The RTX 5060 Ti 16GB is the card I recommend most often to people starting with local AI. It’s the cheapest current NVIDIA GPU with 16 GB of VRAM, and VRAM decides what you can run.
But it has a narrow 128-bit memory bus, and prices have climbed a lot in 2026. This guide shows exactly what it runs, how fast, the settings I’d use, and when another card makes more sense.
- Specs: 4,608 CUDA cores, 144 Tensor cores, 16 GB GDDR7, 128-bit, 448 GB/s, 180 W, 450 W PSU.
- Price: about $770 in November 2026 (launch MSRP $429).
- LLMs: 7B-14B models fully on the GPU at 50-100 tokens/s. MoE models like Gemma 4 26B and Qwen3.6 35B-A3B run well with a small quant or offload.
- Images: fine for most image models in FP8 or GGUF. Video works with GGUF but is slow.
- Avoid the 8 GB version. Same name, half the memory, much less useful for AI.
- Verdict: the best-value way into local AI on NVIDIA today. Pay more only for speed (5070 Ti) or 24-32 GB (used 3090, R9700).
RTX 5060 Ti 16GB Specs for AI
These are the specs from TechPowerUp’s database. The two that matter most for AI are the 16 GB and the 448 GB/s.
| Spec | RTX 5060 Ti 16GB | What it means for AI |
|---|---|---|
| Architecture | Blackwell (GB206) | Supports FP4/FP8 Tensor math and current CUDA |
| CUDA cores | 4,608 | Compute for image/video models and prompt processing |
| Tensor cores | 144 (5th gen) | Speeds up matrix maths in PyTorch and ComfyUI |
| VRAM | 16 GB GDDR7 | Decides which models fit |
| Memory bus | 128-bit | The weak spot |
| Bandwidth | 448 GB/s | Decides LLM token speed |
| Power | 180 W, 450 W PSU suggested | Easy to run in almost any PC |
| Interface | PCIe 5.0 x8 (in an x16 slot) | Slower model loading on old PCIe 3.0 boards |
Warning: NVIDIA also sells an RTX 5060 Ti 8GB. Check the box and listing carefully. For AI, the 8 GB card is a big step down.
Price in November 2026
The 5060 Ti launched at $429 for the 16 GB version. The global memory shortage has pushed it to about $769 at the cheapest US retailers, according to Tom’s Hardware’s tracker on 3 November 2026.
| GPU | VRAM | Bandwidth | MSRP | Street (Nov 2026) | $ per GB VRAM |
|---|---|---|---|---|---|
| RTX 5060 Ti 16GB | 16 GB | 448 GB/s | $429 | ~$770 | ~$48 |
| RTX 5070 Ti | 16 GB | 896 GB/s | $749 | ~$1,400 | ~$88 |
| RTX 5080 | 16 GB | 960 GB/s | $999 | ~$2,000 | ~$125 |
| RTX 3090 (used) | 24 GB | 936 GB/s | – | ~$1,450 | ~$60 |
| Radeon AI PRO R9700 | 32 GB | 640 GB/s | $1,299 | ~$1,700-$1,850 | ~$55 |
Board partners raised RTX 5060 Ti 16 GB cost prices by another $37-$60 in early November, so expect it to creep up. I explain why in why GPU prices are so high in 2026.
Which LLMs Run on 16 GB
A model needs its file size plus 1-2 GB for context. On a 16 GB card, about 14-15 GB is usable once Windows and the driver take their share.
Fits fully on the GPU
- Gemma 4 12B (about 8 GB): the best all-rounder, with vision and room for a long context.
- MiMo-V2.6 9B (6.6-8.7 GB): strong at coding and agents.
- Granite 4.2 8B (5.3 GB) and Bonsai 2 27B (8.3 GB, ternary).
- gpt-oss-20b and other 14B-20B models at Q4.
Runs with a small quant or some offload
- Qwen3.8 27B (18 GB at default): use a Q3 quant to fit, or let a few layers spill into RAM. See my Qwen3.8-27B on 16 GB guide.
