Can Jina Embeddings v3 run on MacBook Air M2 16GB?

Q: Is unified memory on MacBook Air M2 16GB as fast as VRAM for Jina Embeddings v3?

Not always. MacBook Air M2 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.

YES — Runs Great

A81Great

Estimated from fit model

Jina Embeddings v3 needs ~5.2 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With F16 quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: 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

F16 (Maximum quality) — 6.1 GB, 8.0 tok/s, Runs well

6.1 GB required11.5 GB available

53% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24176 ms

Safe context

Memory

6.1 GB / 11.5 GB

Memory breakdown

Weights1.2 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsJina Embeddings v3 on MacBook Air M2 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: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 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	A	Runs well	8.0 tok/s	13187 ms	8K
Coding	A	Runs well	8.0 tok/s	24176 ms	8K
Agentic Coding	S	Runs well	8.0 tok/s	35165 ms	8K
Reasoning	A	Runs well	8.0 tok/s	28571 ms	8K
RAG	S	Runs well	8.0 tok/s	43956 ms	8K

Quantization options

How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	A83
Q3_K_S	3	0.3 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run Jina Embeddings v3 on your machine.

Run

ollama run jina/jina-embeddings-v3

Your hardware

More models your MacBook Air M2 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	12.7 tok/s
Qwen 3.5 4B	4B	S	28.6 tok/s

Frequently asked questions

Can MacBook Air M2 16GB run Jina Embeddings v3?

Yes, MacBook Air M2 16GB can run Jina Embeddings v3 with a A grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does Jina Embeddings v3 need?

Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 5.2 GB of memory with F16 quantization.

What is the best quantization for Jina Embeddings v3?

The recommended quantization for Jina Embeddings v3 is F16, which balances quality and memory efficiency.

What speed will Jina Embeddings v3 run at on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Jina Embeddings v3 achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24176ms using F16 quantization.

Can MacBook Air M2 16GB run Jina Embeddings v3 for coding?

For coding workloads, Jina Embeddings v3 on MacBook Air M2 16GB receives a A grade with 8.0 tok/s and 8K context.

What context window can Jina Embeddings v3 use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Jina Embeddings v3 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for Jina Embeddings v3?

See all results for MacBook Air M2 16GB See all hardware for Jina Embeddings v3

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