Can I run GLM-4.7-Flash on a M6 32GB Mac?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-10-19.
Conditions: 8-bit · KV cache F16 · 32K context · macOS reserve (8.5 GB).
Memory breakdown (8-bit, F16 KV, 32K context)
| Model weights | 29.1 GB |
| KV cache | 1.7 GB |
| Runtime + macOS | 13.2 GB |
| Total used | 43.9 / 32 GB |
| Short by | 11.9 GB |
Context at 8-bit: does not fit at any context. Unified memory is shared with macOS and your apps — the recommended max leaves 20% of it free.
Every quantization on M6 32GB
| Quant | Weights | Recommended max ctx | Hard limit | Used @32K |
|---|---|---|---|---|
| 4bit | 14.5 GB | ~9K | ~82K | ⚠️ 27.7 / 32 GB |
| 8bit | 29.1 GB | won't fit | ❌ 43.9 / 32 GB | |
| 16bit | 58.1 GB | won't fit | ❌ 76.5 / 32 GB | |
Recommended max = the longest context that still keeps the 20% headroom macOS needs · hard limit = the longest that fits at all (KV cache F16).
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 at exactly these conditions, never stale:
[](https://fitllm.run/can-i-run/glm-4-7-flash-on-m6-32gb)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "GLM-4.7-Flash" --mac 32 --quant 8 --ctx 32768 --kv 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 32GB: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, Spark-X2.5-4B, 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