- Gemma 4 26B MoE (about 3.8B active) and Qwen3.6 35B-A3B: MoE models stay fast even when part sits in RAM.
- Flint 40B (23 GB, about 2.5B active): works with offload and 32 GB+ of system RAM.
Too big for one 5060 Ti
- Dense 31B-70B models at good quality. Gemma 4 31B only fits at very low quants.
- 120B-class MoE models need 64-128 GB of system RAM with heavy offload.
My best Ollama models by VRAM list has download sizes and pull commands for all of these.
How Fast Is It?
RunAIHome measured these on an RTX 5060 Ti 16GB with Ollama 0.23.2 on Windows 11:
| Model (Q4_K_M) | VRAM used | Tokens/s |
|---|---|---|
| Mistral 7B | 5.9 GB | 90.2 |
| DeepSeek-Coder 6.7B | 11.6 GB (16K context) | 101.4 |
| Llama 2 13B | 11.3 GB | 53.4 |
Those are older models, but the rule carries over: speed depends on file size. Here’s what to expect from current models, worked out from 448 GB/s.
| Model | Approx. file | Expected speed (estimate) |
|---|---|---|
| MiMo-V2.6 9B | ~7 GB | 45-60 tok/s |
| Gemma 4 12B | ~8 GB | 40-50 tok/s |
| gpt-oss-20b-class at Q4 | ~12-13 GB | 25-35 tok/s |
| Qwen3.8 27B at Q3, all on GPU | ~13-14 GB | about 20-28 tok/s |
| Qwen3.6 35B-A3B MoE | ~15-20 GB | fast, often 40+ tok/s even with light offload |
Note: The second table is my estimate from bandwidth and file size, not a test I ran. Prompt processing on the 5060 Ti is much faster than generation thanks to its Tensor cores.
To watch what your card is doing while it runs, use my GPU and VRAM monitoring guide.
Image and Video Generation in ComfyUI
For image models, 16 GB is the comfortable minimum in 2026. The 5060 Ti has fewer cores than a 5070 Ti, so expect each image to take roughly twice as long.
- Image models (Qwen Image 2.1, Flux-class, SDXL): use FP8 or GGUF Q8/Q6 versions. Offload the text encoder to RAM if VRAM is tight.
- Video models (Wan 2.2, LTX 2.5): use GGUF Q4-Q5 and short clips. My LTX 2.5 on 16 GB guide has a working setup.
- LoRA training: image LoRAs are fine. Video LoRAs are slow and memory-hungry.
Tip: RTX 50 cards need a recent PyTorch build with CUDA 12.8 or newer. Follow my ComfyUI on RTX 50 series setup or you’ll get “no kernel image” errors.
Setup: Drivers, Ollama and llama.cpp
- Install the latest NVIDIA driver. Use the Studio or Game Ready driver from nvidia.com or the NVIDIA app. Blackwell needs a current branch.
- Check the card. Run
nvidia-smiin a terminal. You should see the 5060 Ti with about 16,300 MiB total. - Install Ollama. Follow my Ollama install guide. It finds the GPU automatically.
- Pull a model. Start with
ollama pull gemma4:12b, thenollama run gemma4:12b. - Check the split. Run
ollama ps. You want “100% GPU”. If it shows a CPU share, the model is too big for your settings. - Optional: llama.cpp. For more control, use a CUDA build of llama.cpp. Newer releases use
llama serveandllama cli.
Best Settings for 16 GB
| Setting | Recommended | Why |
|---|---|---|
| Quant | Q4_K_M for 14B-20B, Q3 for 27B | Best quality that still fits |
| Context | 8K-32K | Huge contexts eat VRAM fast |
| KV cache | q8_0 (OLLAMA_KV_CACHE_TYPE=q8_0) |
Roughly halves context memory |
| Flash attention | On (OLLAMA_FLASH_ATTENTION=1) |
Needed for KV quantisation, saves memory |
| MoE offload (llama.cpp) | Expert layers on CPU, rest on GPU | Lets 35B MoE models run fast |
| Power limit | Default 180 W | Already efficient; undervolting is optional |
Tip: Close browsers and games while running models. Windows and Chrome can quietly hold 1-2 GB of VRAM.
