Can an RTX 4060 run Qwen3.5 27B?

In practice

It does not fit entirely. About 27 of the 64 layers of Qwen3.5 27B fit on the GeForce RTX 4060 at Q4_K_M, and the rest run on the CPU from system RAM. The estimate is about 3.0 tokens per second.

That number is lower than the share on the card suggests, and our own offload sweep shows why: every token waits for the slowest part, so the last layers left in RAM cost far more than the first ones moved. There is no quantization of this model worth using that fits entirely, so the choice is between this split and a smaller model.

The numbers

Qwen3.5 27B has 27.78 billion parameters across 64 layers. The GeForce RTX 4060 has 8 GB of VRAM at 272 GB/s, of which about 7.2 GB is left for a model once the operating system has taken its share.

Nothing on this ladder fits entirely, including the 2-bit levels we do not recommend. Rows marked partial or experts in RAM run with part of the model in system RAM, at the speed shown.

The settings we would use

llama-server -m <model>.gguf -ngl 27 -c 8192 -fa on

27 of 64 layers fit on the card. If the load fails with an out-of-memory error, lower the number by 2 and try again.

This split is set in llama.cpp. Ollama chooses its own placement and does not take these options, so run ollama ps to see what it picked. The llama.cpp flags guide explains each option above.

If you want it faster

A smaller model from the same family is the straightforward answer. These fit on an RTX 4060 at a quantization worth using:

• Qwen3.5 9B, 9.65B at Q5_K_M, 6.42 GB, about 33.3 tokens/s
• Qwen2.5 VL 7B Instruct, 8.29B at Q6_K, 6.74 GB, about 31.5 tokens/s
• Qwen3 8B, 8.19B at Q5_K_M, 6.54 GB, about 32.6 tokens/s

Shortening the context will not rescue this one: at 4k it still wants 15.74 GB against 15.99 GB at 8k, because the weights, not the cache, are what fill the card.

Or the hardware that does run it

The cheapest device we track that runs Qwen3.5 27B at a quantization worth using is the Arc A770 16GB, around $280 used , running it at IQ4_XS at about 23.1 tokens per second. Check the current price (affiliate link, indicative price reviewed 2026-09-01). Compare every option.

What else an RTX 4060 runs well

For comparison, these are the other popular models that fit this device at a sound quantization, best pairings first.

The same model on nearby hardware

Memory decides whether Qwen3.5 27B loads and bandwidth decides how quickly it answers. Here is what a step down, a card with the same memory and a step up do with it.

Wait for the first word350 ms

Then writes at3 tok/s

Whole answer58 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Questions people ask

Keep going

Everything the GeForce RTX 4060 runs, the full breakdown for Qwen3.5 27B, how the numbers are made in the methodology, or a different pairing in the calculator.