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
Can Nemotron Mini 4B run on Mac mini M4 32GB?
YES — Runs Great
C48Usable
▲Estimated — low-sample bucket· few comparable runs
Nemotron Mini 4B needs ~8.7 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.7 GB, 37.8 tok/s, Runs well
8.7 GB required23.0 GB available
38% VRAM used
Fit status
Runs well
Decode
37.8 tok/s
TTFT
5119 ms
Safe context
4K
Memory
8.7 GB / 23.0 GB
Memory breakdown
Weights2.4 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsNemotron Mini 4B on Mac mini M4 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 37.8 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 35.4 tok/s | 2982 ms | 4K |
| Coding | C | Runs well | 35.4 tok/s | 5467 ms | 4K |
| Agentic Coding | C | Runs well | 35.4 tok/s | 7953 ms | 4K |
| Reasoning | C | Runs well | 35.4 tok/s | 6461 ms | 4K |
| RAG | C | Runs well | 35.4 tok/s | 9941 ms | 4K |
Quantization options
How Nemotron Mini 4B (4B params) fits at each quantization level on Mac mini 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 |
Get started
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