The figures below are third-party estimates, not measurements taken on maccloud's own hardware. They come from ModelFit's published bandwidth-and-model-size modeling for a 256GB M5 Ultra Mac Studio, whose own methodology note states plainly: "Speeds are ModelFit estimates from chip bandwidth and model size, not measured benchmarks." We're citing them because they're a reasonable, transparent starting point — not presenting them as in-house benchmark results. As we run our own measured benchmarks on maccloud hardware, we'll publish those separately and label them accordingly.
Estimated throughput by model
All figures below assume the 256GB configuration and the quantization level shown. Memory footprint includes ModelFit's KV-cache assumptions (FP16 cache, the llama.cpp/Ollama default) on top of model weights.
| Model | Params | Quantization | Memory footprint | Est. tokens/sec | Est. first token |
|---|---|---|---|---|---|
| Qwen3-Next 80B-A3B | 80B | Q4_K_M | ~50.4 GB | ~64 tok/s | ~1.4s |
| GPT-OSS 120B | 117B | MXFP4 | ~65.4 GB | ~43 tok/s | ~1.5s |
| Qwen3-Next 80B-A3B | 80B | Q8_0 | ~84.8 GB | ~35 tok/s | ~1.5s |
| Qwen3.8-Flash-Next | 125B | IQ1_S | ~123 GB | ~32 tok/s | ~1.6s |
| Laguna S 2.1 | 118B | Q4_K_M | ~96 GB | ~32 tok/s | ~1.6s |
| Qwen3.5 122B-A10B Instruct | 122B | Q4_K_M | ~72 GB | ~28 tok/s | ~1.6s |
| Llama 4 Scout | 109B | Q4_K_M | ~67 GB | ~23 tok/s | ~1.7s |
| Qwen3 235B A22B | 235B | Q4_K_M | ~130 GB | ~14 tok/s | ~2.0s |
Where Llama 70B fits in
Llama 3.3 70B isn't in ModelFit's top-8 list for the 256GB config (it's tuned toward the larger MoE models that make better use of 256GB of headroom), but its own published GGUF sizes give a good proxy: at Q4_K_M (42.5GB) it sits in roughly the same memory class as GPT-OSS 120B above, and the same bandwidth-based reasoning suggests throughput in a broadly comparable range — likely faster than the 235B-class models on this list and slower than smaller 70–80B MoE models, given Llama 70B is a dense (non-MoE) architecture. See our Llama 70B sizing guide for the full quantization breakdown.
Why this matters for configuration choice
Every model on this list fits inside the 256GB plan with memory to spare. The 512GB plan exists for the cases that don't fit here: running the largest MoE models (like Qwen3 235B) at higher quantization for better quality, running two of these models concurrently, or leaving headroom for a long-context workload that pushes the KV cache well past what these estimates assume.