Dual RTX 3090 (48GB) vs Single RTX 4090 (24GB) for 70B LLMs
Exact hardware breakdown, VRAM sizing, tokens/second inference throughput, and truthful total cost-of-ownership math.
AI Architect Verdict
Dual RTX 3090 is the Undisputed Sovereign Homelab 70B Champion
No matter how fast an RTX 4090 is, Meta LLaMA 3.3 70B requires 44.1 GB VRAM at Q4_K_M with 8k context. A single 24GB card physically cannot load the model weights, resulting in devastating CPU offloading (<2 tokens/sec). Dual RTX 3090 provides 48GB VRAM across two 384-bit memory buses, delivering a fluid 18+ tok/s on 70B models for hundreds of dollars less than a single RTX 4090.
Dual NVIDIA RTX 3090 (48GB Total)
VRAM: 48 GB GDDR6X (Split 2x24GB)
Memory Bus: 2x 384-bit (1,872 GB/s aggregate)
Typical Price: ~$1,400 (Used Dual)
Inference Speed: ~18 tok/s on 70B (Q4_K_M)
Single NVIDIA RTX 4090 (24GB)
VRAM: 24 GB GDDR6X
Memory Bus: 384-bit (1,008 GB/s)
Typical Price: ~$1,800 (New Single)
Inference Speed: Cannot fit 70B (OOM / CPU Offload <2 tok/s)
Popular AI Model Fit Comparison
| AI Model | VRAM Req (8k) | Dual NVIDIA RTX 3090 | Single NVIDIA RTX 4090 |
|---|---|---|---|
| Meta LLaMA 3.3 70B (Q4_K_M) | 44.1 GB | Runs Comfortably (+3.9GB) | Exceeds VRAM (OOM) |
| Qwen 2.5 72B Instruct (Q4_K_M) | 45.4 GB | Runs Comfortably (+2.6GB) | Exceeds VRAM (OOM) |
| Qwen 2.5 Coder 32B (Q8_0) | 37.6 GB | Runs Comfortably (+10.4GB) | Exceeds VRAM (OOM) |
| DeepSeek Coder V2 Lite (Q4_K_M) | 12.0 GB | Runs Comfortably (+36.0GB) | Runs Comfortably (+12.0GB) |
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