Qwen3 8B (Q8_K_XL) — 17 GBon NVIDIA H100 320GB

Code Multilingual Thinking Tool Calls

Q8_K_XL NVIDIA H100 320GB

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 Q8_K_XL quantization (high quality tier), the model weighs 10.08 GB. This fits within the 320 GB of VRAM on NVIDIA H100 320GB, enabling full GPU inference.

Hardware Requirements

Model size	10.08 GB
VRAM available	320 GB
VRAM used	17 GB
System RAM
Min RAM required	0 GB
GPU layers	36 / 36
Context size	40,960
Backend	cuda13
Flash attention	Yes
Reading from disk	Yes

Deploy

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/q8_k_xl/nvidia-h100-320gb.yaml) apply

Generated `values.yaml`

/values/qwen3-8b/q8_k_xl/nvidia-h100-320gb.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-UD-Q8_K_XL.gguf"

Start Server

llama-server \
  -m qwen3-8b.gguf \
  --n-gpu-layers 36 \
  --ctx-size 40960 \
  --flash-attn

Verify

curl http://localhost:8080/health

Frequently Asked Questions

How much VRAM does Qwen3 8B (Q8_K_XL) need?

The Q8_K_XL quantization of Qwen3 8B requires 10.08 GB. All 36 layers fit in the 320 GB of VRAM available on NVIDIA H100 320GB, enabling full GPU acceleration.

Can I run Qwen3 8B on NVIDIA H100 320GB?

Yes. NVIDIA H100 320GB provides 320 GB of VRAM, which is enough to run Qwen3 8B (Q8_K_XL) 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. Q8_K_XL compresses Qwen3 8B from its original size down to 10.08 GB.

What quantization should I choose for Qwen3 8B?

Q8_K_XL is a high-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 H100 320GB, flash attention is enabled to maximise context length and throughput within the available 320 GB of VRAM.

How do I run Qwen3 8B (Q8_K_XL) with Ollama?

Run ollama run qwen3:8b-q8_k_xl to start Qwen3 8B (Q8_K_XL). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026