Sunk Cost sunkcost.ai Data checked 2026-09-03

Can an NVIDIA GeForce RTX 3060, 12GB run local LLMs?

Yes — 13 of the 49 current models on this site fit in its 11 GB of usable memory, and one more if you keep the window shorter than 32k. The strongest it holds, Gemma 4 12B, runs at 38 tok/s with a 32k window, worked out from memory bandwidth. Whether that saves you money is a different question: at its $329 launch price and 500k tokens a day, it pays back in 26 years. Its price here is the card alone, so every figure below leaves out the PC you need to put it in. Every card on this site is set against the others here.

Price$329 at launch — discontinuedcard only
Memory12 GB, about 11 GB of it addressable by the GPU at 360 GB/s
Best model it runsGemma 4 12B — Below every hosted tier, 38 tok/s estimated
Pay-back against the APIPays back in 26 years at 500k tokens a day

Run the numbers on this machine

What the GeForce RTX 3060, 12GB runs

They do not all hold the same window. The weights are a fixed size, but the key-value cache grows with every token you keep, so what is left of 11 GB after the weights is how far the context goes. On 7 of the 12 below, this machine's memory is what runs out first: Granite 4.2 8B stops soonest, at 32k. The rest reach 256k, where the calculator's list ends, or the longest setting their own context limit allows.

ModelSpeedClassMemoryLongest context
Gemma 4 12BQ4_K_M 38 tok/s estimated Below every hosted tier 8.0 GB 128kmemory
Qwen3.5 9BQ4_K_M 45 tok/s estimated Below every hosted tier 6.8 GB 128kmemory
MiniCPM5 2BQ4_K_M 103 tok/s estimated Below every hosted tier 3.0 GB 128k
Qwen3.5 4BQ4_K_M 80 tok/s estimated Below every hosted tier 3.8 GB 128kmemory
Ling 3.0 tinyQ4_K_M 106 tok/s estimated Below every hosted tier 5.0 GB 128k
Granite 4.2 8BQ4_K_M 29 tok/s estimated Below every hosted tier 11 GB 32kmemory
Gemma 4 E4BQAT Q4_0 31 tok/s estimated Below every hosted tier 5.7 GB 128k
LFM2.5 2.6BQ4_K_M 139 tok/s estimated Below every hosted tier 2.2 GB 128k
Ministral 3 8BQ4_K_M 31 tok/s estimated Below every hosted tier 9.8 GB 32kmemory
LFM3 1.7BQ4_K_M 203 tok/s estimated not yet placed 1.5 GB 256k
Ornith 1.5 9BQ4_K_M 45 tok/s estimated not yet placed 6.9 GB 128kmemory
Spark-X2.5 4BQ4_K_M 79 tok/s estimated not yet placed 3.9 GB 128kmemory

Speed and memory are at 32k context, the setting the calculator starts on; the memory column is the weights plus the cache for that much of it. Each speed says how it was arrived at, and every one here is estimated from this machine's 360 GB/s of memory bandwidth rather than taken from a published benchmark. There is a page on how that sum works, and what each size needs, and one on what 12 GB runs, machine by machine. The longest context is the longest setting the calculator offers that this machine still holds the model at, cache included, and each one opens the calculator on that model at that length. A figure tagged memory is one this machine ran out of room for, and the rest are stopped by the model's own limit or by the end of the list.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. 5 of the 12 above reach it, and the slowest is 29 tok/s.

One more fits, and the intelligence index has not scored it, so the table above cannot place it: Spark-X2.6 4B. Its page shows what it needs and what runs it.

The 49 counted above are the current models. This site prices 16 superseded ones as well, because people still run them, and this machine holds three of those at 32k, strongest first: Llama 3.1 8B Instruct at Q4_K_M, Qwen3 8B, Gemma 3 12B it. Each of those pages asks whether the model is still worth running.