5060 Ti vs 5070 Ti vs Used 3090 vs R9700
| Card | Best for | Main downside |
|---|---|---|
| RTX 5060 Ti 16GB (~$770) | First AI PC, 7B-20B LLMs, image models | Half the bandwidth of the others |
| RTX 5070 Ti (~$1,400) | Same models about 2x faster, quicker images | Same 16 GB, nearly double the price |
| Used RTX 3090 (~$1,450) | 24 GB for 27B-32B models, high bandwidth | Old, 350 W, no warranty, no FP8/FP4 |
| Radeon AI PRO R9700 (~$1,700-$1,850) | 32 GB for big LLMs | No CUDA, fewer ComfyUI nodes |
Read more in my best GPU for local LLMs and used RTX 3090 buying guide.
Two 5060 Ti Cards?
Two 16 GB cards give you 32 GB for LLMs for roughly $1,540. Ollama, LM Studio and llama.cpp split layers across both automatically.
- Good for: 27B-32B models at Q4-Q5 with long contexts, and 40B MoE models fully on GPU.
- Not good for: image and video models, which mostly need to fit on one card. You can put the text encoder on the second GPU.
- Check your board: you need two slots with enough spacing and a 650-750 W PSU.
My ComfyUI multi-GPU guide shows how to use a second card for image work.
Troubleshooting
CUDA out of memory
Lower the context, use a smaller quant, or turn on KV cache quantisation. My CUDA out of memory fix guide covers PyTorch, ComfyUI and Ollama step by step.
Ollama runs on the CPU
- Update the driver and restart Ollama.
- Check
ollama ps: a CPU share means the model plus context doesn’t fit. - Laptops: make sure the app runs on the NVIDIA GPU, not the integrated one.
“No kernel image is available” in ComfyUI
Your PyTorch build is too old for Blackwell. Install a PyTorch build for CUDA 12.8 or newer.
Slower than expected
- PCIe 3.0 boards slow down model loading and any offloading, because the card only uses 8 lanes.
- Offloaded dense models slow down sharply. Pick a model that fits, or a MoE model.
Frequently Asked Questions
Is the RTX 5060 Ti 16GB good for AI?
Yes. It’s the cheapest current NVIDIA card with 16 GB, which runs 7B-20B LLMs fully on the GPU and most image models in FP8 or GGUF.
Can the RTX 5060 Ti run a 27B model?
Yes, with a Q3 quant or some layers in system RAM. Expect it to be slower than on a 24 GB card, and keep the context at 16K-32K.
Is the 5060 Ti 8GB OK for AI?
Only for small models. 8 GB limits you to about 8B-9B LLMs and makes image and video generation much harder. Get the 16 GB version.
RTX 5060 Ti 16GB or used RTX 3090 for AI?
The 3090 has 24 GB and twice the bandwidth, so it’s better for bigger LLMs. The 5060 Ti is new, cheaper, uses half the power and supports FP8/FP4.
How many tokens per second does the RTX 5060 Ti get?
RunAIHome measured about 90 tokens/s on Mistral 7B and 53 tokens/s on a 13B model at Q4_K_M. Larger current models run proportionally slower.
Is the RTX 5060 Ti good for Stable Diffusion and ComfyUI?
Yes for images with FP8 or GGUF models. Video models work with GGUF quants and short clips, but slowly.
What power supply do I need for an RTX 5060 Ti?
A quality 450 W unit is enough for one card. Use 650-750 W if you add a second card.
Final Thoughts
The RTX 5060 Ti 16GB isn’t fast, but it’s the most affordable way to get 16 GB on NVIDIA, with full CUDA support. That makes it the right first card for most people learning local AI.
Pick models that fit, lean on MoE models, and keep the context sensible. If you outgrow it, rent a bigger GPU for heavy jobs with my cheapest cloud GPU guide.