RTX 3060 budget inference economics: the 12 GB card, measured

The RTX 3060 12 GB is the lowest-cost 12 GB entry into local model inference. This page prices its hours instead of its frame rates. A 3060-class hour rents from 0.015 at the lowest community tier to about 0.04 at the tier real work runs at. At the October 2026 used price of about 300, the card pays for itself in about 10 months of always-on rental. What fits in 12 GB, how fast it runs, and what a month of paid work covers. All of it sits below in dated tables.

All amounts are United States dollars written as bare numerals. Every table is static. Every rate and price carries its party and its date. No calculator script runs in your browser. The card itself, per NVIDIA's RTX 3060 page, carries 12 GB of GDDR6 on a 192-bit bus, with 170 W board power.

Last verified: 2026-10-26

The answer in one line

months to break even  M  =  card price  /  monthly rental
monthly rental        R  =  rate per card-hour  x  730

Work it for the headline row. A used 3060 12 GB ran about 300 in the October 2026 sold reads. The work-tier rate is 0.04 per card-hour, so R is 0.04 x 730, about 29 a month. Divide 300 by 29 and the card equals about 10 months of always-on renting. In hours, 300 divided by 0.04 is 7,500 card-hours. That single number is the page. The 3060 is cheap to buy and cheap to rent. The choice between them is a question of hours, not of rate.

What a 3060-class hour rents for

Buy-advice pages never price the card's hours. Here is the rate picture, every row named and dated, all read 26 October 2026.

The source for the first two rows is Salad's pricing page. The spread is the story. A conventional pod runs a 3060-class hour at 0.06 to 0.12. The community entry sits at 0.015, and the tier our own work runs at bills about 0.04. The host can preempt jobs on the cheap tiers. That fits work which checkpoints and resumes. It does not fit live control loops. A start-run-stop window costs about 1 a day at the work tier.

Compare the 3090 class at the same host. Public tiers run 0.090 lowest to 0.170 high. Our mid-priority runs billed 0.041, nearly the same as the 3060. Both classes cost nearly the same per hour at that tier. The choice between them is memory and supply, not price. That comparison gets its own guide.

What the card costs today

Street prices are reads, not constants. Every row below names its surface and its date.

Three readings fall out. The used 3060 rose through the fall, from about 250 in September reads to about 300 by mid October. Per gigabyte it runs near 25. The faster 3060 Ti, at about 250 used with 8 GB, runs near 31 per gigabyte. The used market now pays for memory, not frame rates. And the 3090's sold price, about 1,093, buys twice the memory for about 3.6 times the money.

The break-even, worked

The table runs the formula from the top of the page on each row. Rental months are always-on at 730 hours.

One sensitivity line, as the law of this page. At the 0.015 entry tier the monthly rental falls to about 11. The used 3060 then stretches from about 10 months to about 27. Cheap hours push the buy further out, not closer.

The duty cycle decides everything. Those 7,500 hours are 10 months only if the card runs around the clock. At two hours a day, the same hours stretch past ten years, and renting wins by a mile. Hobby bursts of one to four hours cost about 1 a day rented. You carry no capital, no idle power bill, and no used-hardware risk.

Here is the observation most buyer's guides miss. Board power is 170 W. United States household power ran about 18 cents per kilowatt-hour in 2026 through August, the year-to-date average, per the EIA Electric Power Monthly. One active hour of card burns about 0.03 of power at the socket. A whole box at the wall runs about 0.045. Renting a 3060-class hour at the work tier costs about the same as the wall power for an hour on a card you own. The rental rate is not a markup on power. It is the price of not owning the idle hours.

What fits in 12 GB

Memory gates everything on this card. The working rule comes from cross-checked fit tables. A 4-bit copy of a dense model takes roughly 0.55 GB per billion parameters. The runtime plus a usable context window wants about 2 GB on top.

