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text model · Nemotron · macOS

Can I run Nemotron 3.5 Super VL 120B-A12B on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Nemotron 3.5 Super VL 120B-A12B needs ~87.4 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

Needs ~87.4 GB Device usable ~10.5 GB

Needs ~87.4 GB even at Q4_K_M, but only ~10.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 76.9 GB: Nemotron 3.5 Super VL 120B-A12B needs roughly 87.4 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Nemotron 3.5 Super VL 120B-A12B is the Apple M4 Max (128GB) at 128 GB. See Nemotron 3.5 Super VL 120B-A12B on Apple M4 Max (128GB).

Q4_K_M needed
~87.4 GB
Usable on device
~10.5 GB
Device memory
16 GB
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Which quant fits

Quant ladder vs ~10.5 GB usable
Q2_K
~53.7 GB
Q3_K_M
~62.1 GB
Q4_K_M
~87.4 GB
Q5_K_M
~88.9 GB
Q6_K
~101.8 GB
Q8_0
~130.9 GB
FP16
~243.4 GB
The line marks Apple M2 (16GB)'s ~10.5 GB budget; rungs past it are too large.
Model Nemotron
Parameters
120B (MoE, 12B active)
Q4_K_M size
83.96 GB
Context
1024k
Ollama tag
nemotron-3.5-super:120b-a12b-q4_K_M
Full Nemotron 3.5 Super VL 120B-A12B requirements →
Device macOS
Memory
16 GB unified
Usable for weights
~10.5 GB
Power draw
~50 W
Best runtime
Ollama (llama.cpp Metal backend) / MLX
Best models for Apple M2 (16GB) →

What you can run instead

Run Nemotron 3.5 Super VL 120B-A12B on other hardware

Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)Nvidia DGX Spark (128GB)Apple M3 Ultra (256GB)

FAQ

Can Apple M2 (16GB) run Nemotron 3.5 Super VL 120B-A12B?

No. Nemotron 3.5 Super VL 120B-A12B needs ~87.4 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

How much memory does Nemotron 3.5 Super VL 120B-A12B need?

Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~83.96 GB; with KV cache and runtime overhead, budget ~87.4 GB at a 4k context. It is a Mixture-of-Experts model (120B total / 12B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Nemotron 3.5 Super VL 120B-A12B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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$ [![Nemotron 3.5 Super VL 120B-A12B on Apple M2 (16GB)](https://localmodel.run/badge/nemotron-3.5-super-120b-a12b/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/nemotron-3.5-super-120b-a12b/apple-m2-16gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.