DeepSeek R1 Distill Qwen 7B (Q4_K_M) — 12.6 GBon OVH l40s-1-gpu
Overview
DeepSeek R1 Distill Qwen 7B is a 7.62B 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 7B is a 7.62-billion-parameter dense transformer from DeepSeek, distilled from the R1 reasoning model into a compact Qwen-based architecture. It brings chain-of-thought reasoning and thinking capabilities to the 7B parameter class, performing above its weight on math and logic tasks. Compared to standard 7B instruct models, it offers noticeably stronger structured reasoning. With a 128K context window and nine supported languages, it fits on a single consumer GPU and quantizes well for efficient self-hosted deployment.
At Q4_K_M quantization (medium quality tier), the model weighs 4.36 GB. This fits within the 48 GB of VRAM on OVH l40s-1-gpu, enabling full GPU inference.
The NVIDIA L40S is a datacenter GPU with 48 GB of GDDR6 VRAM and 864 GB/s memory bandwidth. It delivers 362 FP16 TFLOPS with Ada Lovelace architecture. A versatile GPU for AI inference, training, and graphics workloads. Handles quantized models up to 40B parameters comfortably.
Hardware Requirements
| Model size | 4.36 GB |
| VRAM available | 48 GB |
| VRAM used | 12.6 GB |
| System RAM | 80 GB |
| Min RAM required | 0 GB |
| GPU layers | 28 / 28 |
| Context size | 131,072 |
| 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/deepseek-r1-distill-qwen-7b/q4_k_m/nvidia-l40s.yaml) applyGenerated values.yaml
/values/deepseek-r1-distill-qwen-7b/q4_k_m/nvidia-l40s.yaml
Loading values…Frequently Asked Questions
How much VRAM does DeepSeek R1 Distill Qwen 7B (Q4_K_M) need?
The Q4_K_M quantization of DeepSeek R1 Distill Qwen 7B requires 4.36 GB. All 28 layers fit in the 48 GB of VRAM available on OVH l40s-1-gpu, enabling full GPU acceleration.
Can I run DeepSeek R1 Distill Qwen 7B on OVH l40s-1-gpu?
Yes. OVH l40s-1-gpu provides 48 GB of VRAM, which is enough to run DeepSeek R1 Distill Qwen 7B (Q4_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. Q4_K_M compresses DeepSeek R1 Distill Qwen 7B from its original size down to 4.36 GB.
What quantization should I choose for DeepSeek R1 Distill Qwen 7B?
Q4_K_M 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 DeepSeek R1 Distill Qwen 7B on OVH l40s-1-gpu, flash attention is enabled to maximise context length and throughput within the available 48 GB of VRAM.
How do I run DeepSeek R1 Distill Qwen 7B (Q4_K_M) with Ollama?
Run ollama run deepseek-r1:7b-qwen-distill-q4_k_m to start DeepSeek R1 Distill Qwen 7B (Q4_K_M). Ollama handles downloading the model weights automatically on first run.