Would CUDA be a better path than Intel Arc A770 16GB for Qwen3-VL 30B A3B Instruct?

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 Qwen3-VL 30B A3B Instruct run on Intel Arc A770 16GB?

Q: What should I upgrade first if Qwen3-VL 30B A3B Instruct feels slow on Intel Arc A770 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

A76Great

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~19.2 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q3_K_S quantization, expect ~23 tok/s.

Runtime: LM StudioCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Qwen3-VL 30B A3B Instruct at Q4_K_M needs 22.8 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at Q3_K_S (19.2 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.8 GB, exceeds 16.0 GB available

22.8 GB required16.0 GB available

143% VRAM needed

6.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.1 tok/s

TTFT

13760 ms

Safe context

Memory

22.8 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime1.4 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-VL 30B A3B Instruct on Intel Arc A770 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 14.1 tok/s decode · 13.8s TTFT (warm) · 35 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	F	Too heavy	15.1 tok/s	7006 ms	4K
Coding	F	Too heavy	14.1 tok/s	13760 ms	4K
Agentic Coding	F	Too heavy	12.3 tok/s	22822 ms	4K
Reasoning	F	Too heavy	14.1 tok/s	16262 ms	4K
RAG	F	Too heavy	12.3 tok/s	28528 ms	4K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.

Run

lms load Qwen3-VL-30B-A3B-Instruct && lms server start

Upgrade options

Hardware that runs Qwen3-VL 30B A3B Instruct well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.38.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+16)1792 GB/s (+1232)

Makes the model fit on the accelerator instead of staying completely out of reach.187.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.315.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.405 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run Qwen3-VL 30B A3B Instruct?

Yes, Intel Arc A770 16GB can run Qwen3-VL 30B A3B Instruct at Q3_K_S quantization (Very compromised (needs ~2.4 GB host RAM)). The recommended Q4_K_M requires 22.8 GB which exceeds available memory, but at Q3_K_S it needs only 19.2 GB. Expected decode speed: 23.4 tok/s.

How much VRAM does Qwen3-VL 30B A3B Instruct need?

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 22.8 GB at Q4_K_M quantization. On Intel Arc A770 16GB, it fits at Q3_K_S using 19.2 GB.

What is the best quantization for Qwen3-VL 30B A3B Instruct?

The recommended quantization is Q4_K_M, but on Intel Arc A770 16GB the best fitting quantization is Q3_K_S, which uses 19.2 GB.

What speed will Qwen3-VL 30B A3B Instruct run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Qwen3-VL 30B A3B Instruct achieves approximately 23.4 tokens per second decode speed with a time-to-first-token of 8272ms using Q3_K_S quantization.

Can Intel Arc A770 16GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on Intel Arc A770 16GB receives a F grade with 14.1 tok/s and 4K context.

What context window can Qwen3-VL 30B A3B Instruct use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Qwen3-VL 30B A3B Instruct can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for Qwen3-VL 30B A3B Instruct

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