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AI Compute Radar

Hardware guide

Best local models for
Mac mini M6 16GB

◇ Estimated— Estimated: computed from our curated model and hardware catalog — not a live reading.

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Specifications

Memory
16 GB
Memory type
Unified
Bandwidth
153 GB/s
Class
Apple unified 16 GB

The short answer

Gemma 4 12B

Weights ~6.6 GB (Q4_K_M), context ~0.4 GB at 8K tokens, runtime ~1.0 GB. Your 16 GB leaves ~10.4 GB usable — macOS lets the GPU wire only about two thirds of unified memory on Macs of this size; we plan with a conservative 65%.

GGUF (Q4_K_M) ↗ (External link)Original (safetensors) ↗ (External link)Ollama ↗ (External link)

Fit labels

  • Runs easily
  • Runs
  • Barely fits
  • Spills into RAM
  • Too big

Every curated model on this card

Context length: 8K tokens · f16 (default)

Model fit on the selected hardware
ModelFitEst. memoryEstimated memory = weights + context + runtime
Ornith 1.5 9BOrnith AI · 9.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~6.7 GB5.4 + 0.3 + 1.0
Granite 4.2 8BIBM · 8.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~7.3 GB5.0 + 1.3 + 1.0
LFM2.5 8B-A1BLiquid AI · 8.5B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~5.9 GB4.8 + 0.1 + 1.0
Qwen3 8BAlibaba Qwen · 8.2B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~6.8 GB4.7 + 1.1 + 1.0
Gemma 4 E4BGoogle · 8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~5.8 GB4.6 + 0.3 + 1.0
Ling 3.0 TinyInclusionAI · 7.9B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Runs easily~5.7 GB4.5 + 0.2 + 1.0
Qwen3 4BAlibaba Qwen · 4B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~4.3 GB2.3 + 1.1 + 0.9
Phi-4 MiniMicrosoft · 3.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~4.2 GB2.3 + 1.0 + 0.9
Granite 4.2 3BIBM · 3.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~3.6 GB2.1 + 0.6 + 0.9
LFM2.5 2.6BLiquid AI · 2.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~2.6 GB1.6 + 0.1 + 0.9
LFM3 1.7BLiquid AI · 1.7B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Runs easily~2.0 GB1.0 + 0.1 + 0.9
LFM2.5 1.2BLiquid AI · 1.2B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~1.7 GB0.7 + 0.1 + 0.9
Qwen3 0.6BAlibaba Qwen · 0.6B · Q8_0GGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs easily~2.3 GB0.6 + 0.9 + 0.9
Gemma 4 12BGoogle · 12B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Runs~8.1 GB6.6 + 0.4 + 1.0
Nemotron Nano 9B v2NVIDIA · 8.9B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Runs~8.9 GB6.1 + 1.8 + 1.0
Qwen3.8 2.4T-A95BAlibaba Qwen · 2.4T-A95B · IQ4_XSGGUF ↗ (External link)Original ↗ (External link)Too big~1259.0 GB1220.8 + 0.7 + 37.5
DeepSeek-V4-ProDeepSeek · 1650B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Too big~816.4 GB791.3 + 0.5 + 24.6
GLM-5.3Zhipu AI · 753B · Q4_KGGUF ↗ (External link)Original ↗ (External link)Too big~449.8 GB435.2 + 0.7 + 13.9
GLM-5.2Zhipu AI · 753B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~448.3 GB433.8 + 0.7 + 13.9
DeepSeek-R1DeepSeek · 685B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~389.4 GB376.7 + 0.5 + 12.2
Ornith 1.5 397BOrnith AI · 403B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~235.4 GB227.5 + 0.2 + 7.7
GLM-5.3-FlashZhipu AI · 321B · Q4_KGGUF ↗ (External link)Original ↗ (External link)Too big~192.5 GB186.0 + 0.1 + 6.4
Hy3Tencent · 299B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~174.6 GB166.3 + 2.5 + 5.8
DeepSeek-V4-FlashDeepSeek · 304B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Too big~150.0 GB144.4 + 0.4 + 5.2
Qwen3.8 Flash-NextAlibaba Qwen · 180B MoE · Q4_KGGUF ↗ (External link)Original ↗ (External link)Too big~107.8 GB103.7 + 0.2 + 4.0
Ornith 1.5 122BOrnith AI · 122B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~73.8 GB70.6 + 0.2 + 3.0
Ling 3.0 FlashInclusionAI · 127B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~73.4 GB70.1 + 0.4 + 3.0
GLM-4.5-AirZhipu AI · 110B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~72.3 GB68.0 + 1.4 + 2.9
gpt-oss 120BOpenAI · 117B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~61.4 GB58.5 + 0.3 + 2.6
Qwen3-Coder NextAlibaba Qwen · 79.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~48.1 GB45.1 + 0.8 + 2.2
Llama 3.3 70BMeta · 70.6B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~44.1 GB39.6 + 2.5 + 2.0
Qwen AgentWorld 35B-A3BAlibaba Qwen · 34.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~22.2 GB20.6 + 0.2 + 1.5
Ornith 1.5 35B-A3BOrnith AI · 36B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~21.8 GB20.2 + 0.2 + 1.5
KAT-Coder V2.5Kwaipilot · 34.7B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~21.5 GB19.9 + 0.2 + 1.4
Qwen3 32BAlibaba Qwen · 32.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~21.8 GB18.4 + 2.0 + 1.4
Qwen3-Coder 30B-A3BAlibaba Qwen · 30.5B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~19.4 GB17.3 + 0.8 + 1.4
Gemma 4 31BGoogle · 31.3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~19.9 GB17.1 + 1.4 + 1.4
GLM-4.7-FlashZhipu AI · 31.2B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Too big~18.9 GB17.1 + 0.4 + 1.4
Granite 4.2 30BIBM · 29.3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~19.8 GB16.5 + 2.0 + 1.3
Gemma 4 26B-A4BGoogle · 25.8B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~17.5 GB15.8 + 0.4 + 1.3
Gemma 3 27BGoogle · 27.4B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~17.7 GB15.4 + 1.0 + 1.3
Qwen3.8 27BAlibaba Qwen · 27.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~17.1 GB15.3 + 0.5 + 1.3
LFM2.5 24B-A2BLiquid AI · 24B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~14.9 GB13.5 + 0.2 + 1.3
Mistral Small 3.2Mistral AI · 24B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~15.8 GB13.3 + 1.3 + 1.2
Devstral Small 2 24BMistral AI · 24B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~15.8 GB13.3 + 1.3 + 1.2
gpt-oss 20BOpenAI · 20.9B MoE · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~12.2 GB10.8 + 0.2 + 1.2
Qwen3 14BAlibaba Qwen · 14.8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Too big~10.8 GB8.4 + 1.3 + 1.1
Llama 3.1 8BMeta · 8B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Unknown—
Llama 3.2 3BMeta · 3B · Q4_K_MGGUF ↗ (External link)Original ↗ (External link)Ollama ↗ (External link)Unknown—

