Jina Embeddings v3 needs ~6.1 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With F16 quantization, expect ~8 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Runs well
Decode
8.0 tok/s
TTFT
24176 ms
Safe context
8K
Memory
6.1 GB / 11.5 GB
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.
| 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 |
How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on MacBook Pro M2 Pro 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 |
Copy-paste commands to run Jina Embeddings v3 on your machine.
Run
ollama run jina/jina-embeddings-v3Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 27.4 tok/s | |
| Qwen 3.5 4B | 4B | S | 56 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run Jina Embeddings v3 with a A grade (Runs well). Expected decode speed: 8.0 tok/s.
Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 6.1 GB of memory with F16 quantization.
The recommended quantization for Jina Embeddings v3 is F16, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Jina Embeddings v3 achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24176ms using F16 quantization.
For coding workloads, Jina Embeddings v3 on MacBook Pro M2 Pro 16GB receives a A grade with 8.0 tok/s and 8K context.
On MacBook Pro M2 Pro 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/jina-embeddings-v3-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.3 GB |
| Medium |
| A83 |
Q4_K_M | 4 | 0.3 GB | Medium | A83 |
Q5_K_M | 5 | 0.4 GB | High | A84 |
Q6_K | 6 | 0.5 GB | High | A84 |
Q8_0 | 8 | 0.6 GB | Very High | A84 |
F16Best for your GPU | 16 | 1.2 GB | Maximum | A84 |
| Qwen 3 8B | 8B | S | 30.8 tok/s |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 53.2 tok/s |
Not always. MacBook Pro M2 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.