The RTX 5070 Ti has 16 GB of GDDR7 on a 256-bit bus, 8,960 CUDA cores, 896 GB/s of memory bandwidth and 1,406 AI TOPS, at 300 W total graphics power. It launched at a $749 list price in February 2025.
For AI, 16 GB is the number to plan around: it runs 8B models at 8-bit, 14B models at 4-bit and SDXL-class image models, and it does not run 32B models at 4-bit. It has the same memory size as the more expensive RTX 5080.
TL;DR
- 16 GB GDDR7 is the same capacity as the RTX 5080, for a lower launch list price ($749 against $999).
- Fits (computed): 8B at INT8 and INT4, 14B at INT4. Does not fit: 14B at INT8 by a hair, or 32B at any common precision.
- 896 GB/s of bandwidth is 93% of the 5080's 960 GB/s, so memory-bound generation is closer than the price gap suggests (computed).
- 300 W is easy to power and cool compared with the 5090's 575 W.
- It is the sensible mid-range pick if 16 GB covers your models.
RTX 5070 Ti specs
| Spec | RTX 5070 Ti | RTX 5080 | RTX 5060 Ti |
|---|---|---|---|
| CUDA cores | 8,960 | 10,752 | 4,608 |
| Memory | 16 GB GDDR7 | 16 GB GDDR7 | 16 GB or 8 GB GDDR7 |
| Memory bus | 256-bit | 256-bit | 128-bit |
| Memory bandwidth | 896 GB/s | 960 GB/s | 448 GB/s |
| AI TOPS (Tensor) | 1,406 | 1,801 | 759 |
| Total graphics power | 300 W | 360 W | 180 W |
| Launch list price | $749 (Feb. 2025) | $999 (Jan. 30, 2025) | $429 (16 GB), $379 (8 GB) (Apr. 16, 2025) |
All values are from NVIDIA's pages (Sources below). The launch list prices are NVIDIA's at launch, and what cards sell for today differs. Side by side with live availability: the RTX 5060 Ti vs RTX 5070 Ti compare page, and the RTX 5070 Ti page.
What fits in 16 GB
Computed: weights equal parameters times bytes per parameter (FP16 is 2, INT8 is 1, INT4 is 0.5), plus 20% for KV cache and runtime buffers, which is our assumption. Long contexts need more. The formula is explained in how much VRAM you need for LLMs.
| Model size | FP16 with overhead | INT8 with overhead | INT4 with overhead | Fits in 16 GB? |
|---|---|---|---|---|
| 8B | 19.2 GB | 9.6 GB | 4.8 GB | INT8 and INT4 |
| 14B | 33.6 GB | 16.8 GB | 8.4 GB | INT4 only |
| 32B | 76.8 GB | 38.4 GB | 19.2 GB | None |
| 70B | 168 GB | 84 GB | 42 GB | None |
In practice, an 8B model at 8-bit leaves around 6 GB for KV cache, which is plenty for a chat session. A 14B model at 4-bit leaves around 7.6 GB. You can check specific models on the RTX 5070 Ti VRAM calculator. Quantization formats such as GGUF, GPTQ and AWQ are what make the INT4 row possible, and each has a quality cost you should test on your own task.
Running local LLMs
Ollama and llama.cpp both use an NVIDIA GPU out of the box. From Ollama's README:
curl -fsSL https://ollama.com/install.sh | sh
ollama run gemma4
From llama.cpp's README, which also lists support for 1.5-bit through 8-bit integer quantization:
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
Replace the model with one that fits your 16 GB budget using the table above. For setup details, see our Ollama guide and llama.cpp guide.
If a model is slightly too big, both tools can keep some layers in system RAM. That makes it load but is much slower than a model that fits entirely in VRAM, because generation then waits on system memory.
Image generation
FLUX.1 dev is a 12 billion parameter model per its Hugging Face card. Computed: about 24 GB at BF16 and about 12 GB at 8-bit for the main transformer alone. On 16 GB, that means 8-bit (or smaller) weights and keeping the text encoders in system memory. SDXL-class models are much smaller and are comfortable on this card. ComfyUI is the common front end and runs on a single GPU.
Fine-tuning
Full fine-tuning needs roughly 18 bytes per parameter with mixed-precision AdamW, so it is out of reach here. QLoRA is the practical route. Computed: a 4-bit 8B base is about 4 GB of frozen weights, which leaves room for adapters, optimizer state and activations on 16 GB at short to moderate sequence lengths. A 4-bit 14B base is about 7 GB, which also works with tighter limits. See LoRA fine-tuning.
5070 Ti vs the neighbours
- vs the RTX 5080: same 16 GB and bus width, about 7% less bandwidth (computed) and fewer cores. If 16 GB is enough, the price gap buys you speed and not capacity. See the 5080 vs 5090 comparison for what the next step up buys.
- vs the RTX 5060 Ti 16 GB: same capacity, but the 5060 Ti has half the bus width and half the bandwidth (448 GB/s against 896 GB/s). Both hold the same models. The 5070 Ti generates faster. See the RTX 5060 Ti guide.
- vs the RTX 5090: twice the memory and about 2x the bandwidth, at nearly three times the launch list price. See RTX 5090 for AI.
For the wider buying picture, read consumer GPUs for AI.
Rent today
If 16 GB might not be enough for your model, check before you buy. The box shows what is available now.
FAQ
How much VRAM does the RTX 5070 Ti have?
16 GB of GDDR7 on a 256-bit bus, per NVIDIA.
Is the RTX 5070 Ti good for AI?
For 7B to 14B models at 4-bit or 8-bit, SDXL-class image generation and QLoRA on small models, yes. It cannot hold 32B models at common precisions.
What is the RTX 5070 Ti's launch price?
NVIDIA announced $749, available starting in February 2025. Street prices differ.
Can the RTX 5070 Ti run a 14B model?
At 4-bit, yes (about 8.4 GB with overhead, computed). At 8-bit it needs about 16.8 GB with overhead, just over the limit.
How does the 5070 Ti compare with the 4070 Ti Super for AI?
Both have 16 GB. See our RTX 4070 Ti Super guide for that card's specs.
Sources
- NVIDIA, GeForce RTX 50 series (5070 family): https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5070-family/
- NVIDIA, Compare GeForce graphics cards (bandwidth, cores, TGP, AI TOPS): https://www.nvidia.com/en-us/geforce/graphics-cards/compare/
- NVIDIA, Blackwell GeForce RTX 50 series launch (5070 Ti price and date): https://nvidianews.nvidia.com/news/nvidia-blackwell-geforce-rtx-50-series-opens-new-world-of-ai-computer-graphics
- NVIDIA, RTX 50 series announcements (896 GB/s): https://www.nvidia.com/en-us/geforce/news/rtx-50-series-graphics-cards-gpu-laptop-announcements/
- NVIDIA, GeForce RTX 5080: https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5080/
- NVIDIA, RTX 5060 family: https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/
- Ollama README: https://github.com/ollama/ollama
- llama.cpp README: https://github.com/ggml-org/llama.cpp
- FLUX.1 dev model card: https://huggingface.co/black-forest-labs/FLUX.1-dev
