Llama-3.3 70B High-Efficiency (INT8 SmoothQuant Precision) on NVIDIA A100 80GB PCIe VRAM & Throughput Calculator

Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for Llama-3.3 70B High-Efficiency quantized in INT8 SmoothQuant Precision deployed on NVIDIA A100 80GB PCIe.

Hardware & Deployment Parameters

Model Parameter Count (Llama-3.3 70B High-Efficiency) Billion Params

Quantization Precision

Target Context Window Length Tokens

Concurrent Serving Batch Size Concurrency

Single GPU VRAM Target (NVIDIA A100 80GB PCIe) GB

Initializing Scientific Computational Engine...

Engineering Implementation Guidelines

Set model parameter size (70B) and verify INT8 SmoothQuant Precision quantization precision.

Define production context length in tokens and peak concurrent query concurrency.

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 Llama-3.3 70B High-Efficiency require in INT8 SmoothQuant Precision?

Uncompressed weights alone consume 70.0 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.