Can I Run DeepSeek-R1 Distill Qwen 7B on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek-R1 Distill Qwen 7B (7.6B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (48 GB).
✓ Fits Comfortably in VRAM
Local Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8B–14B coding models with FP4/Q6_K precision
Cloud Instance Options for DeepSeek-R1 Distill Qwen 7B
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 15.2 GB | 0.94 GB | 18.12 GB (38%) | ~66 t/s | FITS |
| Q8_08.5 bpw | 8.06 GB | 0.94 GB | 10.73 GB (22%) | ~123 t/s | FITS |
| Q6_K6.56 bpw | 6.23 GB | 0.94 GB | 8.84 GB (18%) | ~158 t/s | FITS |
| Q5_K_M5.68 bpw | 5.4 GB | 0.94 GB | 7.98 GB (17%) | ~181 t/s | FITS |
| Q4_K_M4.8 bpw | 4.56 GB | 0.94 GB | 7.11 GB (15%) | ~213 t/s | FITS |
| FP44.25 bpw | 4.03 GB | 0.94 GB | 6.56 GB (14%) | ~240 t/s | FITS |
| IQ3_XS3.3 bpw | 3.12 GB | 0.94 GB | 5.62 GB (12%) | ~305 t/s | FITS |
Frequently Asked Questions: DeepSeek-R1 Distill Qwen 7B on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
How much VRAM does DeepSeek-R1 Distill Qwen 7B need on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-R1 Distill Qwen 7B requires 7.11 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will DeepSeek-R1 Distill Qwen 7B achieve on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
Based on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)'s 1648 GB/s memory bandwidth, expected decode speed is approximately 213 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.