The RTX 4060 Ti 16GB used to be the default answer to “cheapest NVIDIA card with 16 GB for AI”. It is still a capable card: 16 GB fits 14B models with long context, gpt-oss-20b runs fully on the GPU, and Ada Lovelace has FP8 support that older cards lack.
The problem in November 2026 is price. New stock has nearly vanished, and the cheapest listings now cost more than a brand-new RTX 5060 Ti 16GB. This guide covers what the card runs, real benchmark numbers, current prices, the best settings, and when it is still worth buying.
- Runs well: gpt-oss-20b fully in VRAM at about 63 t/s, Qwen3 14B at about 27 t/s, SDXL and FLUX-class image models.
- Weak spot: only 288 GB/s of bandwidth, so text generation is slower than an older RTX 3060 12GB on models that fit both cards.
- Price (9 Nov 2026): used from about $690 on eBay, new about $950. A new RTX 5060 Ti 16GB costs about $770.
- Verdict: great if you already own one or find one used for about $450 or less. At today’s typical prices, buy the RTX 5060 Ti 16GB instead.
RTX 4060 Ti 16GB Specs for AI
| Spec | RTX 4060 Ti 16GB | Why it matters |
|---|---|---|
| VRAM | 16 GB GDDR6 | Fits 14B models with long context and gpt-oss-20b |
| Memory bus | 128-bit | Narrow, which limits bandwidth |
| Bandwidth | 288 GB/s | The main limit on text generation speed |
| CUDA cores | 4,352 | Good prompt and image speed for the class |
| Tensor Cores | 4th gen (Ada Lovelace) | FP16, BF16 and FP8; no FP4 |
| Power | 165 W | Runs on a modest PSU |
| Launch | July 2023, $499 |
There is also an 8 GB RTX 4060 Ti. It looks the same in listings, so always check the memory size. For AI the 8 GB version is a poor buy.
Price in November 2026
These prices come from GPUDojo’s tracker of eBay, Amazon and Newegg (updated 9 November 2026).
| Where | Price |
|---|---|
| Cheapest used, eBay US | $689.99 (plus shipping) |
| New, Amazon | $949.99 |
| Last Newegg offer (2 Oct) | $875.03 |
| Used low point since August | $450 (4 Sep) |
| Launch MSRP (2023) | $499 |
Used prices have climbed from the mid-$500s in September to around $700 in November. New cards have become scarce, and the last ones are priced well above launch.
Warning: at $690 used or $950 new, the 4060 Ti 16GB costs more than a new RTX 5060 Ti 16GB (about $770), which is faster and has a full warranty. Don’t pay those prices.
The wider reasons for these prices are in my guide to why GPU prices are so high in 2026.
LLM Speed: Real Tokens per Second
I haven’t benchmarked this card myself. These numbers are from Hardware Corner’s March 2026 tests and the llama.cpp CUDA scoreboard.
| Model | Quant | Context | Prompt (t/s) | Generation (t/s) |
|---|---|---|---|---|
| gpt-oss-20b | MXFP4 | 4K | 3,274 | 63.2 |
| gpt-oss-20b | MXFP4 | 32K | 1,965 | 51.5 |
| gpt-oss-20b | MXFP4 | 128K | 780 | 31.1 |
| Qwen3 8B | Q4_K | 4K | 2,675 | 45.8 |
| Qwen3 8B | Q4_K | 32K | 760 | 25.5 |
| Qwen3 14B | Q4_K | 4K | 1,646 | 27.4 |
| Qwen3 14B | Q4_K | 16K | 918 | 22.4 |
| Qwen3 14B | Q4_K | 32K | 541 | 17.9 |
gpt-oss-20b is the standout. It fits entirely in 16 GB and stays above 30 t/s even at 128K context, which a 12 GB card can’t do without offloading.
On the llama.cpp scoreboard’s Llama 2 7B test, the 4060 Ti generates about 64 t/s against about 77 t/s on an RTX 3060 and 93 t/s on an RTX 5060 Ti 16GB. (That scoreboard row is the 8 GB 4060 Ti, which has the same chip and bandwidth.)
