Can Qwen3-VL 30B A3B Instruct run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~35.4 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~73 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: HighStack: 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) — 35.4 GB, 72.6 tok/s, Runs well

35.4 GB required92.2 GB available

38% VRAM used

Fit status

Runs well

Decode

72.6 tok/s

TTFT

2668 ms

Safe context

256K

Memory

35.4 GB / 92.2 GB

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime1.8 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsQwen3-VL 30B A3B Instruct on Mac Studio M2 Ultra 128GB
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: 72.6 tok/s decode · 2.7s TTFT (warm) · 181 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	72.6 tok/s	1455 ms	256K
Coding	S	Runs well	72.6 tok/s	2668 ms	256K
Agentic Coding	S	Runs well	72.6 tok/s	3881 ms	256K
Reasoning	S	Runs well	72.6 tok/s	3153 ms	256K
RAG	S	Runs well	72.6 tok/s	4851 ms	256K

Inference speed

Qwen3-VL 30B A3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-VL 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~143 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	142.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	119.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	108.1	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	102.5	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	87.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	72.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	68.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	37.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	34.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	32.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	23.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	8.3	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A82
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A83
Q5_K_M	5	21.6 GB	High	A83
Q6_K	6	24.6 GB	High	A84
Q8_0	8	32.1 GB	Very High	S85
F16Best for your GPU	16	61.5 GB	Maximum	S90

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

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	5.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 122B A10B	122B	S	16.9 tok/s
Qwen 3.6 35B A3B	35B	S	59 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Qwen3-VL 30B A3B Instruct?

Yes, Mac Studio M2 Ultra 128GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 72.6 tok/s.

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

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 35.4 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-VL 30B A3B Instruct run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen3-VL 30B A3B Instruct achieves approximately 72.6 tokens per second decode speed with a time-to-first-token of 2668ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on Mac Studio M2 Ultra 128GB receives a S grade with 72.6 tok/s and 256K context.

What context window can Qwen3-VL 30B A3B Instruct use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen3-VL 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Qwen3-VL 30B A3B Instruct?

Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M2 Ultra 128GB See all hardware for Qwen3-VL 30B A3B Instruct

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