Best quality at ≥ 5 t/s
Qwen3.8-27B
33.7 AA index Xhigh
MLX 8-bitMLX-LMUnified memory
~7.8–8.0 t/s Estimated
Apple · Apple Silicon Mac
GPUs
Unified memory
System RAM
≈ 89.6 GB/s theoretical ceiling (MT/s × 8 bytes × channels). DDR5’s two 32-bit subchannels count as one channel.
CPU
Used for CPU layers/experts and the -t flag. A reference platform fills its maximum channels and speed; set what you actually populated.
Defaults are editorial planning choices, not measurements: 1 GiB runtime + 0.5 GiB margin per device, 75% of unified memory, 8 GiB RAM for the OS, a vendor “24 GB” treated as 24 GiB (enter your driver’s available VRAM to override), 5 t/s minimum speed (research editorial default). Memory ledger
Apple M4 Pro (20-core GPU), 48 GB · 75% (36.0 GiB) planned for models · 273 GB/s8K contextF16 KVany backend≥ 5 t/s
Best quality at ≥ 5 t/s
33.7 AA index Xhigh
MLX 8-bitMLX-LMUnified memory
~7.8–8.0 t/s Estimated
Faster tradeoff
25.3 AA index single mode
Q5_K_Mllama.cpp MetalUnified memory
~14–40 t/s Estimated extrapolated
Next point on the quality/speed front; then Qwen3.6-35B-A3B.
Possibly better · speed not established
No higher-scoring model has a feasible plan here.
Best plan per model that reaches your floor by a matching measurement, a calibrated range, or an extrapolated estimate (labelled). Sorted by the selected quality metric, then speed.
Qwen/Qwen3.8-27B
IFM/K2-Horizon-MoVA-36B-A4B
IFM/K2-Horizon-7B
Qwen/Qwen3.6-35B-A3B
meta-models/Muse-Glimmer-30B
IFM/K2-Horizon-3.7B
LGAI-EXAONE/EXAONE-4.5-33B
Qwen/Qwen3.5-4B
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
ibm-granite/granite-4.2-30b
openbmb/MiniCPM5-2B
nvidia/Nemotron-Cascade-2-30B-A3B
Qwen/Qwen3.5-9B
ibm-granite/granite-4.2-8b
ai9stars/G9v3-3B
ibm-granite/granite-4.2-3b
trillionlabs/Tri-21B-Think
LiquidAI/LFM2.5-2.6B
LiquidAI/LFM2.5-8B-A1B
allenai/Olmo-3.1-32B-Think
Qwen/Qwen3.5-2B
allenai/Olmo-3.1-32B-Instruct
Qwen/Qwen3.5-0.8B
ibm-granite/granite-4.0-h-small
microsoft/phi-4
allenai/Olmo-3-7B-Think
allenai/Olmo-3-7B-Instruct
LiquidAI/LFM2.5-1.2B-Thinking
LiquidAI/LFM2.5-1.2B-Instruct
allenai/Molmo2-8B
LiquidAI/LFM2.5-VL-1.6B
swiss-ai/Apertus-8B-Instruct-2509
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
IFM/K2-Horizon-0.9B
The weights fit, but the KV cache for this attention type (or the chosen cache layout) is not established, so a fit cannot be guaranteed.
google/gemma-4-26B-A4B-it
google/gemma-4-31B-it
google/gemma-4-12B-it
inclusionAI/Ling-3.0-tiny
google/diffusiongemma-26B-A4B-it
google/gemma-4-E4B-it
google/gemma-4-E2B-it
microsoft/Phi-4-mini-instruct
CohereLabs/tiny-aya-global
No measurement, calibration or bandwidth figure for this plan.
openai/gpt-oss-20b
ibm-granite/granite-4.0-h-1b
ibm-granite/granite-4.0-micro
ibm-granite/granite-4.0-350m
ibm-granite/granite-4.0-h-350m
| Memory | 48 GB unified | source |
|---|---|---|
| Bandwidth | 273 GB/s | source |
| FP16 | unknown | Apple publishes no comparable FP16 throughput or GPU-only TDP in reviewed chip/device technical specifications. Power adapter rating and whole-system maximum continuous power are not GPU TDP. |
| Power | unknown | Apple publishes no comparable FP16 throughput or GPU-only TDP in reviewed chip/device technical specifications. Power adapter rating and whole-system maximum continuous power are not GPU TDP. |
Found in: Mac mini (2024)
15 observations
Resolved to this device (same chip and GPU-core count). Reported by the linked sources; not reproduced by LocalFit.
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