Can Qwen3-VL 30B A3B Instruct run on RTX 3070 8GB?

Q: What should I upgrade first if Qwen3-VL 30B A3B Instruct feels slow on RTX 3070 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~23.0 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: LowStack: OptimizedBottleneck: Memory capacity

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) — 23.0 GB, exceeds 8.0 GB available

23.0 GB required8.0 GB available

288% VRAM needed

15.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.6 tok/s

TTFT

34628 ms

Safe context

Memory

23.0 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime2.4 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-VL 30B A3B Instruct on RTX 3070 8GB
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: 5.6 tok/s decode · 34.6s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 23.0 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.1 tok/s	20541 ms	4K
Coding	F	Too heavy	5.1 tok/s	37658 ms	4K
Agentic Coding	F	Too heavy	5.1 tok/s	54776 ms	4K
Reasoning	F	Too heavy	5.1 tok/s	44505 ms	4K
RAG	F	Too heavy	5.1 tok/s	68469 ms	4K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on RTX 3070 8GB (8.0 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

Upgrade options

Hardware that runs Qwen3-VL 30B A3B Instruct well

RTX 5090 32GBBudget pick

32 GB VRAM (+24)1792 GB/s (+1344)

Makes the model fit on the accelerator instead of staying completely out of reach.142.7 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

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+24)896 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.89.5 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.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+24)576 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.54.8 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.

~$4,000 MSRP

Frequently asked questions

Can RTX 3070 8GB run Qwen3-VL 30B A3B Instruct?

No, Qwen3-VL 30B A3B Instruct requires more memory than RTX 3070 8GB provides.

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

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.0 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 RTX 3070 8GB?

On RTX 3070 8GB, Qwen3-VL 30B A3B Instruct achieves approximately 5.1 tokens per second decode speed with a time-to-first-token of 37658ms using Q4_K_M quantization.

Can RTX 3070 8GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on RTX 3070 8GB receives a F grade with 5.1 tok/s and 4K context.

What context window can Qwen3-VL 30B A3B Instruct use on RTX 3070 8GB?

On RTX 3070 8GB, Qwen3-VL 30B A3B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-VL 30B A3B Instruct feels slow on RTX 3070 8GB?

See all results for RTX 3070 8GB See all hardware for Qwen3-VL 30B A3B Instruct

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