Can Qwen 3 30B A3B run on RTX 6000 Ada Laptop 16GB?

YES — With Q2_K

S93Excellent

Estimated from fit model

Qwen 3 30B A3B needs ~16.2 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

Qwen 3 30B A3B at Q4_K_M needs 22.9 GB — too much for RTX 6000 Ada Laptop 16GB (16.0 GB). Runs at Q2_K (16.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.9 GB, exceeds 16.0 GB available

22.9 GB required16.0 GB available

143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

22.5 tok/s

TTFT

8612 ms

Safe context

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 30B A3B on RTX 6000 Ada Laptop 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: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement 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	24.1 tok/s	4386 ms	4K
Coding	F	Too heavy	22.5 tok/s	8612 ms	4K
Agentic Coding	F	Too heavy	19.7 tok/s	14275 ms	4K
Reasoning	F	Too heavy	22.5 tok/s	10177 ms	4K
RAG	F	Too heavy	19.7 tok/s	17844 ms	4K

Inference speed

Qwen 3 30B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3 30B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~182 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	181.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	115.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.5	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	104.5	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	99.1	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	91.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	67.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	67.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	41.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	32.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	11.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	7.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.5	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 Qwen 3 30B A3B (30.5B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	F0
Q3_K_S	3	14.9 GB	Low	F0
NVFP4	4	17.1 GB	Medium	F0
Q4_K_M	4	18.6 GB	Medium	F0
Q5_K_M	5	22.0 GB	High	F0
Q6_K	6	25.0 GB	High	F0
Q8_0	8	32.6 GB	Very High	F0
F16	16	62.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3 30B A3B on your machine.

Run

ollama run qwen3:30b-a3b

Upgrade-Optionen

Hardware, die Qwen 3 30B A3B gut ausführt

RTX 4000 Ada 20GBBestes Preis-Leistungs-Verhältnis

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 tok/s Dekodierung

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

Adds memory headroom for longer context windows and future model growth.

ca. $1,250 MSRP

RTX 3090 24GBGünstige Wahl

24 GB VRAM (+8)936 GB/s (+360)

Makes the model fit on the accelerator instead of staying completely out of reach.99.1 tok/s Dekodierung

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.

ca. $1,499 MSRP

RTX 4090 24GBNVIDIA-Upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.115.8 tok/s Dekodierung

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.

ca. $1,599 MSRP

Frequently asked questions

Can RTX 6000 Ada Laptop 16GB run Qwen 3 30B A3B?

Yes, RTX 6000 Ada Laptop 16GB can run Qwen 3 30B A3B at Q2_K quantization (Runs with offload (needs ~0.1 GB host RAM)). The recommended Q4_K_M requires 22.9 GB which exceeds available memory, but at Q2_K it needs only 16.2 GB. Expected decode speed: 62.1 tok/s.

How much VRAM does Qwen 3 30B A3B need?

Qwen 3 30B A3B (30.5B parameters) requires approximately 22.9 GB at Q4_K_M quantization. On RTX 6000 Ada Laptop 16GB, it fits at Q2_K using 16.2 GB.

What is the best quantization for Qwen 3 30B A3B?

The recommended quantization is Q4_K_M, but on RTX 6000 Ada Laptop 16GB the best fitting quantization is Q2_K, which uses 16.2 GB.

What speed will Qwen 3 30B A3B run at on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 3 30B A3B achieves approximately 62.1 tokens per second decode speed with a time-to-first-token of 3118ms using Q2_K quantization.

Can RTX 6000 Ada Laptop 16GB run Qwen 3 30B A3B for coding?

For coding workloads, Qwen 3 30B A3B on RTX 6000 Ada Laptop 16GB receives a F grade with 22.5 tok/s and 4K context.

What context window can Qwen 3 30B A3B use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 3 30B A3B can safely use up to 14K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 30B A3B feels slow on RTX 6000 Ada Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 6000 Ada Laptop 16GB See all hardware for Qwen 3 30B A3B

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