Yes — with 175.0 GB to spare
Gemma 3 27B at Q4_K_M fits your M3 Ultra · 256 GB entirely in unified memory at 8K context, at an estimated 29 tokens per second. There is room for its full 128K window.
Fully in unified memory
8K context
Q4_K_M · 15.4 GB
Gemma Terms of Use
Released Mar 2025
Vision
The 2025 single-GPU generalist with vision. Its Gemma-licence terms are the reason to prefer Gemma 4 now.
The VRAM budget
weights 15.4 GB
Weights 15.4 GB
KV cache @ 8K 1.03 GB
Runtime overhead 0.6 GB
Free 175.0 GB of 192.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 27.1 GB | 28.7 GB | 128K | 16 | −0.1% ppl | Long context |
| Q6_K | 20.9 GB | 22.6 GB | 128K | 21 | −0.4% ppl | Long context |
| Q5_K_M | 18.1 GB | 19.7 GB | 128K | 25 | −0.8% ppl | Long context |
| Q4_K_M | 15.4 GB | 17.0 GB | 128K | 29 | −1.9% ppl | Recommended |
| Q3_K_M | 12.5 GB | 14.1 GB | 128K | 36 | −5.4% ppl | Long context |
| Q2_K | 10.7 GB | 12.3 GB | 128K | 42 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (1024 tokens, 1 global in 6), which is why its cache barely grows with context.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/gemma-3-27b-it-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.
01Download is 15.4 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02macOS caps what the GPU may wire down at about 192.0 GB of 256 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.
03There is room to go to the model's full 128K context on this card.