Would CUDA be a better path than Intel Arc A370M 4GB for Jina Embeddings v3?

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.

Can Jina Embeddings v3 run on Intel Arc A370M 4GB?

Q: What should I upgrade first if Jina Embeddings v3 feels slow on Intel Arc A370M 4GB?

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.

YES — With Offload

A83Great

Estimated from fit model

Jina Embeddings v3 needs ~3.8 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With F16 quantization, expect ~8 tok/s.

Runtime: Sentence TransformersCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: 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) — 3.8 GB, 8.0 tok/s, Runs with offload

3.8 GB required4.0 GB available

95% VRAM used

Fit status

Runs with offload

Decode

8.0 tok/s

TTFT

24176 ms

Safe context

Memory

3.8 GB / 4.0 GB

Memory breakdown

Weights1.2 GB

KV Cache2.0 GB

Runtime0.3 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsJina Embeddings v3 on Intel Arc A370M 4GB
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

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.

Best improvement path

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.

Performance by workload

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

Quantization options

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

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 Intel Arc A370M 4GB can run

Model	Params	Grade	Decode	Capabilities
Phi-4 Mini Reasoning 4B	3.8B	A	15 tok/s

Frequently asked questions

Can Intel Arc A370M 4GB run Jina Embeddings v3?

Yes, Intel Arc A370M 4GB can run Jina Embeddings v3 with a A grade (Runs with offload). Expected decode speed: 8.0 tok/s.

How much VRAM does Jina Embeddings v3 need?

Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 3.8 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 Intel Arc A370M 4GB?

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.

Can Intel Arc A370M 4GB run Jina Embeddings v3 for coding?

For coding workloads, Jina Embeddings v3 on Intel Arc A370M 4GB receives a A grade with 8.0 tok/s and 8K context.

What context window can Jina Embeddings v3 use on Intel Arc A370M 4GB?

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.

What should I upgrade first if Jina Embeddings v3 feels slow on Intel Arc A370M 4GB?

See all results for Intel Arc A370M 4GB See all hardware for Jina Embeddings v3

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