Qwen3.5 4B (Q5_K_XL) — 24.3 GBon OVH h100-1-gpu
Overview
Qwen3.5 4B is a 4.66B 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 4B is a model from Alibaba's Qwen 3.5 family built on the Gated Delta Networks hybrid architecture with 4.66 billion parameters, widely regarded as the community sweet spot for performance per watt. 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, nearly matching previous-generation 80B MoE models on coding benchmarks. Released under the Apache 2.0 license, it runs in roughly 3 GB of VRAM at Q4, delivering fast and stable self-hosted deployment on consumer hardware.
At Q5_K_XL quantization (medium quality tier), the model weighs 3.03 GB. This fits within the 80 GB of VRAM on OVH h100-1-gpu, enabling full GPU inference.
The NVIDIA H100 80 GB is a datacenter GPU with 80 GB of HBM3 VRAM and 3350 GB/s memory bandwidth. It delivers 1979 FP16 TFLOPS on the Hopper architecture, making it the fastest single-GPU option for large language model inference. It handles models up to 70B parameters with high throughput. Built for datacenter teams running demanding production AI workloads.
Hardware Requirements
| Model size | 3.03 GB |
| VRAM available | 80 GB |
| VRAM used | 24.3 GB |
| System RAM | 350 GB |
| Min RAM required | 0 GB |
| GPU layers | 32 / 32 |
| Context size | 262,144 |
| 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_toolkitCommand
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen3-5-4b/q5_k_xl/nvidia-h100-80gb.yaml) applyGenerated values.yaml
/values/qwen3-5-4b/q5_k_xl/nvidia-h100-80gb.yaml
Loading values…Frequently Asked Questions
How much VRAM does Qwen3.5 4B (Q5_K_XL) need?
The Q5_K_XL quantization of Qwen3.5 4B requires 3.03 GB. All 32 layers fit in the 80 GB of VRAM available on OVH h100-1-gpu, enabling full GPU acceleration.
Can I run Qwen3.5 4B on OVH h100-1-gpu?
Yes. OVH h100-1-gpu provides 80 GB of VRAM, which is enough to run Qwen3.5 4B (Q5_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. Q5_K_XL compresses Qwen3.5 4B from its original size down to 3.03 GB.
What quantization should I choose for Qwen3.5 4B?
Q5_K_XL 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 4B on OVH h100-1-gpu, flash attention is enabled to maximise context length and throughput within the available 80 GB of VRAM.
How do I run Qwen3.5 4B (Q5_K_XL) with Ollama?
Run ollama run qwen3.5:4b-q5_k_xl to start Qwen3.5 4B (Q5_K_XL). Ollama handles downloading the model weights automatically on first run.