Will It Run AI

Can Nomic Embed Text v1.5 run on MacBook Pro M1 Pro 16GB?

YES — Runs Great

B70Good
Estimated from fit model

Nomic Embed Text v1.5 needs ~3.6 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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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) — 3.8 GB, 2.0 tok/s, Runs well
3.8 GB required11.5 GB available
33% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

8K

Memory

3.8 GB / 11.5 GB

Memory breakdown

Weights0.3 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom1.7 GB

See how fast it feels

See how fast it feelsNomic Embed Text v1.5 on MacBook Pro M1 Pro 16GB
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 CodingARuns well2.0 tok/s140800 ms8K
ReasoningBRuns well2.0 tok/s114400 ms8K
RAGARuns well2.0 tok/s176000 ms8K

Quantization options

How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowA79
Q3_K_S
3
0.1 GB
LowA79
NVFP4
4
0.1 GB
MediumA79
Q4_K_M
4
0.1 GB
MediumA79
Q5_K_M
5
0.1 GB
HighA79
Q6_K
6
0.1 GB
HighA79
Q8_0
8
0.1 GB
Very HighA79
F16Best for your GPU
16
0.3 GB
MaximumA79

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 M1 Pro 16GB run Nomic Embed Text v1.5?

Yes, MacBook Pro M1 Pro 16GB 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 3.6 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 M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, 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 M1 Pro 16GB run Nomic Embed Text v1.5 for coding?

For coding workloads, Nomic Embed Text v1.5 on MacBook Pro M1 Pro 16GB 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 M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, 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 M1 Pro 16GB?

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 M1 Pro 16GB as fast as VRAM for Nomic Embed Text v1.5?

Not always. MacBook Pro M1 Pro 16GB 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 M1 Pro 16GBSee all hardware for Nomic Embed Text v1.5
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