Can I run GLM-4.7-Flash on a M6 16GB Mac?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-09-14.
Memory breakdown (8-bit, F16 KV, 33K context)
| Model weights | 29.1 GB |
| KV cache | 1.7 GB |
| Runtime + macOS | 13.7 GB |
| Total used | 44.4 / 16 GB |
| Short by | 28.4 GB |
Max context at 8-bit: does not fit. Unified memory is shared by the OS — FitLLM leaves ~20% headroom.
Every quantization on M6 16GB
| Quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| 4bit | 14.5 GB | ❌ won't fit | 28.2 / 16 GB |
| 8bit | 29.1 GB | ❌ won't fit | 44.4 / 16 GB |
| 16bit | 58.1 GB | ❌ won't fit | 77.0 / 16 GB |
Lower quants free memory at some output-quality cost — 4-bit is the common sweet spot for local use.
▶ Open the interactive calculator (this exact setup)Embed this verdict
Live badge for your README or model card — recomputed by the engine, never stale:
[](https://fitllm.run/can-i-run/glm-4-7-flash-on-m6-16gb)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm-engine "GLM-4.7-Flash" --mac 16Why most calculators get this wrong
GLM-4.7-Flash uses MLA (Multi-head Latent Attention): K/V are compressed into a single low-rank latent (512 + 64 RoPE dims) shared across all heads — cached once, not per-head K and V. Naive "2 × heads × head_dim × layers" formulas over-count its KV cache by an order of magnitude.
Other options
same Mac Models that fit in 16GB: MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, MiniCPM5-2B.
Reproduce it
Open math: fitllm-engine (MIT), from official config.json.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home