Can Qwen 3 8B run on RTX 3080 10GB?

YES — Tight Fit

S94Excellent

Estimated from fit model

Qwen 3 8B needs ~9.3 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.3 GB, 112.0 tok/s, Tight fit

9.3 GB required10.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

21K

Memory

9.3 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsQwen 3 8B on RTX 3080 10GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	S	Runs well	112.0 tok/s	943 ms	21K
Coding	S	Tight fit	112.0 tok/s	1729 ms	21K
Agentic Coding	A	Very compromised (needs ~0.6 GB host RAM)	71.4 tok/s	3942 ms	21K
Reasoning	S	Tight fit	112.0 tok/s	2043 ms	21K
RAG	A	Very compromised (needs ~0.6 GB host RAM)	71.4 tok/s	4927 ms	21K

Quantization options

How Qwen 3 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S91
Q3_K_S	3	3.9 GB	Low	S93
NVFP4	4	4.5 GB	Medium	S93
Q4_K_M	4	4.9 GB	Medium	S93
Q5_K_M	5	5.8 GB	High	S92
Q6_KBest for your GPU	6	6.6 GB	High	S92
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run qwen3:8b

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	113.1 tok/s

Frequently asked questions

Can RTX 3080 10GB run Qwen 3 8B?

Yes, RTX 3080 10GB can run Qwen 3 8B with a S grade (Tight fit). Expected decode speed: 112.0 tok/s.

How much VRAM does Qwen 3 8B need?

Qwen 3 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 8B?

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

What speed will Qwen 3 8B run at on RTX 3080 10GB?

On RTX 3080 10GB, Qwen 3 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 10GB run Qwen 3 8B for coding?

For coding workloads, Qwen 3 8B on RTX 3080 10GB receives a S grade with 112.0 tok/s and 21K context.

What context window can Qwen 3 8B use on RTX 3080 10GB?

On RTX 3080 10GB, Qwen 3 8B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 8B feels slow on RTX 3080 10GB?

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 3080 10GB See all hardware for Qwen 3 8B

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<iframe src="https://willitrunai.com/embed/qwen-3-8b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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