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ENGINEERING COMPUTATIONAL TOOL #95
Mixtral 8x7B MoE Classic (INT8 SmoothQuant Precision) on NVIDIA A100 80GB PCIe VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for Mixtral 8x7B MoE Classic quantized in INT8 SmoothQuant Precision deployed on NVIDIA A100 80GB PCIe.
Hardware & Deployment Parameters
Billion Params
Tokens
Concurrency
GB
Initializing Scientific Computational Engine...
Engineering Implementation Guidelines
1
Set model parameter size (46.7B) and verify INT8 SmoothQuant Precision 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 A100 80GB PCIe nodes.
Frequently Asked Engineering Questions (FAQ)
How much VRAM does Mixtral 8x7B MoE Classic require in INT8 SmoothQuant Precision?
Uncompressed weights alone consume 46.7 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 A100 80GB PCIe run this model without Out-Of-Memory (OOM)?
If total weights + KV cache exceeds the 80 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.