| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 5060 Ti | NVIDIA GeForce RTX 5070 |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q6_K) | 15.94 GB | Tight Fit (+0.06 GB, ~26 tok/s) | Exceeds VRAM (-3.94 GB OOM) |
| Qwen 2.5 14B Instruct (Q4_K_M, 32k FP8) | 14.06 GB | Tight Fit (+1.94 GB, ~37 tok/s) | Exceeds VRAM (-2.06 GB Swap) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Tight Fit (+1.57 GB, Resident) | Exceeds VRAM (-2.43 GB Spill) |
| Alibaba Wan 2.1 14B 720p (Q4 / FP8) | 18.15 GB | Light Block-Swap (3–4 Layers) | Severe Thrash (-6.15 GB Spill) |
| Google Gemma 4 12B Multimodal (Q4_K_M) | 9.55 GB | Comfort Margin (+6.45 GB, ~46 tok/s) | Fits (+2.45 GB, ~68 tok/s) |
Blackwell unit economics expose why the $549 RTX 5070 fails AI builders: NVIDIA trims GB205 down to six 32-bit GDDR7 channels (192-bit, 672 GB/s) totaling 12GB ($45.75/GB) at 250W TDP. Conversely, GB206 inside the $449 RTX 5060 Ti uses clamshell high-density 28 Gbps GDDR7 modules on a 128-bit bus (448 GB/s) at 180W, lowering VRAM cost to $28.06/GB. Because Windows desktop compositors reserve ~1.0 GB of dedicated memory, a 12GB card actually leaves ~11 GB usable—immediately choking Qwen 2.5 14B Q4_K_M (12.08 GB at 8k FP16 KV), Phi-4 14B Q6_K (15.96 GB), and Wan 2.1 14B 720p diffusion (18.15 GB un-offloaded). On the 16GB card, Qwen 2.5 14B at 32k FP8 context (14.06 GB) stays completely resident and Wan 2.1 requires swapping only 3–4 DiT blocks rather than half the transformer.
Already own a 12GB RTX 5070? Shrink 14B footprints below 11 GB by running `llama-server -m Phi-4-14B-IQ4_XS.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0 -c 4096` and plug your desktop monitors into motherboard integrated iGPU ports so Windows DWM stops stealing ~1 GB of dedicated GDDR7.
Matched Upgrade: Running 16GB diffusion and GGUF workloads requires rapid cold-staging from disk; a Samsung 990 PRO 2TB NVMe ensures multi-gigabyte Wan 2.1 and Phi-4 checkpoints load in seconds.
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Model file size ignores KV-cache tensors, PyTorch compute activations, and CUDA driver scratchpad buffers (~1.5–1.9 GB). With an 8k FP16 KV-cache, 14B Q4_K_M swells to 12.08 GB—and Q6_K reaches 15.94 GB—immediately spilling out of 12GB Windows VRAM into RAM.
Yes—provided you load GGUF Q4 or FP8 DiT weights, offload the heavy UMT5-XXL text encoder onto system RAM, enable tiled 3D VAE decoding, and swap 3–4 transformer blocks, avoiding the relentless PCIe thrashing that cripples 12GB GPUs.
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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