Home / AI Large Models, VRAM & Deep Learning Compute / Llama-3.3 70B High-Efficiency (FP16 Uncompressed Native) on NVIDIA H100 80GB SXM5 VRAM & Throughput Calculator
ENGINEERING COMPUTATIONAL TOOL #22
Llama-3.3 70B High-Efficiency (FP16 Uncompressed Native) on NVIDIA H100 80GB SXM5 VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for Llama-3.3 70B High-Efficiency quantized in FP16 Uncompressed Native deployed on NVIDIA H100 80GB SXM5.
Hardware & Deployment Parameters
Billion Params
Tokens
Concurrency
GB
Initializing Scientific Computational Engine...
Engineering Implementation Guidelines
1
Set model parameter size (70B) and verify FP16 Uncompressed Native 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 H100 80GB SXM5 nodes.
Frequently Asked Engineering Questions (FAQ)
How much VRAM does Llama-3.3 70B High-Efficiency require in FP16 Uncompressed Native?
Uncompressed weights alone consume 140.0 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 H100 80GB SXM5 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.