RTX 4070

Gemma 4 12B is the best use of this card: 8.2 GB of the 12 GB available, an estimated 51 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.

Qwen3 14B is the largest model the card will hold, at 10.8 GB, but it is the wrong daily driver: filling 90% of memory leaves room for only 11,264 tokens of conversation, and it generates at 41 tokens per second against 51. More parameters are not worth a context window that runs out mid-document.

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.

Quantising the KV cache to Q8 roughly doubles what you can hold, at a quality cost most people never notice: Qwen3 14B on this card goes from 11,264 to 23,552 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.

The first model out of reach is gpt-oss 20B: about 13.4 GB at Q4_K_M and 8k context, against 12 GB of usable memory. Dropping to Q3_K_M would need about 11 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.

A reasonable buy if you already want the card for games or image work and language models are a bonus. A poor buy if models are the reason: the money is better spent on memory.

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 200 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.

• 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 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 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.