What LLMs can an RTX 5060 Ti run?

16 GB VRAM 448 GB/s 180 W 2025 Sold new

RTX 5060 Ti 16 GB addresses 16 GB at 448 GB/s. Of the 29 open models tracked on this site, it runs 11 at Q4_K_M with 8k of context. The largest is gpt-oss 20B, needing about 13.4 GB and generating an estimated 150 tokens per second — faster than you can read.

The newest 16 GB GeForce below the high end, and a clear step up from the 4060 Ti it replaces.

What an RTX 5060 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 448 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.

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

Gemma 4 12B is the best use of this card: 8.2 GB of the 16 GB available, an estimated 45 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 in the table 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 150 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.

Quantising the KV cache to Q8 roughly doubles what you can hold — Ministral 3 14B on this card goes from 37,888 to 75,776 tokens — at a quality cost most people never notice. 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.

The honest take

Sixteen gigabytes at 448 GB/s fixes the 4060 Ti's problem: the memory was right, the bandwidth was not. It holds the same models as any 16 GB card and generates them about half again as fast as the card it replaces. It is a Blackwell card, so it runs the new FP4 formats, and it needs recent builds of the software to be recognised at all.

What it is good at

• 448 GB/s on a 16 GB card: about half again the 4060 Ti's 288 GB/s, at the same memory.
• A 180 W board that fits ordinary cases and power supplies.
• Blackwell generation, so FP4 weight formats run natively.

What it is not

• Sixteen gigabytes is still sixteen: the 24B and 30B classes do not fit at Q4 with a usable context, so 20B-class models are the ceiling.
• Blackwell needs CUDA 12.8 or newer builds of PyTorch and llama.cpp; an old install will not use the card.
• An 8 GB card carries the same name. Check the memory on the box.

The thing people get wrong: There are two RTX 5060 Ti cards, 8 GB and 16 GB, with identical bandwidth. For language models only the 16 GB one is worth buying; the 8 GB version runs out of room above the 8B class.

Buy or rent

A sensible new card if 16 GB covers what you run. If you need 24 GB, a used 3090 holds more and reads it about twice as fast, at several times the power draw. For the occasional larger model, renting by the hour is cheaper than buying up a tier.

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 180 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 4060 Ti — 16 GB · 288 GB/s · best fit Gemma 4 12B at ~29 tok/s. More memory than its neighbours, and less bandwidth than a card two years older.
• RTX 3090 — 24 GB · 936 GB/s · best fit Qwen3.8 27B at ~41 tok/s. The value benchmark for local models, and it has been for years.
• 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 20 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 5060 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 5060 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 45 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 5060 Ti generate?

It depends entirely on the size of the model, because generating a token means reading every active weight out of memory. At 448 GB/s and 70% efficiency, this card produces an estimated 68 tokens per second on an 8B model at Q4, and about 150 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 68 as a ceiling on 448 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 11 of the 29 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 5060 Ti or RTX 4060 Ti for local models?

Both address about the same memory, so they run the same models. On speed, this card is faster: 448 GB/s against 288 GB/s, and bandwidth is what sets chat speed.

RTX 5060 Ti 16 GB or a used RTX 3090 for local LLMs?

The 3090 has 24 GB and reads it at 936 GB/s against 448 GB/s, so it holds larger models and generates about twice as fast on anything both can hold. The 5060 Ti is new, draws 180 W, and runs FP4 formats natively. If capacity and speed matter most, the 3090; if power, warranty and a quiet case matter more, the 5060 Ti.

Is the 8 GB RTX 5060 Ti enough for local LLMs?

It reads memory just as fast as the 16 GB card but holds half as much, which rules out everything above the 8B class at a useful context. For models, buy the 16 GB version.

• 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 3 of 11 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: 16 GB, 448 GB/s, 180 W — manufacturer figures; see NVIDIA's own page for this card. The bandwidth figure: NVIDIA's GeForce comparison page.
• Model architecture values come from each model's published config; the self-hosted LLM guide explains what each one changes.