DeepSeek R1 Distill Qwen 7B (FP16) — 22.5 GBon NVIDIA H100
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 FP16 quantization (full-precision quality tier), the model weighs 14.19 GB. This fits within the 80 GB of VRAM on NVIDIA H100, 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 | 14.19 GB |
| VRAM available | 80 GB |
| VRAM used | 22.5 GB |
| System RAM | |
| 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/fp16/nvidia-h100-80gb.yaml) applyGenerated values.yaml
/values/deepseek-r1-distill-qwen-7b/fp16/nvidia-h100-80gb.yaml
Loading values…Frequently Asked Questions
How much VRAM does DeepSeek R1 Distill Qwen 7B (FP16) need?
The FP16 quantization of DeepSeek R1 Distill Qwen 7B requires 14.19 GB. All 28 layers fit in the 80 GB of VRAM available on NVIDIA H100, enabling full GPU acceleration.
Can I run DeepSeek R1 Distill Qwen 7B on NVIDIA H100?
Yes. NVIDIA H100 provides 80 GB of VRAM, which is enough to run DeepSeek R1 Distill Qwen 7B (FP16) 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. FP16 compresses DeepSeek R1 Distill Qwen 7B from its original size down to 14.19 GB.
What quantization should I choose for DeepSeek R1 Distill Qwen 7B?
FP16 is a full-precision 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 NVIDIA H100, flash attention is enabled to maximise context length and throughput within the available 80 GB of VRAM.
How do I run DeepSeek R1 Distill Qwen 7B (FP16) with Ollama?
Run ollama run deepseek-r1:7b-qwen-distill-fp16 to start DeepSeek R1 Distill Qwen 7B (FP16). Ollama handles downloading the model weights automatically on first run.