The RTX 5070 Ti is NVIDIA’s cheapest fast 16 GB card, one step below the RTX 5080. It has double the memory bandwidth of the RTX 5060 Ti, so the same models run about twice as fast.
The catch in November 2026 is the price. It launched at $749 and now sells for $1,230 to $1,400. This guide covers what it runs, how fast it is, the settings I’d use, and whether it’s still worth buying at today’s prices.
- Specs: 8,960 CUDA cores, 16 GB GDDR7 on a 256-bit bus, 896 GB/s, 300 W.
- Price: about $1,230-$1,400 new in early November 2026, up from a $749 MSRP. Used cards are no cheaper.
- LLMs: 7B-20B models fully on the GPU, roughly twice as fast as a 5060 Ti. 27B models need a Q3 quant or offload.
- Images and video: one of the best 16 GB cards for ComfyUI, with FP8 and FP4 support.
- Verdict: buy it for speed on 16 GB models. If you need more VRAM, a used RTX 3090 (24 GB) costs about the same.
RTX 5070 Ti Specs for AI
For local AI, two specs matter most. VRAM decides which models fit. Bandwidth decides how fast an LLM writes tokens.
| Spec | RTX 5070 Ti | What it means for AI |
|---|---|---|
| Architecture | Blackwell (GB203) | FP4 and FP8 Tensor maths, current CUDA |
| CUDA cores | 8,960 | Nearly double the 5060 Ti, so images render faster |
| Tensor cores | 280 (5th gen) | Speeds up PyTorch, ComfyUI and prompt processing |
| VRAM | 16 GB GDDR7 | Same capacity as the 5060 Ti 16GB and 5080 |
| Memory bus | 256-bit | Twice as wide as the 5060 Ti |
| Bandwidth | 896 GB/s | About 2x the 5060 Ti for LLM token speed |
| Power | 300 W, 750 W PSU suggested | Needs a decent power supply and case airflow |
| Interface | PCIe 5.0 x16 | Full lanes, so loading and offload are quick |
Note: the RTX 5080 uses the same GB203 chip with more cores and the same 16 GB. For LLMs the two cards are close. The 5080 mostly helps with image and video speed.
Price in November 2026 (and Why It Jumped)
The 5070 Ti launched at $749. Like every RTX 50 card, it now sells far above that.
| Where | Price | Date |
|---|---|---|
| Tom’s Hardware lowest US price tracker | $1,229 | 26 Oct 2026 |
| NVIDIA marketplace (PNY) | $1,269.99 | 2 Nov 2026 |
| Newegg, new (GPUDojo tracker) | $1,339.99 | 6 Nov 2026 |
| eBay, used (GPUDojo tracker) | $1,399.99 | 6 Nov 2026 |
| Amazon, new (GPUDojo tracker) | $1,745.00 | 6 Nov 2026 |
Used prices on eBay were around $900-$995 from late August to the end of October. In the first week of November they jumped to about $1,400, which is now more than new.
Retailers and trackers link the rise to memory costs and tight supply, but NVIDIA hasn’t announced a new MSRP. I explain the memory side in my GPU prices 2026 guide and RAM prices guide.
Warning: don’t pay more for a used card than for a new one. A new card comes with a full warranty, so check Newegg, Best Buy and the NVIDIA marketplace first.
Which LLMs Run on It
A model plus its context has to fit in 16 GB to run at full speed. Here’s what fits at sensible quality.
| Model size | Example | Fits on 16 GB? |
|---|---|---|
| 7B-9B | MiMo-V2.6 9B, Qwen3.8 8B | Yes, even at Q8 with long context |
| 12B-14B | Gemma 4 12B | Yes at Q4-Q6 |
| 20B-24B | gpt-oss-20b-class, Mistral Small class | Yes at Q4, with 8K-16K context |
| 27B-31B dense | Qwen3.8 27B, Gemma 4 31B | Only at Q3, or with offload to RAM |
| 30B-35B MoE | Qwen3.6 35B-A3B | Yes with light offload, and still fast |
| 70B and up | Llama-class 70B, 120B MoE | Only with heavy offload and 64-128 GB of RAM |
For a 27B model on this card, follow my Qwen3.8-27B on a 16 GB GPU guide. For ready-to-pull picks, see the best Ollama models list.
