DeepSeek R1 Distill Qwen 32B (Q8_0) — 47.4 GBon NVIDIA L40S
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 Q8_0 quantization (high quality tier), the model weighs 32.43 GB. This fits within the 48 GB of VRAM on NVIDIA L40S, 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 | 32.43 GB |
| VRAM available | 48 GB |
| VRAM used | 47.4 GB |
| System RAM | |
| Min RAM required | 0 GB |
| GPU layers | 64 / 64 |
| Context size | 89,949 |
| 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/q8_0/nvidia-l40s.yaml) applyGenerated values.yaml
/values/deepseek-r1-distill-qwen-32b/q8_0/nvidia-l40s.yaml
Loading values…Frequently Asked Questions
How much VRAM does DeepSeek R1 Distill Qwen 32B (Q8_0) need?
The Q8_0 quantization of DeepSeek R1 Distill Qwen 32B requires 32.43 GB. All 64 layers fit in the 48 GB of VRAM available on NVIDIA L40S, enabling full GPU acceleration.
Can I run DeepSeek R1 Distill Qwen 32B on NVIDIA L40S?
Yes. NVIDIA L40S provides 48 GB of VRAM, which is enough to run DeepSeek R1 Distill Qwen 32B (Q8_0) 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_0 compresses DeepSeek R1 Distill Qwen 32B from its original size down to 32.43 GB.
What quantization should I choose for DeepSeek R1 Distill Qwen 32B?
Q8_0 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 DeepSeek R1 Distill Qwen 32B on NVIDIA L40S, 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 32B (Q8_0) with Ollama?
Run ollama run deepseek-r1:32b-qwen-distill-q8_0 to start DeepSeek R1 Distill Qwen 32B (Q8_0). Ollama handles downloading the model weights automatically on first run.