Qwen3 8B (Q4_K_S) — 9.4 GBon NVIDIA RTX 3080

Q4_K_S NVIDIA RTX 3080

Overview

Qwen3 8B is a 8B parameter dense language model by Qwen, with code, multilingual, thinking, tool-calls capabilities. It supports a context window of up to 40,960 tokens.

Qwen3 8B is an 8-billion-parameter dense transformer from Alibaba's Qwen team, featuring built-in thinking capabilities alongside code generation, tool calling, and multilingual support. It advances beyond Qwen2.5 with improved reasoning, supporting chain-of-thought inference in a compact form factor. The model covers 14 languages including English, Chinese, and Arabic. With a 40K context window and flash attention, it fits on a single consumer GPU and quantizes efficiently for cost-effective self-hosted reasoning workloads.

At Q4_K_S quantization (medium quality tier), the model weighs 4.47 GB. This fits within the 10 GB of VRAM on NVIDIA RTX 3080, enabling full GPU inference.

Hardware Requirements

Model size	4.47 GB
VRAM available	10 GB
VRAM used	9.4 GB
GPU layers	36 / 36
Context size	26,835
Backend	cuda13
Flash attention	Yes

Performance Notes

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen3-8b/q4_k_s/nvidia-rtx3080.yaml) apply

Generated values.yaml

/values/qwen3-8b/q4_k_s/nvidia-rtx3080.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o qwen3-8b.gguf "https://huggingface.co/unsloth/Qwen3-8B-GGUF/resolve/main/Qwen3-8B-Q4_K_S.gguf"

Start Server

llama-server \
  -m qwen3-8b.gguf \
  --n-gpu-layers 36 \
  --ctx-size 26835 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Qwen3 8B (Q8_0)
• Qwen3 8B (Q5_K_M)
• Qwen3 8B (Q4_K_M)

Other hardware

• Qwen3 8B on CPU Only
• Qwen3 8B on NVIDIA A100 80GB
• Qwen3 8B on NVIDIA H100

Frequently Asked Questions

How much VRAM does Qwen3 8B (Q4_K_S) need?

The Q4_K_S quantization of Qwen3 8B requires 4.47 GB. All 36 layers fit in the 10 GB of VRAM available on NVIDIA RTX 3080, enabling full GPU acceleration.

Can I run Qwen3 8B on NVIDIA RTX 3080?

Yes. NVIDIA RTX 3080 provides 10 GB of VRAM, which is enough to run Qwen3 8B (Q4_K_S) with all layers on the GPU for optimal performance.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_K_S compresses Qwen3 8B from its original size down to 4.47 GB.

What quantization should I choose for Qwen3 8B?

Q4_K_S is a medium-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

What is flash attention and why is it enabled?

Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Qwen3 8B on NVIDIA RTX 3080, flash attention is enabled to maximise context length and throughput within the available 10 GB of VRAM.

How do I run Qwen3 8B (Q4_K_S) with Ollama?

Run ollama run qwen3:8b-q4_k_s to start Qwen3 8B (Q4_K_S). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026