text model · Nemotron · macOS
Can I run Llama-3.3-Nemotron-Super-49B-v1 on Apple M4 Max (64GB)?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on Apple M4 Max (64GB) at Q4_K_M (~30.6 GB of ~48 GB usable).
Runs at Q4_K_M using ~30.6 GB of ~48 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Max (64GB) leaves ~17.4 GB of headroom.
- Q4_K_M needed
- ~30.6 GB
- Usable on device
- ~48 GB
- Device memory
- 64 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~145 W
- Electricity / 1M tokens
- ~$0.38
- Pays for itself after
- ~29,158M tok
At ~$0.15/kWh and the estimated ~16 tok/s, a million generated tokens costs about $0.38 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,499 Apple M4 Max (64GB) pays for itself after roughly 29,158 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF:Q4_K_M lms get bartowski/nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 49B
- Q4_K_M size
- 28.14 GB
- Q8_0 size
- 49.36 GB
- Context
- 128k
- Memory
- 64 GB unified
- Usable for weights
- ~48 GB
- Power draw
- ~145 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Llama-3.3-Nemotron-Super-49B-v1 on other hardware
FAQ
Can Apple M4 Max (64GB) run Llama-3.3-Nemotron-Super-49B-v1?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on Apple M4 Max (64GB) at Q4_K_M (~30.6 GB of ~48 GB usable).
How much memory does Llama-3.3-Nemotron-Super-49B-v1 need?
Apple M4 Max (64GB) has room to spare. At Q4_K_M the weights are ~28.14 GB; with KV cache and runtime overhead, budget ~30.6 GB at a 4k context.
What is the best tool to run Llama-3.3-Nemotron-Super-49B-v1 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/nemotron-super-49b/apple-m4-max-64gb) 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.