Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for Qwen-2.5 14B High-Density quantized in GGUF Q4_K_M Medium Quant deployed on AMD Instinct MI300X 192GB.
Uncompressed weights alone consume 8.3 GB. In addition, the KV cache scales with context tokens and concurrency batch size, plus ~1.8 GB CUDA driver overhead.
If total weights + KV cache exceeds the 192 GB boundary, Tensor Parallelism (TP) or vLLM PagedAttention multi-GPU sharding across NVLink is required.
Modern AWQ and GPTQ retain >98% perplexity compared to FP16 while halving memory footprint and doubling memory-bandwidth-bound token generation speed.