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October 13, 2026No. 164
Home / Will it run? / Qwen2.5 7B Instruct / GeForce RTX 5060 Ti 8GB
GeForce RTX 5060 Ti 8GB · 8GB GDDR7

Can the GeForce RTX 5060 Ti 8GB run Qwen2.5 7B Instruct?

Fits comfortably at Q4_K_M.

Qwen2.5 7B Instruct is 7.61B parameters. At Q4_K_M the weights are about 4.3 GiB; with the 1.5 GiB runtime reserve that is 5.8 GiB against the card's 8GB, leaving about 2.2 GiB for context.

All three quants

8GB card
QuantWeightsWith reserveVerdictHeadroom
Q4_K_Mthe everyday quant4.3 GiB5.8 GiBFits comfortably2.2 GiB
Q8_0near-lossless7.5 GiB9.0 GiBOnly with CPU offloadshort 1.0 GiB
FP16full weights14.2 GiB15.7 GiBOnly with CPU offloadshort 7.7 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 4.3 GiB; with the 1.5 GiB we set aside for the runtime that is 5.8 of the card's 8 GB, leaving about 2.2 GB for the context cache.

The cheapest card on our list that runs it comfortably at Q4 is the Arc B570 at about $260, against about $450 for this card.

At about $450, this card costs roughly $56 per GB of VRAM.

Running it at its full 180 W board power for 4 hours a day would use about 22 kWh a month, about $3.97 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 13, 2026, since averages include premium variants; launch prices are marked where we have no listing.

Good match. Get the card.

The GeForce RTX 5060 Ti 8GB clears Qwen2.5 7B Instruct at Q4_K_M. Live listings below; this page was rebuilt 2026-10-13.

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About the model

Qwen2.5 7B Instruct by Alibaba Qwen: 32,768-token native context, text, released 2024 under the Apache-2.0. Model card → · Hosted pricing on PicksByModel →

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