MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 28%.48.2 tok/s decode
Raises estimated decode speed by about 28%.
~$1,999 MSRP
Nemotron Mini 4B needs ~8.7 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~38 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
Fit status
Runs well
Decode
37.8 tok/s
TTFT
5119 ms
Safe context
4K
Memory
8.7 GB / 23.0 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 37.8 tok/s | 2792 ms | 4K |
| Coding | C | Runs well | 37.8 tok/s | 5119 ms | 4K |
| Agentic Coding | C | Runs well | 37.8 tok/s | 7445 ms | 4K |
| Reasoning | C | Runs well | 37.8 tok/s | 6049 ms | 4K |
| RAG | C | Runs well | 37.8 tok/s | 9307 ms | 4K |
How Nemotron Mini 4B (4B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C45 |
Q3_K_S | 3 | 2.0 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run Nemotron Mini 4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
--hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 28%.
~$1,999 MSRP
Raises estimated decode speed by about 48%.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run Nemotron Mini 4B with a C grade (Runs well). Expected decode speed: 37.8 tok/s.
Nemotron Mini 4B (4B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Nemotron Mini 4B achieves approximately 37.8 tokens per second decode speed with a time-to-first-token of 5119ms using Q4_K_M quantization.
For coding workloads, Nemotron Mini 4B on MacBook Pro M4 32GB receives a C grade with 37.8 tok/s and 4K context.
On MacBook Pro M4 32GB, Nemotron Mini 4B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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-mini-4b-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 2.4 GB | Medium | C45 |
Q5_K_M | 5 | 2.9 GB | High | C45 |
Q6_K | 6 | 3.3 GB | High | C46 |
Q8_0 | 8 | 4.3 GB | Very High | C46 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C49 |
Not always. MacBook Pro M4 32GB 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.