Which LLMs Run on 16 GB
| Model | Quant | Approx. size | On a 4060 Ti 16GB |
|---|---|---|---|
| Qwen3 8B / Llama 3.1 8B | Q8_0 | ~9 GB | Fully in VRAM with long context |
| Gemma 4 12B | Q4_K_M to Q6_K | ~7-10 GB | Fully in VRAM |
| Qwen3 14B | Q4_K_M | ~9 GB | Fully in VRAM, 32K context fits |
| gpt-oss-20b | MXFP4 | ~12-13 GB | Fully in VRAM, fast |
| Gemma 4 26B A4B (MoE) | Q4 | ~16 GB | Small offload; stays quick |
| Qwen3.8-27B (dense) | Q3_K_M / IQ4 | 13-15 GB | Fits with short context; slow-ish |
| 32B dense | Q4 | ~19 GB | Needs offload; slow |
| 70B dense | Q4 | ~43 GB | Not practical |
For the 27B class, my Qwen3.8-27B on a 16 GB GPU guide shows the exact quants and settings that fit. For model picks by size, see best Ollama models by VRAM.
Image and Video Generation
| Workload | On a 4060 Ti 16GB |
|---|---|
| SDXL, 1024px | Runs comfortably, with room for LoRAs and ControlNet |
| FLUX-class and Qwen-Image | FP8 or GGUF Q5-Q8 fit; offload the text encoder if needed |
| LoRA training (SDXL) | Works with standard memory-saving settings |
| Video (Wan 2.2 and similar) | GGUF versions at modest resolution; slow but usable |
The extra 4 GB over a 12 GB card is what lets you use higher-quality quants and keep LoRAs loaded. Ada’s FP8 support also helps with FP8 checkpoints, which RTX 30 cards have to convert.
My Qwen-Image 2.1 GGUF guide lists which files to pick for 12-16 GB.
4060 Ti 16GB vs 5060 Ti 16GB vs 3060 12GB vs 3090
| Card | VRAM | Bandwidth | Qwen3 14B Q4 (4K) | Rough price, Nov 2026 |
|---|---|---|---|---|
| RTX 3060 12GB (used) | 12 GB | 360 GB/s | 31.2 t/s | $345-$450 |
| RTX 4060 Ti 16GB (used) | 16 GB | 288 GB/s | 27.4 t/s | From about $690 |
| RTX 5060 Ti 16GB (new) | 16 GB | 448 GB/s | Faster (93 vs 64 t/s on 7B) | About $770 |
| RTX 3090 24GB (used) | 24 GB | 936 GB/s | Much faster | About $1,450 |
The comparison is simple right now. If 12 GB is enough, a used RTX 3060 12GB is cheaper and generates text at least as fast. If you need 16 GB, the new RTX 5060 Ti 16GB is faster, adds FP4, and costs about the same as a used 4060 Ti.
For 27B-32B models at good speed, the used RTX 3090 is still the card to beat.
Best Settings for Ollama, LM Studio and ComfyUI
Ollama
- Update the NVIDIA driver and Ollama (my Ollama install guide covers every OS).
- Start with a model that fits:
ollama run gpt-oss:20borollama run qwen3:14b. - Check placement with
ollama ps. It should say 100% GPU. - Raise context carefully:
OLLAMA_CONTEXT_LENGTH=32768works for 14B at Q4. Go lower if layers move to the CPU. - Save memory with
OLLAMA_FLASH_ATTENTION=1andOLLAMA_KV_CACHE_TYPE=q8_0.
LM Studio and llama.cpp
- Offload all layers for models under about 14 GB.
- For MoE models slightly over 16 GB, use llama.cpp’s
--n-cpu-moeto keep only some expert layers on the CPU. - Turn on flash attention and a Q8 KV cache for long documents.
ComfyUI
- Prefer FP8 checkpoints where available; Ada handles them natively.
- Use GGUF Q5-Q8 for big image models and the ComfyUI-GGUF loader.
- If you run out of memory, offload the text encoder or start with
--lowvram.
