Can I Run DeepSeek-R1 Distill Llama 70B 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 Llama 70B (70.6B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (48 GB).
! Partial Offload — Slow CPU/RAM Spillover
Local Hardware Configurations & Pricing
Partial Offload: Dedicated 100% GPU OptionsNVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
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
Apple Mac Mini M4 Pro (64GB Unified Memory)
Silent 65W desktop running 32B–70B models with large KV context
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Mistral Large 2 123B & Llama 3.3 70B Q8_0 locally with 128K context
Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek-R1 Distill Llama 70B @ 50.66 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 141.2 GB | 5.37 GB | 152.96 GB (319%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 84.28 GB (176%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.74 GB (139%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58.7 GB (122%) | ~4 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 50.66 GB (106%) | ~5 t/s | PARTIAL |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 45.55 GB (95%) | ~27 t/s | FITS |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.78 GB (77%) | ~34 t/s | FITS |
Frequently Asked Questions: DeepSeek-R1 Distill Llama 70B on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
How much VRAM does DeepSeek-R1 Distill Llama 70B 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 Llama 70B requires 50.66 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will DeepSeek-R1 Distill Llama 70B 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 5 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.