What has been measured on the GeForce RTX 3060, 12GB

Not one of the speeds above was measured on this machine, and that is not because nobody has run a model on one. This site holds two published speeds taken on this machine, both on superseded models the table above leaves out. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

ModelAt 32kAs published
Qwen3 8BQ4_K_Msuperseded 32 tok/s measured 55 tok/smeasured at 4k
Qwen3 14BQ4_K_Msuperseded 29 tok/s measuredat 8k 31 tok/smeasured at 4k

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. Where this machine does not hold a model at 32k, the row gives the longest window it does hold. The figures come from hardware-corner.net.

One more, at a shorter window

The table above counts what fits at 32k, the context the calculator starts on. One more model fits this machine at a shorter one. Same weights, smaller cache.

ModelLongest window it fitsNeeds thereNeeds at 32k
Ministral 3 14BQ4_K_M 16k 11 GB 14 GB

Each window is the longest setting the calculator offers that this machine still holds the model at, and it opens the calculator on that model at that length. The memory figures are the weights plus the key-value cache at that window, against the 11 GB this machine's GPU can use.

Other machines to weigh against the GeForce RTX 3060, 12GB

MachinePriceMemoryModels that fitPay-back
Other NVIDIA cards
NVIDIA GeForce RTX 5090, 32GB $1,999card only 32 GB 33 Pays back in 123 years
NVIDIA RTX PRO 6000 Blackwell, 96GB $18,000card only 96 GB 40 Pays back in 1,071 years
NVIDIA GeForce RTX 4080, 16GBdiscontinued $1,199card only 16 GB 15 Pays back in 93 years
NVIDIA GeForce RTX 3090, 24GBdiscontinued $1,499card only 24 GB 28 Pays back in 94 years
NVIDIA GeForce RTX 4090, 24GBdiscontinued $1,599card only 24 GB 28 Pays back in 104 years
Nearest in price elsewhere on the list
Mac mini M6, 16GB $899 16 GB 12 Pays back in 71 years
Strix Halo Framework Desktop, 32GB $1,269 32 GB 29 Pays back in 91 years
MacBook Air M5 (13-inch), 16GB $1,299 16 GB 12 Pays back in 102 years
AMD Radeon AI PRO R9700, 32GB $1,299card only 32 GB 33 Pays back in 84 years
MacBook Pro M5 (14-inch), 16GB $1,999 16 GB 12 Pays back in 157 years

Every row uses the same defaults as the figures above: 500k tokens a day at 15:1 input to output, 32k context, and each machine's strongest model that fits, counted against the same 49 current models. Graphics cards are priced as the card alone, so add the PC around one before comparing it with a complete computer.

Head to head with another card: RTX PRO 6000 Blackwell, 96GB · GeForce RTX 5090, 32GB · GeForce RTX 4090, 24GB · GeForce RTX 3090, 24GB · Radeon AI PRO R9700, 32GB · GeForce RTX 4080, 16GB. There are 111 machine match-ups on the site.

The specifics

Chip
GeForce RTX 3060 — Ampere · 12 GB GDDR6
Memory bandwidth
360 GB/s — 15 Gbps × 192-bit bus ÷ 8 = 360 GB/s (15 Gbps from TechPowerUp, 192-bit from NVIDIA).
Usable by the GPU
11 GB — Usable memory is the VRAM less 1 GB: the margin llama.cpp's automatic fitting (--fit) leaves free on each device by default (common/common.h, fit_params_target = 1 GiB). Treated as nominal GB, like the other machines here.
Power under load
170 W (published) — 170 W is the maker's rated board power for the card alone. The rest of the PC adds more, and LLM decoding usually draws less than the full rating. A whole Intel 265K + RTX 3060 PC measured 214-229 W at peak during llama.cpp runs, prompt processing included (Jeff Geerling).
Availability
Launch price. Two generations old; usually a used or clearance purchase.
Sources
NVIDIA, NVIDIA (news), TechPowerUp, geerlingguy/ai-benchmarks, ggml-org/llama.cpp