Tip: because the 128-bit bus is the bottleneck, a small memory overclock in MSI Afterburner can add a few percent to token speed. Test for stability with a long run before you rely on it.
Buying a Used RTX 4060 Ti 16GB
- Confirm 16 GB. Ask for a GPU-Z screenshot showing 16384 MB. The 8 GB model is far more common.
- Compare with a new 5060 Ti 16GB on the same day. If the gap is under $100, buy new.
- Check completed sales, not asking prices.
- Test on arrival: load a model that fills about 15 GB and run it for 15-20 minutes while watching temperatures with
nvidia-smi. - Keep buyer protection until testing is done.
My guide to checking GPU VRAM and usage shows how to confirm memory size and load on Windows and Linux.
Is It Worth It in 2026?
| Your situation | Verdict |
|---|---|
| Already own one | Keep it; it’s a solid 16 GB AI card |
| Found one used for about $450 or less | Good buy |
| Typical used price ($600-$750) | Buy a new RTX 5060 Ti 16GB instead |
| New at $850-$950 | No |
| Your models fit in 12 GB | A used RTX 3060 12GB is cheaper |
| You want 27B-32B models fast | Used RTX 3090 |
For every budget, my best GPU for local LLMs guide ranks the current options.
Troubleshooting
Text generation feels slower than expected
That’s the 288 GB/s bandwidth. Use MoE models like gpt-oss-20b, which read fewer parameters per token, or a smaller quant.
Ollama shows part of the model on the CPU
The model plus context is over 16 GB. Lower the context length, use a Q8 KV cache, or pick a smaller quant until ollama ps shows 100% GPU.
The card shows only 8 GB
You have the 8 GB model. Check the exact name in GPU-Z or with nvidia-smi.
Out of memory in ComfyUI with FLUX-class models
Switch to an FP8 or GGUF version, offload the text encoder, reduce batch size, or start ComfyUI with --lowvram.
An NVFP4 model won’t run
FP4 hardware needs an RTX 50 card. Use an FP8, GGUF or MXFP4 version instead.
Frequently Asked Questions
Is the RTX 4060 Ti 16GB good for AI?
Yes. It runs gpt-oss-20b fully in VRAM at about 63 tokens per second and 14B models at about 27 tokens per second, plus SDXL and FLUX-class image models. Its weak point is memory bandwidth.
How much is an RTX 4060 Ti 16GB in November 2026?
On 9 November 2026 the cheapest used card on eBay US was about $690 and new cards were about $950 on Amazon. That is more than a new RTX 5060 Ti 16GB.
RTX 4060 Ti 16GB or RTX 5060 Ti 16GB for AI?
The RTX 5060 Ti 16GB. It has 448 GB/s of bandwidth instead of 288, FP4 support and a warranty, and it currently costs about the same as a used 4060 Ti.
RTX 4060 Ti 16GB or RTX 3060 12GB?
The 4060 Ti if you need 16 GB for gpt-oss-20b, long context or bigger image models. If your models fit in 12 GB, the cheaper 3060 generates text at least as fast.
Can the RTX 4060 Ti 16GB run 27B models?
Only at Q3 or IQ4 quants with short context, and not quickly. For 27B-32B models a 24 GB card such as a used RTX 3090 works much better.
Does the RTX 4060 Ti support FP8?
Yes. Ada Lovelace Tensor Cores support FP8, but not FP4, which needs an RTX 50 card.
Is the 8 GB RTX 4060 Ti OK for AI?
Not really. 8 GB limits you to small models and struggles with modern image models. Get 12 GB or more.
Final Thoughts
The RTX 4060 Ti 16GB is a good AI card held back by a narrow memory bus and, now, by silly prices. If you have one, enjoy it: gpt-oss-20b and 14B models run well, and image work is comfortable.
If you’re buying in late 2026, compare it with a new RTX 5060 Ti 16GB on the day. Unless the used 4060 Ti is far cheaper, the newer card wins. My VRAM requirements guide will help you decide whether 12, 16 or 24 GB is right for you.