The biggest usable row gets two checks. One checked guide derives 10.4 GB for Qwen3 14B at Q4_K_M, about 1.6 GB to spare, as of October 2026. That figure is derived, not measured. A measured table runs the same load at 11.9 GB and calls it tight. So the ceiling of the useful band sits at 10.5 to 12 GB with moderate context. Below it everything runs with room. Above it you are not fitting a model. You are streaming one, and the offload math below takes over.

Two warnings eat real budgets. A desktop session on the same card claims about 4.4 GB. That is the gap between a 14B model resident and a load failure. Run the model headless, or give the display its own card. The context cache also grows with the window. At 16K context an 8B model can spend nearly as much memory on the cache as on the weights.

The robot side of the fit table comes from our own October 2026 runs, measured on real loads.

In short, the 3060 carries the whole small end of robot work and holds the 7B policies with room. Only the pi0.5 class and the big vision reasoner push past it.

Throughput, from named runs

Speed claims for this card vary wildly across the web. The same model class gets quoted from about 20 to about 55 tokens per second. The spread is real and explainable. Context length, runtime, and settings each move the number. Here are receipts, each named and dated.

The one figure that needs its warning is the 20 token per second claim in one September guide. Every dated run above it, and the run-counted medians, sit at 35 to 55 for the 7 to 9B class. Treat any number without its context length and runtime as a mood, not a measurement.

The runtime moves the result by single digits. llama.cpp runs 3 to 10 percent faster than Ollama on this class of card, per a January 2026 benchmark roundup updated April 2026. The big lever is offload. A measured sweep found a flat cost. Every expert layer pushed to system RAM adds the same time per token, from the first layer to the last. With every expert streamed, the card's bandwidth stops mattering. The RAM sets the speed. A 12 GB card ran the 35B sparse class at 38.9 tokens per second. That is fast, and it is a RAM speed, not a card speed. The full treatment of that class is the A3B lane guide.

Against the 24 GB sibling, a same-rig community test in February 2024 ran a 7B model on both a 3090 and a 3060 12 GB. The 3060 delivered half the 3090's speed, not the fifth its spec sheet suggests. The reason is the bandwidth ratio, 360 GB/s against 936. Half the memory speed, half the tokens, and the 3060's 12 GB still loads what the 8 GB cards around it cannot.

A month of paid work: the serving break-even

The break-even above prices the card against its own rental. The other break-even asks: if the card earns, how much work covers its month? Work it on about 30 a month, the rounded always-on month both card classes share. The arithmetic is a division. The platform's per-unit rates make the revenue side, and they live in the machine table below, rendered from the platform's model data.

The honest state, in plain words. Several of these rows sit in the platform's model data with their rates set, and they are not on the public list as of 26 October 2026. The rendered table carries the listed rows, and this page quotes no live rate for the rest. A written record is not a launch. The models index is the live catalog.

units per month   U  =  monthly card cost  /  retail price per unit
units per day        =  U  /  30

Worked with the fence rows as the price side, on the about-30 month:

The vision reasoner row deserves its arithmetic in the open. Six million output tokens a month is 200,000 a day. A long vision answer runs about 2,800 tokens. The bar is about 70 answers a day. The 2B reasoner row, at 0.40 per million output, needs about 75 million tokens a month. That is millions a day, a long-run lane, not a month-one candidate.

Read the table as a ranked list of chances. The vision pair, segmenter large plus depth, carries the lowest bars on the board at 20 to 25 images a day. Demand runs broadest there, because mask and depth work feeds labeling and preprocessing. The 7B action policy sits at about 167 calls a day, and one research team's test sweep is thousands of calls. The pi0.5 class and the smallest lanes are proven loads with punishing bars. They belong on bigger or busier hardware.

The real bottleneck is not capability. Every load above runs, and the footprints are measured. The open question is whether the work shows up. That is what a cheap month of 3060 time measures. Find the demand before you commit to a card that serves it. The full robot-hour math, what a served hour of robot policy work costs, is the 6.48 derivation. The units themselves, per call against per robot hour, get their own guide.

