Nemotron 3 Nano 30B needs ~20.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q3_K_S quantization, expect ~21 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
6.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.2 tok/s
TTFT
12767 ms
Safe context
4K
Memory
24.2 GB / 17.3 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 16.1 tok/s | 6555 ms | 4K |
| Coding | F | Too heavy | 15.2 tok/s | 12767 ms | 4K |
| Agentic Coding | F | Too heavy | 13.6 tok/s | 20723 ms | 4K |
| Reasoning | F | Too heavy | 15.2 tok/s | 15089 ms | 4K |
| RAG | F | Too heavy | 6.5 tok/s | 54135 ms | 4K |
How Nemotron 3 Nano 30B (30B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 11.7 GB | Low | S91 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
Copy-paste commands to run Nemotron 3 Nano 30B on your machine.
Run
ollama run nemotron-nano:30bUpgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
NVIDIA A100 40GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$10,000 MSRP
Yes, MacBook Pro M4 Pro 24GB can run Nemotron 3 Nano 30B at Q3_K_S quantization (Very compromised (needs ~2.4 GB host RAM)). The recommended Q4_K_M requires 24.2 GB which exceeds available memory, but at Q3_K_S it needs only 20.6 GB. Expected decode speed: 21.3 tok/s.
Nemotron 3 Nano 30B (30B parameters) requires approximately 24.2 GB at Q4_K_M quantization. On MacBook Pro M4 Pro 24GB, it fits at Q3_K_S using 20.6 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M4 Pro 24GB the best fitting quantization is Q3_K_S, which uses 20.6 GB.
On MacBook Pro M4 Pro 24GB, Nemotron 3 Nano 30B achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9091ms using Q3_K_S quantization.
For coding workloads, Nemotron 3 Nano 30B on MacBook Pro M4 Pro 24GB receives a F grade with 15.2 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, Nemotron 3 Nano 30B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-3-nano-30b-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
16.8 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M4 Pro 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.