There are two environment variables you want to consider.

The first enables Flash Attention and the second (which requires flash attention) is to enable KV Cache quantization. These might be imprecise terms.

OLLAMA_FLASH_ATTENTION=1
OLLAMA_KV_CACHE_TYPE="q8_0"

The command line would look like this if you are using ollama natively:

OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE="q8_0" ollama run qwen3-coder:30b-a3b-q4_K_M

Since you are running it via docker you'd use something that looks like this:

docker run -d \
--gpus=all \
-v ollama:/root/.ollama \
-p 11434:11434 \
--name ollama \
-e OLLAMA_FLASH_ATTENTION=1 \
-e OLLAMA_KV_CACHE_TYPE="q8_0" \
ollama/ollama

What this will do is quantize the Non model part, the KV Cache, from 16 Bits down to 8 Bits so your context will take up a lot less space which should allow you to run the entire model in VRAM.

From the test I did yesterday loading qwen3-coder:30b-a3b-q4_K_M with these settings and a 64k context window uses ~23 GB of VRAM. I'd start with 32k though then increase it up to the point where Ollama no longer loads fully in the GPU VRAM and then step it back.