| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 5070 Ti | NVIDIA GeForce RTX 5080 |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q4_K_M) | 12.08 GB | Comfort Margin (~74 tok/s) | Comfort Margin (~80 tok/s) |
| Google Gemma 4 12B Multimodal (Q6_K) | 12.70 GB | Comfort Margin (~63 tok/s) | Comfort Margin (~68 tok/s) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Tight Fit (+1.57 GB) | Tight Fit (+1.57 GB) |
| Mistral Small 24B (Q4_K_M) | 17.82 GB | Exceeds VRAM (-1.82 GB) | Exceeds VRAM (-1.82 GB) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.66 GB | CPU MoE Offload (--n-cpu-moe) | Identical 16GB Wall (-4.66 GB) |
Unlike flagship shifts that widen the memory interface, NVIDIA carved both the RTX 5070 Ti and RTX 5080 from the exact same GB203 silicon die wired to an identical 256-bit GDDR7 controller. The RTX 5080 enables all 84 SMs (10,752 CUDA cores) and clocks GDDR7 chips slightly faster at 30 Gbps for 960 GB/s at 360W TDP, whereas the harvested RTX 5070 Ti runs 70 SMs (8,960 cores) with 28 Gbps GDDR7 yielding 896 GB/s at 300W. Because single-user autoregressive LLM decoding reads the full active weight matrix once per generated token, throughput scales almost linearly with memory bandwidth rather than shader counts. That means the 5080's 20% CUDA core advantage mainly accelerates FP16 LoRA training passes or heavy 32k RAG prompt ingestion while delivering an imperceptible 7% decode bump.
Unlock 30B+ Mixture-of-Experts models on a 16GB RTX 5070 Ti without paying $1,099 for an RTX 5080 by running `llama-server -m Qwen3-Coder-30B-A3B-Q4_K_M.gguf -ngl 99 --n-cpu-moe 14 -fa on -ctk q8_0 -ctv q8_0`, pinning hot attention tensors in 896 GB/s GDDR7 while streaming sparse experts cleanly from 64GB DDR5.
Matched Upgrade: Redirect the $300 saved by choosing the RTX 5070 Ti over the RTX 5080 straight into a 64GB DDR5 kit for smooth MoE expert offloading and huge model mmap staging.
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No. Both cards expose the exact same 16,384 MB of physical GDDR7 memory once desktop overhead is subtracted. Any checkpoint exceeding ~15.2 GB usable VRAM—such as Mistral Small 24B Q4_K_M (17.82 GB) or Qwen 32B Q4—overflows identically on both.
Only in compute-saturated workloads rather than interactive token generation—specifically multi-epoch QLoRA fine-tuning, large-batch ComfyUI diffusion pipelines, dense 32k context prompt ingestion, or 4K gaming where the RTX 5080's 10,752 shaders outpace the RTX 5070 Ti's 8,960 shaders by 15–18%.
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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