Would CUDA be a better path than Intel Arc A370M 4GB for BGE M3?

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 BGE M3 run on Intel Arc A370M 4GB?

YES — Runs Great

S85Excellent

Estimated from fit model

BGE M3 needs ~3.0 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With F16 quantization, expect ~8 tok/s.

Runtime: Sentence TransformersCapacity: RoomyBandwidth: 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) — 3.0 GB, 8.0 tok/s, Runs well

3.0 GB required4.0 GB available

75% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24346 ms

Safe context

Memory

3.0 GB / 4.0 GB

Memory breakdown

Weights1.2 GB

KV Cache1.1 GB

Runtime0.3 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsBGE M3 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 · 24.3s 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.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	8.0 tok/s	13280 ms	8K
Coding	S	Runs well	8.0 tok/s	24346 ms	8K
Agentic Coding	A	Runs with offload (needs ~0 GB host RAM)	8.0 tok/s	35412 ms	8K
Reasoning	S	Runs well	8.0 tok/s	28773 ms	8K
RAG	S	Runs well	8.0 tok/s	44266 ms	8K

Quantization options

How BGE M3 (0.5680000185966492B 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	S90
Q3_K_S	3	0.3 GB	Low	S91
NVFP4	4

Get started

Copy-paste commands to run BGE M3 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "BAAI/bge-m3" \
  --hf-file "bge-m3-F16.gguf" \
  -c 4096 -ngl 99

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
Jina Embeddings v3	0.57B	A	8 tok/s

Frequently asked questions

Can Intel Arc A370M 4GB run BGE M3?

Yes, Intel Arc A370M 4GB can run BGE M3 with a S grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does BGE M3 need?

BGE M3 (0.5680000185966492B parameters) requires approximately 3.0 GB of memory with F16 quantization.

What is the best quantization for BGE M3?

The recommended quantization for BGE M3 is F16, which balances quality and memory efficiency.

What speed will BGE M3 run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, BGE M3 achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24346ms using F16 quantization.

Can Intel Arc A370M 4GB run BGE M3 for coding?

For coding workloads, BGE M3 on Intel Arc A370M 4GB receives a S grade with 8.0 tok/s and 8K context.

What context window can BGE M3 use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, BGE M3 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 BGE M3 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.

See all results for Intel Arc A370M 4GB See all hardware for BGE M3

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