Will It Run AI

Can Snowflake Arctic Embed L run on MacBook Pro M1 Max 32GB?

YES — Runs Great

A72Great
Estimated from fit model

Snowflake Arctic Embed L needs ~6.3 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 6.8 GB, 4.7 tok/s, Runs well
6.8 GB required23.0 GB available
30% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

6.8 GB / 23.0 GB

Memory breakdown

Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsSnowflake Arctic Embed L on MacBook Pro M1 Max 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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 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
ChatARuns well4.7 tok/s22516 ms512
CodingARuns well4.7 tok/s41279 ms512
Agentic CodingARuns well4.7 tok/s60043 ms512
ReasoningARuns well4.7 tok/s48785 ms512
RAGARuns well4.7 tok/s75053 ms512

Quantization options

How Snowflake Arctic Embed L (0.33500000834465027B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowA77
Q3_K_S
3
0.2 GB
LowA77
NVFP4
4
0.2 GB
MediumA77
Q4_K_M
4
0.2 GB
MediumA77
Q5_K_M
5
0.2 GB
HighA77
Q6_K
6
0.3 GB
HighA77
Q8_0
8
0.4 GB
Very HighA77
F16Best for your GPU
16
0.7 GB
MaximumA77

Get started

Copy-paste commands to run Snowflake Arctic Embed L on your machine.

Run

ollama run snowflake-arctic-embed

Your hardware

More models your MacBook Pro M1 Max 32GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BA29.4 tok/s
AlibabaQwen 3.5 27B27BA13.1 tok/s
AlibabaQwen 3.6 27B27BS14.5 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BA30.9 tok/s
AlibabaQwen 3.5 9B9BS43.1 tok/s

Frequently asked questions

Can MacBook Pro M1 Max 32GB run Snowflake Arctic Embed L?

Yes, MacBook Pro M1 Max 32GB can run Snowflake Arctic Embed L with a A grade (Runs well). Expected decode speed: 4.7 tok/s.

How much VRAM does Snowflake Arctic Embed L need?

Snowflake Arctic Embed L (0.33500000834465027B parameters) requires approximately 6.3 GB of memory with F16 quantization.

What is the best quantization for Snowflake Arctic Embed L?

The recommended quantization for Snowflake Arctic Embed L is F16, which balances quality and memory efficiency.

What speed will Snowflake Arctic Embed L run at on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Snowflake Arctic Embed L achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.

Can MacBook Pro M1 Max 32GB run Snowflake Arctic Embed L for coding?

For coding workloads, Snowflake Arctic Embed L on MacBook Pro M1 Max 32GB receives a A grade with 4.7 tok/s and 512 context.

What context window can Snowflake Arctic Embed L use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Snowflake Arctic Embed L can safely use up to 512 tokens of context. The model's official context limit is 512, but available memory constrains the safe maximum.

What should I upgrade first if Snowflake Arctic Embed L feels slow on MacBook Pro M1 Max 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 M1 Max 32GB as fast as VRAM for Snowflake Arctic Embed L?

Not always. MacBook Pro M1 Max 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 M1 Max 32GBSee all hardware for Snowflake Arctic Embed L
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