I tested the V19 Q5 version GGUF many times, but the speed is actually slower than the original V19aio version?
V19-Q5-GGUF takes an average of 45 seconds, while the original V19aio only requires 35 seconds. However, it can indeed reduce memory usage, but I thought the generation speed would be faster with quantization.
From ChatGPT--
The short answer: the quantized Q5-GGUF is bottlenecked by dequantization and memory access, not raw math, so it ends up slower than FP8-scaled in your setup.
What’s going on
Although Q5 is smaller, it’s not necessarily faster. The main issues are:
Dequantization overhead
Q5-GGUF weights must be unpacked and dequantized on the fly.
This adds extra instructions per token.
FP8-scaled weights are often consumed directly by optimized GPU kernels.
Kernel maturity & hardware support
FP8 is increasingly natively supported on modern GPUs (Hopper, Ada, ROCm, etc.).
GGUF Q5 often falls back to less-optimized kernels (or CPU-side work).
Result: FP8 runs closer to peak throughput.
Memory bandwidth vs compute
Q5 saves memory, but:
It increases memory reads + unpacking
Bandwidth + cache misses dominate
FP8 is larger but streams cleanly through fast kernels.
KV cache still dominates
Quantizing weights doesn’t reduce:
KV cache size
Attention compute
So the expected speedup from Q5 never materializes.
Framework/runtime differences
GGUF paths (e.g., llama.cpp) prioritize portability, not max GPU speed.
FP8 paths (vLLM, TensorRT-LLM, ExLlamaV2, etc.) are aggressively optimized.
Why you see 45s vs 35s
Format Bottleneck
Q5-GGUF Dequantization + memory ops
FP8-scaled Pure GPU math (fast kernels)
So the “issue” is quantization overhead outweighing its benefits in your environment.
When Q5 does make sense
CPU inference
Low-VRAM GPUs
Batch size = 1, latency not critical
When FP8 wins
GPU inference
Modern hardware
Throughput or latency sensitive workloads
Thank you I got it