16 GB VRAM 288 GB/s 165 W 2023 Sold new
RTX 4060 Ti 16 GB addresses 16 GB at 288 GB/s. Of the 16 open models tracked on this site, it runs 7 at Q4_K_M with 8k of context. The largest is gpt-oss 20B, needing about 13.6 GB and generating an estimated 97 tokens per second — faster than you can read.
More memory than its neighbours, and less bandwidth than a card two years older.
What an RTX 4060 Ti runs, model by model
Every model at Q4_K_M with 8k of context, the setting most people actually use. This card has 15.2 GB to spend once the 5% safety margin comes off its 16 GB, and it reads that memory at 288 GB/s. Those two numbers decide the last two columns: the first says what loads, the second says how fast it answers. Both are estimates from stated formulas, and the VRAM calculator shows the working.
| Model | Size | Needs | Est. tokens/s | On this card |
|---|---|---|---|---|
| Llama 3.2 3B | 3.2B | 3.4 GB | 109 | fits |
| Llama 3.1 8B | 8B | 6.4 GB | 43 | fits |
| Qwen3 8B | 8.2B | 6.7 GB | 42 | fits |
| Gemma 3 12B | 12.2B | 11.1 GB | 28 | fits |
| Qwen3 14B | 14.8B | 10.8 GB | 23 | fits |
| Phi-4 14B | 14.7B | 11 GB | 24 | fits |
| gpt-oss 20B | 21B · 3.6B active | 13.6 GB | 97 | fits |
| Mistral Small 3.1 24B | 24B | 16.3 GB | – | no |
| Gemma 3 27B | 27.4B | 21.2 GB | – | no |
| Qwen3 30B-A3B (MoE) | 30.5B · 3.3B active | 19.7 GB | – | no |
| Qwen3 32B | 32.8B | 22.4 GB | – | no |
| Qwen2.5 Coder 32B | 32.8B | 22.4 GB | – | no |
| DeepSeek-R1 Distill Qwen 32B | 32.8B | 22.4 GB | – | no |
| Llama 3.3 70B | 70.6B | 45.8 GB | – | no |
| DeepSeek-R1 Distill Llama 70B | 70.6B | 45.8 GB | – | no |
| gpt-oss 120B | 117B · 5.1B active | 71.7 GB | – | no |
On this card that means anything at or under 15.2 GB counts as fitting, and anything above 13.6 GB is marked tight: it loads, but there is little left for the conversation, and a desktop on the same 16 GB can push it over.
The model to actually run on it
gpt-oss 20B is the best use of this card: 13.6 GB of the 16 GB available, an estimated 97 tokens per second, faster than you can read, and 40,960 tokens of context still available. It is a mixture-of-experts model, so all 21B parameters sit in memory but only 3.6B are read per token — which is why it is quick for its size.
If quality matters more than parameter count, Qwen3 8B fits at Q8_0 in about 10.7 GB. Q8 is near-lossless; a smaller model at Q8 often beats a larger one squeezed into Q3.
How much context actually fits
Model size is the question people ask; context is the one that bites. The KV cache grows linearly with the conversation, so a model that loads comfortably can still run out of memory halfway through a long document. On 16 GB, the context you get is whatever the weights leave behind — which is why two cards that run the same model can feel completely different in use. These are the largest contexts this card holds, at Q4_K_M weights.
| Model | Max context, FP16 KV | With Q8 KV cache |
|---|---|---|
| gpt-oss 20B | 40,960 tokens | 81,920 tokens |
| Qwen3 14B | 34,816 tokens | 69,632 tokens |
| Phi-4 14B | 27,648 tokens | 56,320 tokens |
| Gemma 3 12B | 18,432 tokens | 36,864 tokens |
Quantising the KV cache to Q8 roughly doubles what you can hold — gpt-oss 20B on this card goes from 40,960 to 81,920 tokens — at a quality cost most people never notice. Capped at 128k tokens here; a model's own architecture may stop lower, and sliding-window models such as Gemma 3 use less KV than the formula assumes.
Where this card stops
The first model out of reach is Mistral Small 3.1 24B: about 16.3 GB at Q4_K_M and 8k context, against 16 GB of usable memory. Dropping to Q3_K_M would need about 13.6 GB, which fits, though Q3 loses enough quality that a smaller model at Q4 is usually the better trade. Offloading the remainder to system RAM works and is ten to fifty times slower; it is a way to see a model run, not a way to use one.
The honest take
The 16 GB 4060 Ti is the clearest example on this page of why VRAM alone is a bad way to choose a GPU. It holds larger models than a 3060 or a 4070, and then generates more slowly than either, because 288 GB/s is the lowest bandwidth of any card here. It makes sense if you specifically need 16 GB in a small, quiet, 165 W machine and you are patient. It makes no sense as a general upgrade.
