Will It Run AI

Can Nomic Embed Text v1.5 run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

B68Good
Estimated from fit model

Nomic Embed Text v1.5 needs ~5.5 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 5.5 GB, 2.0 tok/s, Runs well
5.5 GB required23.0 GB available
24% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

8K

Memory

5.5 GB / 23.0 GB

Memory breakdown

Weights0.3 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsNomic Embed Text v1.5 on MacBook Pro M2 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well2.0 tok/s52800 ms8K
CodingBRuns well2.0 tok/s96800 ms8K
Agentic CodingBRuns well2.0 tok/s140800 ms8K
ReasoningBRuns well2.0 tok/s114400 ms8K
RAGBRuns well2.0 tok/s176000 ms8K

Quantization options

How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowA76
Q3_K_S
3
0.1 GB
LowA76
NVFP4
4
0.1 GB
MediumA76
Q4_K_M
4
0.1 GB
MediumA76
Q5_K_M
5
0.1 GB
HighA76
Q6_K
6
0.1 GB
HighA76
Q8_0
8
0.1 GB
Very HighA76
F16Best for your GPU
16
0.3 GB
MaximumA76

Get started

Copy-paste commands to run Nomic Embed Text v1.5 on your machine.

Run

ollama run nomic-embed-text

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Nomic Embed Text v1.5?

Yes, MacBook Pro M2 Pro 32GB can run Nomic Embed Text v1.5 with a B grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does Nomic Embed Text v1.5 need?

Nomic Embed Text v1.5 (0.13699999451637268B parameters) requires approximately 5.5 GB of memory with F16 quantization.

What is the best quantization for Nomic Embed Text v1.5?

The recommended quantization for Nomic Embed Text v1.5 is F16, which balances quality and memory efficiency.

What speed will Nomic Embed Text v1.5 run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Nomic Embed Text v1.5 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.

Can MacBook Pro M2 Pro 32GB run Nomic Embed Text v1.5 for coding?

For coding workloads, Nomic Embed Text v1.5 on MacBook Pro M2 Pro 32GB receives a B grade with 2.0 tok/s and 8K context.

What context window can Nomic Embed Text v1.5 use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Nomic Embed Text v1.5 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Nomic Embed Text v1.5 feels slow on MacBook Pro M2 Pro 32GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Nomic Embed Text v1.5?

Not always. MacBook Pro M2 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.

See all results for MacBook Pro M2 Pro 32GBSee all hardware for Nomic Embed Text v1.5
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nomic-embed-text-v1.5-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: