# NVIDIA RTX 5070 Ti (16GB) vs RTX 5080 (16GB) for Local AI

> **Direct Hardware Verdict**: Both Blackwell GPUs hit the exact same 16GB memory wall, mirroring the old 4070 Ti Super versus 4080 Super dilemma. Paying $300 extra for the RTX 5080 buys just +7.1% memory bandwidth (960 vs 896 GB/s) and zero additional VRAM—yielding roughly 5 extra tokens per second on Qwen 2.5 14B (80 vs 74 tok/s). Put those $300 savings into 64GB DDR5 system RAM so hybrid MoE models never stall on host memory.

## Contender Specifications
| Metric | NVIDIA GeForce RTX 5070 Ti (16GB) | NVIDIA GeForce RTX 5080 (16GB) |
|---|---|---|
| VRAM Capacity | 16 GB GDDR7 | 16 GB GDDR7 |
| Memory Bandwidth | 256-bit (896 GB/s) | 256-bit (960 GB/s) |
| Typical Price | ~$799 (New) | ~$1,099 (New) |
| Estimated Throughput | ~74 tok/s (14B Q4) / ~91 tok/s (12B Q4) | ~80 tok/s (14B Q4) / ~98 tok/s (12B Q4) |

## Comparative Model Compatibility Matrix
| 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) |


## Silicon Architecture & Thermal Analysis
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.

## Tier 1 Free Software Optimization ($0)
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.

- Companion Upgrade (Crucial Pro 64GB (2x32GB) DDR5-5600 Kit): https://www.amazon.com/dp/B0C79H54TQ?tag=sparecables-20 (rel="noopener sponsored")

## Verified Hardware & Cloud Pathways
- ASUS Prime GeForce RTX 5070 Ti 16GB GDDR7 OC on Amazon Prime: https://www.amazon.com/dp/B0DVGVZZYY?tag=sparecables-20 (rel="noopener sponsored")
- NVIDIA GeForce RTX 5080 16GB GDDR7 on Amazon Prime: https://www.amazon.com/dp/B0DS2R6948?tag=sparecables-20 (rel="noopener sponsored")
- Rent on RunPod: https://console.runpod.io/deploy?ref=ynypzu94 (rel="noopener sponsored")
- Rent on Vast.ai: https://cloud.vast.ai/?ref_id=723130 (rel="noopener sponsored")

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*Interactive Sizing Calculator available at: https://www.gpugarden.com/compare/rtx-5070-ti-16gb-vs-rtx-5080-16gb/*