DeepSeek R1 Distill Qwen 32B (Q3_K_M) — 23.4 GBon OVH l4-1-gpu
Overview
DeepSeek R1 Distill Qwen 32B is a 32.76B parameter dense language model by DeepSeek, with code, multilingual, thinking, tool-calls capabilities. It supports a context window of up to 131,072 tokens.
DeepSeek R1 Distill Qwen 32B is a 32.76-billion-parameter dense transformer from DeepSeek, distilled from the larger R1 reasoning model into a Qwen-based architecture. It excels at chain-of-thought reasoning, code generation, and multilingual tasks with built-in thinking capabilities. Compared to standard 30B-class instruct models, it provides stronger logical and mathematical reasoning. The model supports nine languages and a 128K context window, making it suitable for developers and researchers who need reasoning-focused inference on mid-range GPU setups.
At Q3_K_M quantization (low quality tier), the model weighs 14.84 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 | 14.84 GB |
| VRAM available | 24 GB |
| VRAM used | 23.4 GB |
| System RAM | 80 GB |
| Min RAM required | 0 GB |
| GPU layers | 64 / 64 |
| Context size | 47,941 |
| 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/deepseek-r1-distill-qwen-32b/q3_k_m/nvidia-l4.yaml) applyGenerated values.yaml
/values/deepseek-r1-distill-qwen-32b/q3_k_m/nvidia-l4.yaml
Loading values…Frequently Asked Questions
How much VRAM does DeepSeek R1 Distill Qwen 32B (Q3_K_M) need?
The Q3_K_M quantization of DeepSeek R1 Distill Qwen 32B requires 14.84 GB. All 64 layers fit in the 24 GB of VRAM available on OVH l4-1-gpu, enabling full GPU acceleration.
Can I run DeepSeek R1 Distill Qwen 32B on OVH l4-1-gpu?
Yes. OVH l4-1-gpu provides 24 GB of VRAM, which is enough to run DeepSeek R1 Distill Qwen 32B (Q3_K_M) 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. Q3_K_M compresses DeepSeek R1 Distill Qwen 32B from its original size down to 14.84 GB.
What quantization should I choose for DeepSeek R1 Distill Qwen 32B?
Q3_K_M is a low-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 DeepSeek R1 Distill Qwen 32B 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 DeepSeek R1 Distill Qwen 32B (Q3_K_M) with Ollama?
Run ollama run deepseek-r1:32b-qwen-distill-q3_k_m to start DeepSeek R1 Distill Qwen 32B (Q3_K_M). Ollama handles downloading the model weights automatically on first run.