Can the GeForce RTX 5090 run Qwen3.6 27B?
Fits comfortably at Q4_K_M.
Qwen3.6 27B is 27.8B parameters. At Q4_K_M the weights are about 15.5 GiB; with the 1.5 GiB runtime reserve that is 17.0 GiB against the card's 32GB, leaving about 15.0 GiB for context.
All three quants
32GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 15.5 GiB | 17.0 GiB | Fits comfortably | 15.0 GiB |
| Q8_0near-lossless | 27.4 GiB | 28.9 GiB | Fits, tight on context | 3.1 GiB |
| FP16full weights | 51.8 GiB | 53.3 GiB | Only with CPU offload | short 21.3 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 15.5 GiB; with the 1.5 GiB we set aside for the runtime that is 17.0 of the card's 32 GB, leaving about 15.0 GB for the context cache.
The highest-quality version that fits entirely in memory is Q8_0.
The cheapest card on our list that runs it comfortably at Q4 is the Radeon RX 7900 XTX at about $850, against about $6,500 for this card.
At about $6,500, this card costs roughly $203 per GB of VRAM.
Running it at its full 575 W board power for 4 hours a day would use about 69 kWh a month, about $12.70 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 5090 clears Qwen3.6 27B 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
Qwen3.6 27B by Alibaba Qwen: 262,144-token native context, text + vision, released 2026 under the Apache-2.0. Model card → · Hosted pricing on PicksByModel →