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ENGINEERING COMPUTATIONAL TOOL #162
CodeLlama 70B Programming Specialist (FP16 Uncompressed Native) on NVIDIA H100 80GB SXM5 VRAM & Throughput Calculator
Exact VRAM memory allocation, dynamic KV-cache requirements, and tensor parallelism slicing for CodeLlama 70B Programming Specialist 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 CodeLlama 70B Programming Specialist 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.