| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 4070 Ti Super | NVIDIA GeForce RTX 5070 Ti |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q4_K_M) | 12.08 GB | Comfort Margin (~56 tok/s) | Comfort Margin (~74 tok/s) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Tight Fit (+1.57 GB, ~39 tok/s) | Tight Fit (+1.57 GB, ~52 tok/s) |
| Google Gemma 4 12B Multimodal (Q4_K_M) | 9.55 GB | Comfort Margin (~68 tok/s) | Comfort Margin (~91 tok/s) |
| FLUX.1 [dev] NF4 / 4-bit | 15.46 GB | Tight Fit (+0.54 GB, Ada FP8) | Tight Fit (+0.54 GB, NVFP4) |
| Mistral Small 24B (Q4_K_M) | 17.82 GB | Exceeds VRAM (-1.82 GB OOM) | Exceeds VRAM (-1.82 GB OOM) |
Both GPUs deploy 256-bit memory controllers, making this one of NVIDIA's cleanest generational bandwidth leaps inside an identical VRAM tier. Ada Lovelace's harvested AD103 chip on the RTX 4070 Ti Super pairs 8,448 CUDA cores and 48 MB of L2 cache with 21 Gbps GDDR6X (672 GB/s) over PCIe 4.0 x16 at 285W TDP. Blackwell's GB203 silicon on the RTX 5070 Ti upgrades to 8,960 cores, PCIe 5.0 x16, and PAM3 28 Gbps GDDR7 chips to unlock 896 GB/s at 300W. Crucially for 2026 quantizations, Ada 4th-gen Tensor Cores top out at E4M3 FP8—requiring shader dequantization loops for 4-bit weights—whereas Blackwell's 5th-gen Tensor Cores compute hardware microscaling NVFP4 and MXFP4 natively, noticeably lowering FLUX image generation times and gpt-oss 20B prompt latency.
Already own an RTX 4070 Ti Super? Squeeze peak efficiency out of its 48 MB L2 cache and 672 GB/s bus using `llama-server -m model.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0`, or launch `vllm --kv-cache-dtype fp8` so Ada's FP8 Tensor Cores halve KV-cache footprint cleanly.
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To match the new RTX 5070 Ti's bandwidth-per-dollar ($799 divided by 896 GB/s) while compensating for expired manufacturer coverage and missing NVFP4 hardware acceleration, a secondhand RTX 4070 Ti Super should cost closer to $540–$580 rather than ~$720.
No—both cards contain exactly 16GB of physical memory, capping fully resident dense Q4 checkpoints near 20B parameters. However, native Blackwell NVFP4 format weights preserve noticeably stronger coding and reasoning accuracy than older INT4/W4A16 quantizations inside that same 16GB footprint.
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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