Jina Embeddings v3 needs ~3.8 GB VRAM. Intel Arc A370M 4GB has 4.0 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 with offload
Decode
8.0 tok/s
TTFT
24176 ms
Safe context
8K
Memory
3.8 GB / 4.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 8.0 tok/s | 13187 ms | 8K |
| Coding | A | Runs with offload | 8.0 tok/s | 24176 ms | 8K |
| Agentic Coding | F | Too heavy | 8.0 tok/s | 35165 ms | 8K |
| Reasoning | A | Runs with offload | 8.0 tok/s | 28571 ms | 8K |
| RAG | F | Too heavy | 8.0 tok/s | 43956 ms | 8K |
How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | S91 |
Q3_K_S | 3 | 0.3 GB | Low | S92 |
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 |
|---|---|---|---|---|
| 3.8B | A | 15 tok/s |
Yes, Intel Arc A370M 4GB can run Jina Embeddings v3 with a A grade (Runs with offload). Expected decode speed: 8.0 tok/s.
Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 3.8 GB of memory with F16 quantization.
The recommended quantization for Jina Embeddings v3 is F16, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, 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 A370M 4GB receives a A grade with 8.0 tok/s and 8K context.
On Intel Arc A370M 4GB, 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-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| S92 |
Q4_K_M | 4 | 0.3 GB | Medium | S92 |
Q5_K_M | 5 | 0.4 GB | High | S92 |
Q6_K | 6 | 0.5 GB | High | S92 |
Q8_0 | 8 | 0.6 GB | Very High | S93 |
F16Best for your GPU | 16 | 1.2 GB | Maximum | S92 |
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.