Can the Arc B580 run Llama 3.2 3B Instruct?
Fits comfortably at Q4_K_M.
Llama 3.2 3B Instruct is 3.21B parameters. At Q4_K_M the weights are about 1.8 GiB; with the 1.5 GiB runtime reserve that is 3.3 GiB against the card's 12GB, leaving about 8.7 GiB for context.
All three quants
12GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 1.8 GiB | 3.3 GiB | Fits comfortably | 8.7 GiB |
| Q8_0near-lossless | 3.2 GiB | 4.7 GiB | Fits comfortably | 7.3 GiB |
| FP16full weights | 6.0 GiB | 7.5 GiB | Fits comfortably | 4.5 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 1.8 GiB; with the 1.5 GiB we set aside for the runtime that is 3.3 of the card's 12 GB, leaving about 8.7 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 $330 for this card.
At about $330, this card costs roughly $27 per GB of VRAM.
Running it at its full 190 W board power for 4 hours a day would use about 23 kWh a month, about $4.20 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 Arc B580 clears Llama 3.2 3B 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 3B Instruct by Meta: 131,072-token native context, text, released 2024 under the Llama 3.2 Community License. Model card → · Hosted pricing on PicksByModel →