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ENGINEERING COMPUTATIONAL TOOL #185
Stable Diffusion 3.5 Large 8B (FP8 Scaled Native Hopper) on NVIDIA B200 192GB Blackwell VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for Stable Diffusion 3.5 Large 8B quantized in FP8 Scaled Native Hopper deployed on NVIDIA B200 192GB Blackwell.
Hardware & Deployment Parameters
Billion Params
Tokens
Concurrency
GB
Initializing Scientific Computational Engine...
Engineering Implementation Guidelines
1
Set model parameter size (8.1B) and verify FP8 Scaled Native Hopper quantization precision.
2
Define production context length in tokens and peak concurrent query concurrency.
3
Evaluate required memory capacity and calculate multi-GPU tensor parallelism scaling across NVIDIA B200 192GB Blackwell nodes.
Frequently Asked Engineering Questions (FAQ)
How much VRAM does Stable Diffusion 3.5 Large 8B require in FP8 Scaled Native Hopper?
Uncompressed weights alone consume 8.1 GB. In addition, the KV cache scales with context tokens and concurrency batch size, plus ~1.8 GB CUDA driver overhead.
Can a single NVIDIA B200 192GB Blackwell run this model without Out-Of-Memory (OOM)?
If total weights + KV cache exceeds the 192 GB boundary, Tensor Parallelism (TP) or vLLM PagedAttention multi-GPU sharding across NVLink is required.
How does 4-bit quantization affect inference quality and speed?
Modern AWQ and GPTQ retain >98% perplexity compared to FP16 while halving memory footprint and doubling memory-bandwidth-bound token generation speed.