How Fast Is It?
When an LLM writes text, it reads the whole model from VRAM for every token. So token speed scales with bandwidth divided by file size. At 896 GB/s, the 5070 Ti is about twice as fast as a 5060 Ti on the same model.
| Model (Q4_K_M) | Approx. file | Expected speed (estimate) |
|---|---|---|
| 7B-9B | 5-7 GB | about 90-130 tok/s |
| Gemma 4 12B | ~8 GB | about 70-90 tok/s |
| 14B | ~9 GB | about 60-75 tok/s |
| 20B-class | ~12-13 GB | about 45-60 tok/s |
| Qwen3.8 27B at Q3, all on GPU | ~13-14 GB | about 40-50 tok/s |
Note: these are my estimates from bandwidth and file size, not tests I ran. GPUDojo’s own estimate for a 14B model is about 67 tok/s generation and 786 tok/s prompt processing, which fits the table.
Speed falls sharply once a dense model spills into system RAM. MoE models handle offload much better because only a few experts run per token.
Image and Video Generation in ComfyUI
This is where the 5070 Ti beats the cheaper 16 GB cards most clearly. With nearly twice the CUDA and Tensor cores of a 5060 Ti, images take roughly half the time.
- Image models (Qwen Image 2.1, Flux-class, SDXL): run FP8 or GGUF Q8 versions. Put the text encoder in system RAM if you get close to 16 GB.
- Video models (Wan 2.2, LTX 2.5): GGUF Q4-Q6 and 5-second clips work well. My LTX 2.5 on 16 GB guide has a full setup.
- FP4 models: Blackwell runs NVFP4 checkpoints natively, which cuts memory and speeds up supported models.
- LoRA training: image LoRAs train comfortably. Video LoRAs work but need low resolution and patience.
Tip: speed-up nodes like SageAttention help a lot on this card. My SageAttention on Windows guide shows the install.
Setup: Drivers, Ollama, llama.cpp and ComfyUI
- Install a current NVIDIA driver. Use the NVIDIA app or nvidia.com. Blackwell cards need a recent driver branch.
- Check the card. Run
nvidia-smiin a terminal. You should see the RTX 5070 Ti with about 16,300 MiB of memory. - Install Ollama. Follow my Ollama install guide. It finds the GPU on its own.
- Run a first model. Try
ollama run gemma4:12b, then checkollama ps. It should say 100% GPU. - Optional llama.cpp. Download a CUDA build and use
llama servefor more control over offload and context. - Set up ComfyUI. Use a PyTorch build for CUDA 12.8 or newer. My ComfyUI on RTX 50 series guide lists the exact versions.
If you’re choosing between apps, my Ollama vs LM Studio vs llama.cpp comparison explains the differences.
Best Settings for 16 GB
| Setting | Recommended | Why |
|---|---|---|
| Quant | Q4_K_M-Q6_K up to 20B, Q3 for 27B | Best quality that still fits |
| Context | 8K-32K | Long contexts eat VRAM quickly |
| KV cache | OLLAMA_KV_CACHE_TYPE=q8_0 |
Roughly halves context memory |
| Flash attention | OLLAMA_FLASH_ATTENTION=1 |
Needed for KV cache quantisation |
| MoE offload | Expert layers on CPU in llama.cpp | Runs 35B MoE models fast |
| Power limit | Default 300 W, or 250 W | Small LLM speed loss, cooler and quieter |
Tip: a lower power limit hurts LLM speed very little, because LLMs are limited by memory, not compute. You can set it with nvidia-smi -pl 250 from an admin terminal.
