Skip to content

text model · Qwen3 · Windows

Can I run Qwen3 8B on Nvidia GeForce RTX 3060 Ti (8GB)?

Compatibility verdict VRAM threshold engine
Yes, but tight GPU accelerated ~58 tok/s est.

Yes. Qwen3 8B runs on Nvidia GeForce RTX 3060 Ti (8GB) at Q4_K_M (~6.5 GB of ~7 GB usable).

Needs ~6.5 GB Device usable ~7 GB

Fits at Q4_K_M (~6.5 GB of ~7 GB usable) but with little headroom. Close other apps; a smaller context frees a few hundred MB.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 3060 Ti (8GB) leaves ~0.5 GB of headroom.

Q4_K_M needed
~6.5 GB
Usable on device
~7 GB
Device memory
8 GB
Best quant
Q4_K_M
Share on X Share on Reddit

Which quant fits

Quant ladder vs ~7 GB usable
Q2_K
~4.9 GB
Q3_K_M
~5.4 GB
Q4_K_M
~6.5 GB
Q5_K_M
~7.2 GB
Q6_K
~8.1 GB
Q8_0
~10.2 GB
FP16
~17.5 GB
The line marks Nvidia GeForce RTX 3060 Ti (8GB)'s ~7 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~200 W
Electricity / 1M tokens
~$0.14
Pays for itself after
~1,108M tok

At ~$0.15/kWh and the estimated ~58 tok/s, a million generated tokens costs about $0.14 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$399 Nvidia GeForce RTX 3060 Ti (8GB) pays for itself after roughly 1,108 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run qwen3:8b
llama.cpp
$ llama-cli -hf Qwen/Qwen3-8B-GGUF:Q4_K_M
LM Studio
$ lms get Qwen/Qwen3-8B-GGUF
Model Qwen3
Parameters
8B
Q4_K_M size
5.03 GB
Q8_0 size
8.71 GB
Context
32k
Ollama tag
qwen3:8b
Full Qwen3 8B requirements →
Device Windows
Memory
8 GB vram
Usable for weights
~7 GB
Power draw
~200 W
Best runtime
Ollama (CUDA) / llama.cpp CUDA
Best models for Nvidia GeForce RTX 3060 Ti (8GB) →

You could also run

Run Qwen3 8B on other hardware

FAQ

Can Nvidia GeForce RTX 3060 Ti (8GB) run Qwen3 8B?

Yes. Qwen3 8B runs on Nvidia GeForce RTX 3060 Ti (8GB) at Q4_K_M (~6.5 GB of ~7 GB usable).

How much memory does Qwen3 8B need?

It is a tight fit on Nvidia GeForce RTX 3060 Ti (8GB). At Q4_K_M the weights are ~5.03 GB; with KV cache and runtime overhead, budget ~6.5 GB at a 4k context.

What is the best tool to run Qwen3 8B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

Embed this

Qwen3 8B on Nvidia GeForce RTX 3060 Ti (8GB) compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![Qwen3 8B on Nvidia GeForce RTX 3060 Ti (8GB)](https://localmodel.run/badge/qwen3-8b/nvidia-rtx-3060-ti-8gb.svg)](https://localmodel.run/can-i-run/qwen3-8b/nvidia-rtx-3060-ti-8gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.