Nemotron Mini 4B needs ~7.9 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~30 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
30.0 tok/s
TTFT
6462 ms
Safe context
4K
Memory
7.9 GB / 17.3 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 | 30.0 tok/s | 3525 ms | 4K |
| Coding | C | Runs well | 30.0 tok/s | 6462 ms | 4K |
| Agentic Coding | C | Runs well | 30.0 tok/s | 9400 ms | 4K |
| Reasoning | C | Runs well | 30.0 tok/s | 7637 ms | 4K |
| RAG | C | Runs well | 30.0 tok/s | 11749 ms | 4K |
How Nemotron Mini 4B (4B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C46 |
Q3_K_S | 3 | 2.0 GB | Low | C47 |
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 87%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Air M3 24GB can run Nemotron Mini 4B with a C grade (Runs well). Expected decode speed: 30.0 tok/s.
Nemotron Mini 4B (4B parameters) requires approximately 7.9 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 Air M3 24GB, Nemotron Mini 4B achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6462ms using Q4_K_M quantization.
For coding workloads, Nemotron Mini 4B on MacBook Air M3 24GB receives a C grade with 30.0 tok/s and 4K context.
On MacBook Air M3 24GB, 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-m3-air-24gb" 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 |
| C47 |
Q4_K_M | 4 | 2.4 GB | Medium | C47 |
Q5_K_M | 5 | 2.9 GB | High | C47 |
Q6_K | 6 | 3.3 GB | High | C47 |
Q8_0 | 8 | 4.3 GB | Very High | C48 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C52 |
Not always. MacBook Air M3 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.