What it is good at
- 16 GB fits a 14B model at Q4 with real context, which 12 GB cards cannot do comfortably.
- Low power and low noise; fits small-form-factor builds without a PSU upgrade.
- Ada-generation encoders and drivers, so it is a better all-round desktop card than a used 3060.
What it is not
- 288 GB/s is the binding constraint. Every model runs slower on this card than on a 3060 with the same weights loaded.
- The extra memory tempts you into larger models, which is exactly where the low bandwidth hurts most.
- Poor value per token generated compared with a used 24 GB card.
The thing people get wrong: Its 288 GB/s is lower than the RTX 3060's 360 GB/s. A cheaper, older, smaller-memory card generates text faster. If speed matters more than capacity to you, the "upgrade" is a downgrade.
Buy or rent
Hard to justify new against a used 3090 with 24 GB and more than three times the bandwidth. If quiet and small matters more than fast, it earns its place; otherwise look at the tier above.
Retail prices drift, and a figure written today would be wrong within a month, so this page does not carry one. Put the figure you are actually looking at, with your electricity rate and your real monthly hours, into the build vs rent calculator — it already knows this card draws 165 W under load. It returns the month owning becomes cheaper, or tells you it never does. Hours per month decides it far more often than the price does.
Nodegrove is the rented side of that comparison: a workspace that stays saved, with a GPU attached only while you are using it. It is the right answer when your hours are low or your needs change; buying is the right answer when they are high and stable.
Compared with the alternatives
- RTX 3090 — 24 GB · 936 GB/s · best fit Qwen3 30B-A3B (MoE) at ~342 tok/s. The value benchmark for local models, and it has been for years.
- RTX 4070 — 12 GB · 504 GB/s · best fit Qwen3 8B at ~74 tok/s. Fast for its class, and capped by 12 GB.
- RTX 3060 — 12 GB · 360 GB/s · best fit Qwen3 8B at ~53 tok/s. The cheapest card that still makes local models worth doing.
All 13 cards are compared side by side on the GPU index, and the same numbers from the model's point of view are on each model fit table. To check one specific pairing rather than read a table, use the can-I-run-it checker.
Questions
Can an RTX 4060 Ti run a 70B model?
Not at Q4_K_M in one card. Llama 3.3 70B needs about 45.8 GB at 8k context and this card can address 16 GB. Your options are a smaller model, a harsher quantisation with very little context, splitting across two cards, or renting a larger one for the hours you need it.
What is the best model to run on an RTX 4060 Ti?
gpt-oss 20B. At Q4_K_M and 8k context it needs about 13.6 GB of the 16 GB available and generates an estimated 97 tokens per second, which is faster than you can read. If quality matters more than size, Qwen3 8B fits at Q8 in about 10.7 GB.
How many tokens per second does an RTX 4060 Ti generate?
It depends entirely on the size of the model, because generating a token means reading every active weight out of memory. At 288 GB/s and 70% efficiency, this card produces an estimated 43 tokens per second on an 8B model at Q4, and about 97 on the largest model it holds, gpt-oss 20B. Reading speed is roughly 5 to 8 words per second, so anything above 25 feels immediate.
Is 16 GB enough for running LLMs locally?
It runs 7 of the 16 models tracked here at Q4_K_M with 8k of context, up to 21B parameters. The honest test is not the model list but the context: gpt-oss 20B on this card holds about 40,960 tokens before memory runs out.
RTX 4060 Ti or RTX 3090 for local models?
The RTX 3090 holds more: 24 GB against 16 GB, so it runs 13 of these models to this card's 7. On speed, the RTX 3090 is faster: 936 GB/s against 288 GB/s, and bandwidth is what sets chat speed.
Why is the RTX 4060 Ti 16 GB slower than cheaper cards at running LLMs?
Because generating text is limited by memory bandwidth, not by compute or capacity, and this card has the lowest bandwidth here at 288 GB/s. The 12 GB RTX 3060 moves 360 GB/s and the 12 GB RTX 4070 moves 504 GB/s, so both generate faster on any model that fits all three. The 4060 Ti wins only when the model needs more than 12 GB.
Is 16 GB of VRAM enough for local AI?
It is enough for 14B-class models at Q4 with real context, which covers most everyday use. It is not enough for the 32B tier, which is where open models start to feel genuinely capable, and that step needs 24 GB.
- Memory: weights (parameters × bytes per parameter) + KV cache (2 × layers × KV heads × head dim × context × bytes) + 0.5 GB runtime + 4% of weights. Full derivation in the VRAM calculator.
- Speed: 70% × bandwidth ÷ active weight bytes, single stream, no batching, prompt processing excluded. See the tokens-per-second estimator.
- Card specification: 16 GB, 288 GB/s, 165 W — manufacturer figures.
- Model architecture values come from each model's published config; the self-hosted LLM guide explains what each one changes.