All memory figures are estimates: measured quantized file size + computed context memory + runtime overhead, with a 12% safety margin on your hardware. Real usage varies with runtime version and settings.

GGUF is the quantized single-file format local runtimes load (Ollama, LM Studio, llama.cpp); our memory figures are measured from the linked GGUF file. The original repo holds full-precision safetensors — a much larger download meant for GPUs with far more memory.

Same memory, different speed

These cards hold the same models — the difference is how fast they read them. Memory bandwidth is the ceiling for token generation, so it decides tokens per second, not which models fit.

Same memory, different speed
CardBandwidthRelative speed
Mac mini M6 16GB(this card)153 GB/s1.00×
RTX 4080716.8 GB/s4.68×
RTX 5080960 GB/s6.27×
RTX 4060 Ti 16GB288 GB/s1.88×
RTX 5060 Ti 16GB448 GB/s2.93×
RTX 5070 Ti896 GB/s5.86×
RTX 4070 Ti Super672 GB/s4.39×
RX 9070 XT640 GB/s4.18×

Relative speed compares memory bandwidth only. Real throughput also depends on the runtime, quantization and how much of the model sits in VRAM.

When this card is not enough

Own the middle.

DGX Spark and GB10-class systems put 128 GB of unified memory on your desk. Rational when large local models are your daily routine.

Read our DGX Spark profile →

Path C — Elastic compute

Rent the spike.

Occasional heavy job? Rent an H100 for the afternoon instead of buying hardware that idles the rest of the year.

Browse Vast.ai offers →

Keep reading