Qwen3.5 9B (Q4_K_S) — 23.4 GBon OVH l4-1-gpu
Overview
Qwen3.5 9B is a 9.65B parameter dense language model by Qwen, with code, multilingual, thinking, tool-calls, vision capabilities. It supports a context window of up to 262,144 tokens.
Qwen3.5 9B is the flagship small model in Alibaba's Qwen 3.5 family, built on the Gated Delta Networks hybrid architecture with 9.65 billion parameters, outperforming gpt-oss-120B on GPQA Diamond with 81.7 versus 80.1 at thirteen times fewer parameters. It is natively multimodal, processing text, images, and video, with built-in thinking capabilities for chain-of-thought reasoning. The model supports a 262K context window and covers over 201 languages. Released under the Apache 2.0 license, it runs in roughly 5 GB of VRAM at Q4, making it a top choice for self-hosted deployment on consumer hardware.
At Q4_K_S quantization (medium quality tier), the model weighs 5.02 GB. This fits within the 24 GB of VRAM on OVH l4-1-gpu, enabling full GPU inference.
The NVIDIA L4 is a datacenter inference GPU with 24 GB of GDDR6 VRAM and 300 GB/s memory bandwidth. It delivers 121 FP16 TFLOPS with Ada Lovelace architecture. Designed for efficient, low-power inference workloads in cloud and edge deployments. Handles quantized models up to 20B parameters.
Hardware Requirements
| Model size | 5.02 GB |
| VRAM available | 24 GB |
| VRAM used | 23.4 GB |
| System RAM | 80 GB |
| Min RAM required | 0 GB |
| GPU layers | 32 / 32 |
| Context size | 140,372 |
| Backend | cuda13 |
| Flash attention | Yes |
| Reading from disk | Yes |
Performance Notes
Deploy
Prerequisites
Ensure your GPU nodes are prepared with the NVIDIA container toolkit:
ansible-playbook prositronic.infra.nvidia_container_toolkitCommand
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen3-5-9b/q4_k_s/nvidia-l4.yaml) applyGenerated values.yaml
/values/qwen3-5-9b/q4_k_s/nvidia-l4.yaml
Loading values…Frequently Asked Questions
How much VRAM does Qwen3.5 9B (Q4_K_S) need?
The Q4_K_S quantization of Qwen3.5 9B requires 5.02 GB. All 32 layers fit in the 24 GB of VRAM available on OVH l4-1-gpu, enabling full GPU acceleration.
Can I run Qwen3.5 9B on OVH l4-1-gpu?
Yes. OVH l4-1-gpu provides 24 GB of VRAM, which is enough to run Qwen3.5 9B (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 Qwen3.5 9B from its original size down to 5.02 GB.
What quantization should I choose for Qwen3.5 9B?
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 Qwen3.5 9B on OVH l4-1-gpu, flash attention is enabled to maximise context length and throughput within the available 24 GB of VRAM.
How do I run Qwen3.5 9B (Q4_K_S) with Ollama?
Run ollama run qwen3.5:9b-q4_k_s to start Qwen3.5 9B (Q4_K_S). Ollama handles downloading the model weights automatically on first run.