| Model & Quantization | VRAM Demand | Dual NVIDIA RTX 4090 | NVIDIA GeForce RTX 5090 |
|---|---|---|---|
| Meta LLaMA 3.3 70B (Q4_K_M, 8k) | 46.1 GB | Tight Fit (+1.9GB, ~40 tok/s TP=2) | Exceeds VRAM (-14.1GB) |
| Qwen 2.5 72B (Q4_K_M, 8k) | 47.3 GB | Tight Fit (+0.7GB) | Exceeds VRAM (-15.3GB) |
| Qwen 2.5 Coder 32B (Q8_0, 8k) | 39.5 GB | Runs Comfortably (+8.5GB) | Exceeds VRAM (-7.5GB) |
| Alibaba Wan 2.1 14B 720p (FP8) | 28.4 GB | Requires Split / Offload (>24GB) | Runs Comfortably (+3.6GB Unified) |
| Google Gemma 4 31B (Q4_K_M, 128k FP8 KV) | 30.6 GB | Runs Comfortably (+17.4GB) | Tight Fit (+1.4GB, ~68 tok/s) |
Combining two Ada Lovelace AD102 boards pools 48GB across two 384-bit GDDR6X interfaces (2,016 GB/s aggregate) with 4th-gen FP8 Tensor Cores, yet omits NVLink entirely. Multi-GPU inference therefore relies on motherboard PCIe 4.0/5.0 x8/x8 bifurcation for NCCL all-reduce traffic while dissipating up to 900W inside workstation chassis. By comparison, Blackwell's single-board GB202 chip wires sixteen 32-bit 28 Gbps GDDR7 channels (512-bit = 1,792 GB/s) directly into one 32GB unified pool backed by 5th-gen Tensor Cores accelerating microscaling `NVFP4`/`MXFP4` math. While Dual 4090 beats a solo 5090 decisively on dense 70B Q4_K_M and 32B Q8_0 (`39.47 GB`) capacity, a single RTX 5090 runs Wan 2.1 14B FP8 (`28.36 GB`) and FLUX.1 [dev] FP8 (`24.09 GB`) cleanly in one address space without multi-GPU synchronization losses.
Tuning Dual RTX 4090s? Apply `nvidia-smi -pl 350` per GPU to slash combined power by 200W and run `vllm serve meta-llama/Llama-3.3-70B-Instruct --tensor-parallel-size 2 --kv-cache-dtype fp8`. On a solo 32GB RTX 5090, deploy `Gemma 4 31B` at `Q6_K` (`~29.2 GB`) or `Qwen3-Coder 30B-A3B` (`20.66 GB`) rather than compressing 70B to 3-bit.
Matched Upgrade: Handling a 900W Dual RTX 4090 tensor-parallel workstation or a 600W RTX 5090 rig demands a Corsair RM1200x SHIFT ATX 3.1 PSU with native 12V-2x6 power cables and rock-solid transient stability.
1200W ATX 3.1 PSU on Amazon Prime ↗Zero monthly subscriptions, 100% private inference, and unmetered tokens for your homelab or workstation.
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At 4.25-bit `IQ4_XS` or `NVFP4` (~39.4 GB weights alone), a 70B checkpoint still exceeds 32GB GDDR7 by ~7.4 GB. Only aggressive 3-bit `IQ3_XS` fits on one 5090, whereas Dual RTX 4090s host full Q4_K_M cleanly in VRAM.
On a workstation motherboard supporting native PCIe 4.0 or 5.0 x8/x8 CPU lane bifurcation, vLLM tensor parallelism still sustains roughly 38–42 tok/s on dense 70B Q4 checkpoints, though asymmetric x16/x4 PCH chipset routing noticeably degrades multi-GPU all-reduce synchronization latency and batch throughput.
Inspect exact KV-cache formulas, test 32k/128k context allocations, or compare cloud rental break-even costs in the interactive matchmaker.
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