5070 Ti vs 5060 Ti vs 5080 vs Used 3090
| Card | VRAM | Bandwidth | Price (Nov 2026) | Best for |
|---|---|---|---|---|
| RTX 5060 Ti 16GB | 16 GB | 448 GB/s | ~$770-$800 | Budget entry to local AI |
| RTX 5070 Ti | 16 GB | 896 GB/s | ~$1,230-$1,400 | Fast 16 GB LLMs and ComfyUI |
| RTX 5080 | 16 GB | 960 GB/s | ~$1,580-$1,700 | A bit faster images, same VRAM |
| Used RTX 3090 | 24 GB | 936 GB/s | ~$1,300-$1,500 | 27B-32B models at better quants |
Quick answer: if your models fit in 16 GB, the 5070 Ti is the sweet spot. The 5080 costs a few hundred more for a small gain. If you want 27B-32B models at good quality, a used 3090 gives 8 GB more for similar money.
More detail is in my RTX 5060 Ti 16GB guide, used RTX 3090 guide and RTX 5090 vs 5080 comparison.
Buying Tips at Inflated Prices
- Compare new and used. In November 2026 used cards cost as much as new ones, so new is usually the better deal.
- Check the NVIDIA marketplace. Partner cards sometimes appear there closer to MSRP than at big retailers.
- Watch price trackers. Prices moved by over $100 in a week, so set alerts rather than buying in a hurry.
- Avoid third-party sellers. Big retailers sometimes list marketplace sellers. Check who ships the card.
- Consider renting. For a one-off big job, a cloud GPU can be cheaper. See my cheapest cloud GPU guide.
Troubleshooting
CUDA out of memory
Lower the context, use a smaller quant or turn on KV cache quantisation. My CUDA out of memory fix walks through PyTorch, ComfyUI and Ollama.
Ollama shows a CPU share
The model plus context is too big for 16 GB. Shrink the context or pick a smaller quant, then check again with ollama ps.
No kernel image is available
Your PyTorch build is too old for Blackwell. Install a build for CUDA 12.8 or newer.
Black screen or crashes under load
Check the 12V-2×6 power cable is fully seated and that the PSU is at least 750 W. Avoid cheap adapters.
Lower speed than expected
Watch VRAM with my GPU and VRAM usage guide. If memory is full, Windows may be spilling into shared memory, which is very slow.
Frequently Asked Questions
Is the RTX 5070 Ti good for AI?
Yes. It has 16 GB and 896 GB/s, so it runs 7B-20B LLMs fully on the GPU at high speed and is one of the fastest 16 GB cards for ComfyUI.
How much is the RTX 5070 Ti in November 2026?
About $1,230-$1,400 new at US retailers in early November 2026, up from a $749 launch MSRP. Used cards on eBay were about $1,400.
Can the RTX 5070 Ti run a 27B model?
Yes with a Q3 quant fully on the GPU, or a Q4 quant with some layers in system RAM. Keep the context at 16K-32K.
RTX 5070 Ti or RTX 5080 for AI?
Both have 16 GB, and LLM speed is close. The 5080 is a little faster for images and video but costs a few hundred dollars more.
RTX 5070 Ti or used RTX 3090 for local LLMs?
The 3090 has 24 GB, which fits bigger models. The 5070 Ti is new, has a warranty, uses less power and supports FP8 and FP4.
How many tokens per second does the RTX 5070 Ti get?
Roughly 60-75 tokens per second on a 14B model at Q4, and over 90 on 7B-9B models. These are estimates from bandwidth.
What power supply do I need for an RTX 5070 Ti?
NVIDIA suggests a 750 W unit. Use a quality PSU with a native 12V-2×6 cable.
Final Thoughts
The RTX 5070 Ti is the best 16 GB card for people who care about speed. It writes tokens twice as fast as the 5060 Ti and renders images much faster, with full CUDA support.
At $1,230-$1,400 it’s no longer a bargain, so be sure 16 GB covers what you want to run. If not, look at my best GPU for local LLMs picks or how much VRAM you need first.
