The Starter 12 GB build

One used RTX 3060 12 GB added to a desktop you already own, enough for 8B to 14B models at Q4 to Q8 on the full CUDA stack.

Verdict

For someone with a desktop PC already, who wants to try local models without building a new machine. You add one used RTX 3060 12 GB and nothing else. 12 GB holds a 14B model at Q4_K_M with an 8k context, or an 8B model at Q8_0 with 16k. The card is rated at 170 W, and the one Basis build that reports a figure saw its whole machine draw about 280 W under full GPU and CPU load. It is the least expensive way into this site's Tiers, because the only purchase is one used card; the trade-off is speed, since the 3060's memory bandwidth is a fraction of a 3090's.

Model fit

Model Quantisation Context Note
Qwen2.5 14B Instruct Q4_K_M GGUF (8.99 GB file) 8k File plus about 1.6 GB of 16-bit KV cache comes to about 10.6 GB against 12 GB (12.88 GB); arithmetic in the FAQ. 16k leaves under 0.7 GB, too tight to count on. Estimated, not measured.
Llama 3.1 8B Instruct Q8_0 GGUF (8.54 GB file) 16k File plus about 2.1 GB of 16-bit KV cache comes to about 10.7 GB. For 32k, drop to Q4_K_M (4.92 GB file), which comes to about 9.2 GB with the cache. Estimated, not measured.
Phi-4 (14B) Q4_K_M GGUF (9.05 GB file) 8k (the model's native maximum is 16k) File plus about 1.7 GB of 16-bit KV cache comes to about 10.7 GB. The sahibgasimov Basis build runs phi4:14b at 4-bit through Ollama on its 3060, with 16k set in its editor config, but reports no speed or memory figure; at 16k our arithmetic leaves under 0.5 GB. Estimated, not measured.

Parts list

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Qty Part Buy USBuy DEBuy UK
1 NVIDIA GeForce RTX 3060 12 GB (ASUS Dual V2 OC)
VRAM
12 GB
Memory bandwidth
360 GB/s
TDP
170 W
Slot width
2 slots
Length
200 mm
Power connectors
1x 8-pin
VRAM/W
0.071 GB/W
VRAM/slot
6.0 GB

Basis

GPUs GPU model Board Source
1 RTX 3060 12GB Unknown zhonguncle.github.io
1 RTX 3060 12GB Unknown sahibgasimov.com

Compatibility warnings

This Tier is one card going into a PC you already own, so every warning here is a check on that host machine.

Power & noise

Estimated 24/7 energy use, with every assumption stated:

Arithmetic: 4 h × 0.280 kW ≈ 1.1 kWh, plus 20 h × 0.071 kW ≈ 1.4 kWh, gives about 2.5 kWh a day at the wall. That is about 77 kWh a month (× 30.4 days) and about 930 kWh a year (× 365). Most of it is the host idling, not the card working, so the cheapest saving is to sleep or shut the machine down when you’re not using it. Multiply by your own tariff. The power and noise guide explains the method and how to redo it for your own hours.

Noise: we have no noise measurement for either Basis build. sahibgasimov reports the card at 50 to 60 °C and 30 to 80 W under normal load with a 9B model, peaking around 75 °C and 170 W under sustained inference. The ASUS card is a two-fan open-air design, so its heat stays in the case; check that the host has at least one working intake and one exhaust fan. zhonguncle returned an RTX 2080 Ti with a blower cooler for coil whine before settling on the 3060, so listen to a used card under load while you can still return it.

FAQ

Has anyone built exactly this? The exact card and host pairing, no. Both Basis builds are upgraded OEM machines with a used 12 GB RTX 3060 added, which is this Tier’s whole idea: zhonguncle’s Dell T5810 (Xeon E5-2680 v4, 32 GB, 825 W) and sahibgasimov’s Dell Precision 3630 (Core i7-9700, 48 GB, stock 460 W). Neither uses the ASUS Dual V2 OC this site lists (sahibgasimov’s is an EVGA XC), and neither reports a tokens-per-second figure, so every Model fit row is our arithmetic.

How was the fit worked out? The VRAM sizing guide explains the method. The card holds 12,288 MiB, about 12.88 GB. Qwen2.5 14B has 48 layers, 8 key/value heads and a head size of 128 (5,120 ÷ 40 heads). A 16-bit KV cache, llama.cpp’s default, therefore takes 48 × 8 × 128 × 2 (keys and values) × 2 bytes = 196,608 bytes per token. At 8,192 tokens that is about 1.61 GB; with the 8.99 GB Q4_K_M file, about 10.6 GB, leaving roughly 2.3 GB for the CUDA context and compute buffers. At 16k the cache is 3.22 GB and the margin falls to about 0.7 GB. Llama 3.1 8B (32 layers, 8 key/value heads, head size 128) takes 131,072 bytes per token, 2.15 GB at 16k and 4.29 GB at 32k. Phi-4 (40 layers, 10 key/value heads, head size 128) takes 204,800 bytes per token, 1.68 GB at 8k and 3.36 GB at 16k.

Why a 3060 and not a newer 8 GB card? 12 GB is the point where a 14B model at Q4 fits with context to spare, and bought used, the 3060 is the least expensive card that gets there on CUDA. Its weak point is memory bandwidth: 360 GB/s against the RTX 3090’s 936 GB/s, so expect it to be slower than the bigger cards on the same model.

What’s the upgrade path? The 24 GB build. It is a new machine around one used RTX 3090, sized from the start to take a second card. 24 GB moves you from 14B models to 30B-class models at Q4. The 3060 is not on that Tier’s parts list; keep it in the old PC or sell it.

Can I put two 3060s in my PC instead? Possibly, for 24 GB, but most OEM hosts are the limit. You need a second x16-length slot with room for a two-slot card, a second 8-pin cable and more PSU headroom. zhonguncle calls dual GPUs in his T5810 “risky” for exactly those reasons. If your host can’t take it, the 24 GB build is the cleaner step.

Last reviewed

  • — First published.
  • — Linked the sizing and operating guides.