Can an Arc B580 run Qwen3.5 27B?
No
Only barely. It loads at IQ2_XXS, which compresses the weights far enough to visibly degrade the model, so the honest answer is that this pairing does not work.
The numbers
Qwen3.5 27B has 27.78 billion parameters across 64 layers. The Arc B580 has 12 GB of VRAM at 456 GB/s, of which about 11.2 GB is left for a model once the operating system has taken its share.
| Quantisation | Total needed | Fits | Speed |
|---|---|---|---|
| Q8_0 | 30.4 GB | no | — |
| Q6_K | 24.13 GB | no | — |
| Q5_K_M | 21.32 GB | no | — |
| Q4_K_M | 18.53 GB | no | — |
| IQ4_XS | 16.66 GB | no | — |
| Q3_K_M | 15.56 GB | no | — |
| IQ3_XXS degraded | 12.81 GB | no | — |
| Q2_K degraded | 13.75 GB | no | — |
| IQ2_XXS degraded | 9.58 GB | yes | 34.2 t/s |
1 of the 9 levels fit. Levels marked degraded are listed for completeness, not as advice.
What to do instead
The straightforward answer is a smaller model from the same family. These do fit on a Arc B580, at a quantisation worth using:
- Qwen3 14B — 14.77B at Q4_K_M, 10.47 GB, about 31 tokens/s
- Qwen2.5 14B Instruct — 14.77B at Q4_K_M, 10.72 GB, about 30.2 tokens/s
- Qwen1.5 MoE A2.7B — 14.32B at Q4_K_M, 10.28 GB, about 98.4 tokens/s
Shortening the context window will not rescue this one: at 4k it still wants 17.38 GB against 18.53 GB at 8k, because the weights rather than the cache are what fill the card. The gap here is too large for settings to close.
Or the hardware that does run it
The cheapest device we track that runs Qwen3.5 27B at a quantisation worth using is the Radeon RX 7900 XT, around $650 , running it at Q4_K_M at about 32.7 tokens per second. Check the current price (affiliate link; indicative price reviewed 2026-09-01). Compare every option.
See how fast it feels
Arc B580 running Qwen3.5 27B at IQ2_XXS
YouWhy does my model use more memory when the conversation gets longer?
Because of the KV cache. Every token you send leaves behind a key and a value vector in each layer of the model, and those stay in memory for as long as the conversation lasts.
The weights are a fixed cost: load a 4-bit 8B model and that is about 4.8 GB, whether you write one word or ten thousand. The cache is the part that grows, and it grows in a straight line with the number of tokens in the window.
How steeply depends on the model's attention design. With grouped-query attention, several query heads share one key-value pair, which cuts the cache by that ratio. Without it, every head keeps its own, and a long context can cost more memory than the weights themselves.
If you are short on memory, the first thing to try is lowering the context window in your runtime, not the quantisation.
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU. The same answer on RTX 4090 at Q5_K_M would take about 4.5 s.
Keep going
Everything the Arc B580 runs, the full breakdown for Qwen3.5 27B, or check a different pairing in the calculator.