qwen-Image-Edit) |
gpu | 54 GB | ~30–40 GB (est.) | 48 GB | 64 GB+ | the heavyweight local editor |
brush_train (3DGS) |
gpu | 0 | scales w/ scene | 16 GB | 32 GB | Gaussian-splat training |
What each Mac can do, by RAM
| RAM | Realistic role | Runs |
|---|---|---|
| 8 GB (M1/M2/M3 base) | cloud + light node | all net ops, ffmpeg/ffprobe, bg_remove_local, a 3–4B Ollama LLM. That's a genuinely useful fleet node. |
| 16 GB (base/Pro) | light local gen | above + sf3d, seedvr2_upscale, colmap, Hunyuan INT4/INT8, 7–8B Ollama. FLUX/fp16-Hunyuan/trellis won't fit. |
| 24 GB (M2/M3 base, M4 Pro) | entry real-gen | above + flux_local Klein 4B, hunyuan3d_mlx fp16 (tight), trellis_mac (M3/M4 only). The practical floor for the full local 3D + image stack. |
| 32 GB (Pro/Max) | comfortable all-round | the whole core stack with headroom: Klein 4B, hunyuan fp16 4096, trellis, FLUX dev. |
| 36–48 GB (Max) | hero-tier images | + FLUX.1 dev, FLUX.2 Klein 9B (the accuracy king). |
| 64 GB (Max/Ultra) | everything but the giant | + mflux_image_edit (Qwen 54 GB) comfortably; run two mid models resident. |
| 96–128 GB (Ultra) | multi-model resident | several big models loaded at once; our M1 Ultra 128 GB tier — runs it all (mind the bf16 caveat for trellis). |
| 192–256 GB+ (Ultra) | the flex | every operator + a big local LLM (70B–235B) resident alongside — our M3 Ultra 256 GB primary. |
Our fleet, mapped
| Node | RAM | Tier | What it serves |
|---|---|---|---|
| M3 Ultra | 256 GB | the flex | everything + primary/queue |
| M1 Ultra | 128 GB | multi-model | hunyuan3d_mlx (MLX-native), FLUX, sf3d, RMBG, cpu ops — not trellis (bf16 unverified) |
| M1 Max (studio) | 32 GB | second gen node | flux_local (Klein 4B), hunyuan3d_mlx (fp16), bg_remove_local, ffmpeg/ffprobe — not trellis (M1 bf16); ~2× M3 time. Shared audio box |
| M4 Pro | 24 GB | light helper | bg_remove_local, ffmpeg_frames/ffprobe, Ollama 7B — too small for the 18–28 GB gen models |
| M4 mini | 16 GB | light helper | bg_remove_local + cpu ops — too small for FLUX/fp16-hunyuan/trellis |
| M1 mini | 8 GB | cloud+light node | bg_remove_local, ffmpeg/ffprobe-class, net ops — the cheapest-useful tier |
Buying / sizing rules of thumb
- Cheapest useful node: any 8 GB Apple Silicon → cloud + ffmpeg + background-removal + small LLM.
- Cheapest real local-gen box: 24 GB (M4 Pro mini / M2-M3 24 GB) → Klein 4B images + local 3D.
- Sweet spot for one do-everything machine: 32–48 GB M-series Max → the entire core stack, hero images, both local 3D models, comfortably.
- Want Qwen image-edit or multiple big models resident: 64 GB+.
- M1 owners: prefer
hunyuan3d_mlxovertrellis_macfor local 3D (bf16). Everything MLX (FLUX via mflux, hunyuan) is first-class on M1.
Fleet policy + queue routing: CLUSTER.md. Measured benchmarks: BENCHMARKS.md. Hunyuan-specific RAM tiers: vendor/hunyuan3d-mlx/HARDWARE.md.