Can NVIDIA GeForce RTX 4060 Ti 16GB run Qwen 2.5 Coder 32B?
32B parameter Code model on 16GB GDDR6
Barely — requires CPU/RAM offloading
~1-3 tok/s (offload)
SpeedVery slow — expect 1-3 tokens/sec
QualityQuality is fine but the speed makes it impractical for interactive use
VRAM Requirements
Qwen 2.5 Coder 32B is a 32B parameter model. At full precision (FP16), it requires 64GB of VRAM. Your NVIDIA GeForce RTX 4060 Ti 16GB only has 16GB — not enough even at maximum compression.
FP16 (Full Precision)64GB (need 48GB more)
Maximum quality, no quantization
Q8 (8-bit)32GB (need 16GB more)
Near-lossless, ~50% size reduction
Q4 (4-bit)20GB (need 4GB more)
Good quality, ~75% size reduction
Your GPU VRAM: 16GB GDDR6 at 288 GB/s bandwidth
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)
How to Set It Up
Step 1: Install Ollama
curl -fsSL https://ollama.com/install.sh | shOllama is the easiest way to run local LLMs. Works on Linux, macOS, and Windows.
Step 2: Download and run Qwen 2.5 Coder 32B
ollama run qwen2.5:32b:q4_K_MThis downloads the model (~32GB). First run takes a few minutes.
Step 3: Verify GPU is being used
nvidia-smiCheck that VRAM usage increases when the model loads. You should see ~20GB used.
NVIDIA GeForce RTX 4060 Ti 16GB Specs
VRAM16GB GDDR6
Memory Bandwidth288 GB/s
TDP165W
CUDA Cores4,352
Street Price~$420
AI Rating5/10
Other GPUs That Run Qwen 2.5 Coder 32B
Other Code Models on NVIDIA GeForce RTX 4060 Ti 16GB
About Qwen 2.5 Coder 32B
Best open-source coding model. Needs 24GB+ for good quality.
Category: Code · Parameters: 32B · CUDA required: No (runs via llama.cpp/GGUF)