RTX 4060 Ti
An RTX 4060 Ti runs 12 of the 34 open models tracked here at Q4_K_M with 8k context, computed on 16 October 2026. The largest is gpt-oss 20B, needing 13.4 GB and generating about 97 tokens per second.
- Memory
- 16 GB VRAM
- Bandwidth
- 288 GB/s
- Board power
- 165 W
- Released
- 2023
- Availability
- Sold new
Open the RTX 4060 Ti in the LLM checker
What an RTX 4060 Ti runs, model by model
RTX 4060 Ti 16 GB addresses 16 GB at 288 GB/s. Every model below is at Q4_K_M with 8k of context, the setting most people actually use. Once the 5% safety margin comes off, the card has 15.2 GB to spend: memory decides what loads, bandwidth how fast it answers. Both are estimates from stated formulas, and the VRAM calculator shows the working.
On this card a model fits at or under 15.2 GB, and above 13.6 GB it is tight: it loads, but there is little left for the conversation, and a desktop on the same 16 GB can push it over. Tokens per second are single-stream estimates from memory bandwidth; ≤ marks a ceiling real runtimes fall well below.
Computed 16 October 2026The model to actually run on it
Gemma 4 12B is the best use of this card: 8.2 GB of the 16 GB available, an estimated 29 tokens per second, comfortable for chat, and 131,072 tokens of context still available.
How this is chosen, since no benchmark is quoted: models are compared by size, a mixture-of-experts model counting at the geometric mean of its total and active parameters (a rule of thumb, not a measurement). The pick is the biggest class that fits with memory to spare and answers at 25 tokens per second or better, and within that class the most recent general-purpose release; coding and reasoning specialists are listed above but not recommended by default.
gpt-oss 20B is the largest model the card will hold, at 13.4 GB, with room for 81,920 tokens of context. It is a mixture-of-experts model reading only 3.6B parameters per token, so it is the faster of the two at an estimated 97 tokens per second; what it gives up is the depth of a dense model that reads all of its weights for every token.
It also fits at Q8_0, in about 14.2 GB, which is worth taking whenever the memory allows: Q8 is near-lossless where Q4 costs a little accuracy.
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 |
|---|---|---|
| Gemma 4 12B | 131,072 tokens | 131,072 tokens |
| Qwen3 14B | 34,816 tokens | 40,960 tokens · model limit |
| Phi-4 14B | 16,384 tokens · model limit | 16,384 tokens · model limit |
| Ministral 3 14B | 37,888 tokens | 75,776 tokens |
Quantising the KV cache to Q8 roughly doubles what you can hold, at a quality cost most people never notice: Ministral 3 14B on this card goes from 37,888 to 75,776 tokens. Capped at 128k tokens, or at the model's own native window where that is smaller, marked "model limit": free memory beyond that point buys nothing. Rows that hold far more context than their size suggests are hybrid, sliding-window or latent-attention models, which cache only a fraction of what a standard transformer does; each model page shows the working.
Where this card stops
The first model out of reach is Devstral Small 2 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.
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, which 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.
The honest take
More memory than its neighbours, and less bandwidth than a card two years older.
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.
Compared with the alternatives
- RTX 3090: 24 GB · 936 GB/s · best fit Muse Glimmer 30B at ~38 tok/s. The value benchmark for local models, and it has been for years.
- RTX 4070: 12 GB · 504 GB/s · best fit Gemma 4 12B at ~51 tok/s. Fast for its class, and capped by 12 GB.
- RTX 3060: 12 GB · 360 GB/s · best fit Gemma 4 12B at ~36 tok/s. The cheapest card that still makes local models worth doing.
All 39 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?
- Gemma 4 12B. At Q4_K_M and 8k context it needs about 8.2 GB of the 16 GB available and generates an estimated 29 tokens per second, which is comfortable for chat. It also fits at Q8, at about 14.2 GB, which is worth taking when it fits.
- 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. Treat 43 as a ceiling on 288 GB/s rather than a measurement; the higher a figure, the further real runtimes fall below it.
- Is 16 GB enough for running LLMs locally?
- It runs 12 of the 34 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: Gemma 4 12B on this card holds about 131,072 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 28 of these models to this card's 12. 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 for a standard transformer; sliding-window, hybrid and latent-attention models counted as they actually cache) + 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. A ceiling, not a measurement: the 1 of 12 figures marked ≤ are where real runtimes fall furthest below the number, because so few weights are read per token. See the tokens-per-second estimator.
- Card specification (manufacturer figures): 16 GB, 288 GB/s, 165 W; see NVIDIA's own page for this card. The bandwidth figure: NVIDIA prints no figure for this card, so it is 128-bit × 18 Gbps ÷ 8, with the bus width from NVIDIA's page and the data rate from a 16 GB card's listing in NVIDIA's own store.
- Model architecture values come from each model's published config; the self-hosted LLM guide explains what each one changes.