Jina Embeddings v3 needs ~4.0 GB VRAM. Intel Arc Pro A40 6GB has 6.0 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
4.0 GB / 6.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 8.0 tok/s | 13187 ms | 8K |
| Coding | S | Runs well | 8.0 tok/s | 24176 ms | 8K |
| Agentic Coding | A | Runs with offload | 8.0 tok/s | 35165 ms | 8K |
| Reasoning | A | Runs well | 8.0 tok/s | 28571 ms | 8K |
| RAG | A | Runs with offload | 8.0 tok/s | 43956 ms | 8K |
How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | S87 |
Q3_K_S | 3 | 0.3 GB | Low | S88 |
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 4B | 4B | S | 41.4 tok/s | |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 43.6 tok/s |
Yes, Intel Arc Pro A40 6GB can run Jina Embeddings v3 with a S grade (Runs well). Expected decode speed: 8.0 tok/s.
Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 4.0 GB of memory with F16 quantization.
The recommended quantization for Jina Embeddings v3 is F16, which balances quality and memory efficiency.
On Intel Arc Pro A40 6GB, 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 Intel Arc Pro A40 6GB receives a S grade with 8.0 tok/s and 8K context.
On Intel Arc Pro A40 6GB, 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-arc-pro-a40-6gb" 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 |
| S88 |
Q4_K_M | 4 | 0.3 GB | Medium | S88 |
Q5_K_M | 5 | 0.4 GB | High | S88 |
Q6_K | 6 | 0.5 GB | High | S88 |
Q8_0 | 8 | 0.6 GB | Very High | S88 |
F16Best for your GPU | 16 | 1.2 GB | Maximum | S90 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.