Can the GeForce RTX 5060 run Llama 3.2 1B Instruct?
Fits comfortably at Q4_K_M.
Llama 3.2 1B Instruct is 1.23B parameters. At Q4_K_M the weights are about 0.7 GiB; with the 1.5 GiB runtime reserve that is 2.2 GiB against the card's 8GB, leaving about 5.8 GiB for context.
All three quants
8GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 0.7 GiB | 2.2 GiB | Fits comfortably | 5.8 GiB |
| Q8_0near-lossless | 1.2 GiB | 2.7 GiB | Fits comfortably | 5.3 GiB |
| FP16full weights | 2.3 GiB | 3.8 GiB | Fits comfortably | 4.2 GiB |
Verdict rules: fits comfortably when weights plus reserve sit within 85% of VRAM; tight when they fit with little left for context; CPU offload when they exceed VRAM by up to 2x (runs, slowly, with layers in system RAM); no beyond that. The KV cache per token depends on the model's architecture and is not modelled here.
The numbers for this pairing
At Q4_K_M the weights take about 0.7 GiB; with the 1.5 GiB we set aside for the runtime that is 2.2 of the card's 8 GB, leaving about 5.8 GB for the context cache.
The highest-quality version that fits entirely in memory is FP16 (full quality).
The cheapest card on our list that runs it comfortably at Q4 is the Arc B570 at about $260, against about $410 for this card.
At about $410, this card costs roughly $51 per GB of VRAM.
Running it at its full 145 W board power for 4 hours a day would use about 17 kWh a month, about $3.20 at the US average residential rate of 18.4 cents per kWh (2026-05); real use sits below that ceiling.
Prices are the lowest current listings our tracker saw on Oct 7, 2026, since averages include premium variants; launch prices are marked where we have no listing.
Good match. Get the card.
The GeForce RTX 5060 clears Llama 3.2 1B Instruct at Q4_K_M and at Q8_0. Live listings below; this page was rebuilt 2026-10-07.
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About the model
Llama 3.2 1B Instruct by Meta: 131,072-token native context, text, released 2024 under the Llama 3.2 Community License. Model card → · Hosted pricing on PicksByModel →