Would CUDA be a better path than Intel Arc A730M 12GB for Ministral 3 8B?

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 Ministral 3 8B run on Intel Arc A730M 12GB?

Q: What should I upgrade first if Ministral 3 8B feels slow on Intel Arc A730M 12GB?

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 — Tight Fit

A81Great

Estimated from fit model

Ministral 3 8B needs ~10.1 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: TransformersCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced

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

Q4_K_M (Medium quality) — 10.1 GB, 36.3 tok/s, Tight fit

10.1 GB required12.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

36.3 tok/s

TTFT

5338 ms

Safe context

30K

Memory

10.1 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on Intel Arc A730M 12GB
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: 36.3 tok/s decode · 5.3s TTFT (warm) · 91 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	36.3 tok/s	2912 ms	30K
Coding	A	Tight fit	33.7 tok/s	5738 ms	30K
Agentic Coding	A	Runs with offload (needs ~0.1 GB host RAM)	25.9 tok/s	10857 ms	30K
Reasoning	A	Tight fit	36.3 tok/s	6309 ms	30K
RAG	A	Runs with offload (needs ~0.1 GB host RAM)	25.9 tok/s	13572 ms

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A80
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
  --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Intel Arc A730M 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	32.2 tok/s
Qwen 3 14B	14B	A	11.3 tok/s

Frequently asked questions

Can Intel Arc A730M 12GB run Ministral 3 8B?

Yes, Intel Arc A730M 12GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 33.7 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 8B?

The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 8B run at on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Ministral 3 8B achieves approximately 33.7 tokens per second decode speed with a time-to-first-token of 5738ms using Q4_K_M quantization.

Can Intel Arc A730M 12GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on Intel Arc A730M 12GB receives a A grade with 33.7 tok/s and 30K context.

What context window can Ministral 3 8B use on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Ministral 3 8B can safely use up to 30K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Ministral 3 8B feels slow on Intel Arc A730M 12GB?

See all results for Intel Arc A730M 12GB See all hardware for Ministral 3 8B

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