Can You Run Qwen 2.5 Coder 32B on NVIDIA RTX 4090?

The Qwen 2.5 Coder 32B (32.5B Dense) contains approximately 32.5 billion parameters. At FP16 (2 bytes per parameter), the raw weight matrix occupies 65 GB. With a 15% CUDA kernel overhead factor, effective VRAM for weights alone is 65 GB. The NVIDIA RTX 4090 provides 24 GB GDDR6X of memory, leaving insufficient headroom — quantization to INT8 or INT4 is mandatory for KV-cache and activation tensors.

At INT8 quantization (1 byte per parameter), the weight footprint drops to 33 GB, requiring a tensor parallel degree of approximately 2× across NVIDIA RTX 4090 instances. The estimated inference throughput is ~55 tok/s (INT4, batch=1), which translates to a cost-per-million-output-tokens of roughly $2.00 at current spot rates. For production deployments, vLLM (AWQ/INT4) or Ollama with PagedAttention is the recommended serving stack.