Can I Run Qwen 2.5 72B Instruct 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 Qwen 2.5 72B Instruct (72.7B) 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. (Qwen 2.5 72B Instruct @ 51.97 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 145.4 GB | 5.37 GB | 157.31 GB (328%) | 0 t/s | OOM |
| Q8_08.5 bpw | 77.06 GB | 5.37 GB | 86.58 GB (180%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 59.61 GB | 5.37 GB | 68.52 GB (143%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 51.62 GB | 5.37 GB | 60.25 GB (126%) | ~4 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 43.62 GB | 5.37 GB | 51.97 GB (108%) | ~5 t/s | PARTIAL |
| FP44.25 bpw | 38.53 GB | 5.37 GB | 46.7 GB (97%) | ~26 t/s | FITS |
| IQ3_XS3.3 bpw | 29.81 GB | 5.37 GB | 37.67 GB (78%) | ~33 t/s | FITS |
Export Full Architecture & Infrastructure Spec
Download an instant Markdown architecture spec with exact memory math, hardware benchmarks, and runtime CLI flags.
Frequently Asked Questions: Qwen 2.5 72B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
How much VRAM does Qwen 2.5 72B Instruct need on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 72B Instruct requires 51.97 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 2.5 72B Instruct 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.