DeepSeek R1 Distill Qwen 7B (FP16) — 15.4 GBon NVIDIA RTX 5070 Ti

FP16 NVIDIA RTX 5070 Ti

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 16 GB of VRAM on NVIDIA RTX 5070 Ti, enabling full GPU inference.

Hardware Requirements

Model size	14.19 GB
VRAM available	16 GB
VRAM used	15.4 GB
Min RAM required	0.5 GB
GPU layers	27 / 28
Context size	8,839
Backend	cuda13
Flash attention	Yes

Performance Notes

Reduced performance

1 of 28 layers run on CPU. Your system needs at least 0.5 GB RAM. If system RAM is insufficient, data will be read from disk on demand, with significant performance impact.

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/deepseek-r1-distill-qwen-7b/fp16/nvidia-rtx5070ti.yaml) apply

Generated values.yaml

/values/deepseek-r1-distill-qwen-7b/fp16/nvidia-rtx5070ti.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o deepseek-r1-distill-qwen-7b.gguf "https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-F16.gguf"

Start Server

llama-server \
  -m deepseek-r1-distill-qwen-7b.gguf \
  --n-gpu-layers 27 \
  --ctx-size 8839 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• DeepSeek R1 Distill Qwen 7B (Q8_0)
• DeepSeek R1 Distill Qwen 7B (Q5_K_M)
• DeepSeek R1 Distill Qwen 7B (Q4_K_M)

Other hardware

• DeepSeek R1 Distill Qwen 7B on CPU Only
• DeepSeek R1 Distill Qwen 7B on NVIDIA A100 80GB
• DeepSeek R1 Distill Qwen 7B on NVIDIA H100

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. 27 of 28 layers fit in the 16 GB of VRAM on NVIDIA RTX 5070 Ti; remaining layers are offloaded to CPU.

Can I run DeepSeek R1 Distill Qwen 7B on NVIDIA RTX 5070 Ti?

Yes, with reduced performance. NVIDIA RTX 5070 Ti can run DeepSeek R1 Distill Qwen 7B (FP16), but only 27 of 28 layers fit in VRAM. The rest are offloaded to CPU.

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 RTX 5070 Ti, flash attention is enabled to maximise context length and throughput within the available 16 GB of VRAM.

Why are some layers offloaded to CPU?

NVIDIA RTX 5070 Ti has 16 GB of VRAM, but DeepSeek R1 Distill Qwen 7B (FP16) requires approximately 14.19 GB. Only 27 of 28 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

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.

Last updated: March 7, 2026