Can Nemotron Mini 4B run on MacBook Pro M1 Pro 32GB?

Q: Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Nemotron Mini 4B?

Not always. MacBook Pro M1 Pro 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.

YES — Runs Great

C49Usable

Estimated from fit model

Nemotron Mini 4B needs ~8.7 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.7 GB, 56.0 tok/s, Runs well

8.7 GB required23.0 GB available

38% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

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 MacBook Pro M1 Pro 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	56.0 tok/s	1886 ms	4K
Coding	C	Runs well	53.3 tok/s	3634 ms	4K
Agentic Coding	C	Runs well	56.0 tok/s	5029 ms	4K
Reasoning	C	Runs well	56.0 tok/s	4086 ms	4K
RAG	C	Runs well	56.0 tok/s	6286 ms	4K

Quantization options

How Nemotron Mini 4B (4B params) fits at each quantization level on MacBook Pro M1 Pro 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 99

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run Nemotron Mini 4B?

Yes, MacBook Pro M1 Pro 32GB can run Nemotron Mini 4B with a C grade (Runs well). Expected decode speed: 53.3 tok/s.

How much VRAM does Nemotron Mini 4B need?

Nemotron Mini 4B (4B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Mini 4B?

The recommended quantization for Nemotron Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Mini 4B run at on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Nemotron Mini 4B achieves approximately 53.3 tokens per second decode speed with a time-to-first-token of 3634ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run Nemotron Mini 4B for coding?

For coding workloads, Nemotron Mini 4B on MacBook Pro M1 Pro 32GB receives a C grade with 53.3 tok/s and 4K context.

What context window can Nemotron Mini 4B use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 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.

Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Nemotron Mini 4B?

See all results for MacBook Pro M1 Pro 32GB See all hardware for Nemotron Mini 4B

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