| Model & Quantization | VRAM Demand | Dual NVIDIA RTX 3060 | NVIDIA GeForce RTX 3090 |
|---|---|---|---|
| Qwen 2.5 Coder 32B (Q4_K_M, 8k) | 23.1 GB | Tight Split (~13 tok/s) | Runs Comfortably (~34 tok/s) |
| Google Gemma 4 31B (Q4_K_M, 8k) | 21.4 GB | Runs Split (~14 tok/s) | Runs Comfortably (~37 tok/s) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.7 GB | Runs Split (~48 tok/s) | Runs Comfortably (~95 tok/s) |
| Alibaba Wan 2.1 14B 720p (Q4) | 18.2 GB | Fails Single-Card 12GB Limit | Runs Comfortably (+5.8GB Unified) |
| FLUX.1 [dev] (Q4 / NF4) | 15.5 GB | Requires CPU Offload (>12GB) | Runs Comfortably (+8.5GB Unified) |
Both rigs total 24GB on paper, yet their internal silicon topology behaves very differently under real AI workloads. Pairing two GA106 boards isolates two 192-bit GDDR6 domains (360 GB/s each, zero NVLink fingers) across separate PCIe slots drawing 340W combined. Pipeline splitting (`-sm layer`) activates only one card while layers pass sequentially, capping dense 32B decode near single-card 360 GB/s speed; row tensor splitting (`-sm row`) hits PCIe latency barriers unless both slots wire x8 lanes cleanly. Conversely, the monolithic GA102 flagship aggregates 10,496 CUDA cores and twenty-four 1GB Micron GDDR6X chips onto one unified 384-bit 936 GB/s memory controller. Video diffusion models like Wan 2.1 14B Q4 (`18.15 GB`) or FLUX.1 [dev] reside untouched inside one contiguous address space, and a single RTX 3090 retains an NVLink bridge upgrade path to 48GB later.
Already running Dual RTX 3060 12GB cards? Pass `-sm row -ts 1,1 -fa on -ctk q8_0 -ctv q8_0` in `llama-server` to unlock cross-card matrix row parallelism on x8/x8 boards, or deploy sparse `Qwen3-Coder 30B-A3B` (`20.66 GB`) and offload overflow experts via `--n-cpu-moe` if PCIe slots bottleneck.
Matched Upgrade: Applying a 50x40mm Thermal Grizzly PhaseSheet PTM phase-change pad when buying a ~$680 used RTX 3090 cures dried factory paste hotspots without pump-out, keeping GA102 core and GDDR6X thermals quiet during 24/7 inference.
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No. Standard PyTorch and ComfyUI diffusion loaders allocate the primary DiT model onto GPU 0 (`12 GB`), causing OOM crashes or heavy host RAM swapping on Wan 2.1 14B (`18.15 GB`) unless extra custom multi-GPU nodes are manually configured.
Under default layer-split inference, one GPU waits idle while the active card reads layers at 360 GB/s. The RTX 3090's 384-bit GDDR6X bus reads weights at 936 GB/s natively, hitting ~34 tok/s versus ~13 tok/s.
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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