What the 3060 is not

Here is the honest budget view. The 3060 is not a 24 GB card, and no quant trick makes it one.

• The pi0.5 class fills a 24 GB card by design, one shared process, three variants. Not a 3060 load.
• Dense 27B and up do not fit. The 35B sparse class needs about 22 GB resident. It runs streamed at 38.9 tokens per second with its experts in system RAM.
• The vision reasoner at 9.04 GB of weights is borderline alone on 12 GB, with nothing else resident. It remains fine on a 24 GB card at almost the same monthly rate.

When your work lives above the 12 GB line, the step is the used 3090 at about 1,093, renting at about 30 a month. That whole lane gets its own guide. For the robot-program budget view, integration and all, see the hobbyist compute budget.

One community modifier worth naming. People search for dual 3060 setups. Two used 3060s cost less than one used 3090 and total 24 GB, and the catch is software, not price. Multi-card inference across two slow cards carries real setup friction that a single 24 GB card avoids.

Decision rules

Run it well

Three settings decide half your speed. Quantize the context cache, q8_0 in place of full precision. It is one of the rare free wins, reported faster and lighter in the same benchmarks. Turn flash attention on. And keep the weights resident. Every layer pushed to system RAM bills the same fixed time per token forever. The runtime that carries all of this is llama.cpp and its packaged forms, Ollama and LM Studio. Buy used from a source with returns, ask about mining history, and test the card on arrival. That is the standard checklist for this market.

If you would rather not own hardware, the hosted routes are on the models index and the pricing page. You can open an account at signup.

Frequently asked questions

How much does RTX 3060 inference cost per hour?

At the community host this page measures, a 3060-class hour bills about 0.04 at the mid-priority tier our own runs use. The public entry tier lists from 0.015 at lowest priority. Conventional pods run 0.06 to 0.12 for the same class, per a rate aggregation read 26 October 2026. Always-on at 730 hours, the work tier is about 29 a month.

Is the RTX 3060 12 GB still the budget pick in 2026?

Yes, and the reason has not changed in two years. No new card under 300 gives you 12 GB, in the words of one September 2026 guide, and the October 2026 sold reads put the used card at about 300. The nearest new 12 GB card, the Arc B580, runs about 310 with a younger software stack. Faster 8 GB cards around the same price cannot load the 14B class at all. For inference the memory wins.

What fits in 12 GB?

At 4-bit, dense models through 14B fit, with 14B at about 10.4 GB total and moderate context. An 8B model at 8-bit fits tight. Dense 27B and up do not fit. The 35B sparse class needs about 22 GB resident, though it runs streamed at 38.9 tokens per second with its experts in system RAM. The full fit table with measured robot loads is above.

How many tokens per second does a 3060 12 GB generate?

The run-counted medians sit at about 55 tokens per second for 7 to 9B models at 4-bit, over 23 pooled runs. The 12 to 14B class runs about 28.7 over 12 runs. Both pools were updated in October 2026. Single dated runs span 35 to 44 for the small class and 23 to 32 for the big one. Context length, runtime, and cache settings explain most of the spread between claims you will see.

Should I buy the card or rent hours?

Rent if your hours are bursts. At two hours a day, the 7,500 hours inside a 300 card stretch past ten years. Bursts cost about 1 a day rented. Buy if the card runs around the clock. About 10 months of always-on rental equals the price. The power for an active owned hour, about 0.03 at the 2026 average, nearly equals the hourly rate anyway. The decision is hours, never rate.

The 3060 in one line. It is the lowest-cost 12 GB entry, renting at 0.015 to 0.04 the hour, breaking even in about 10 always-on months. It carries every dense model through 14B and the whole small end of robot work. Every rate for the hosted lanes sits on the pricing page. Sign up, create a key, and the only variable left is your own duty cycle.

Last verified: 2026-10-26