---
title: "Discussion on \"What 57 LLM Benchmark Runs on an AMD Instinct MI300X Taught Me About VRAM, Throughput, and Quantization\" | Hashnode"
description: "Discussion on \"What 57 LLM Benchmark Runs on an AMD Instinct MI300X Taught Me About VRAM, Throughput, and Quantization\". I built AMD LLM Lab to understand LLM inference performance before running the model."
url: "https://hashnode.com/posts/amd-llm-lab/6ac15c1df8437aace82ad975"
---

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# [What 57 LLM Benchmark Runs on an AMD Instinct MI300X Taught Me About VRAM, Throughput, and Quantization](https://vedantjadhav.hashnode.dev/amd-llm-lab?utm_source=hashnode&utm_medium=feed)

I built AMD LLM Lab to understand LLM inference performance before running the model.

Oct 3 · 7 min read · vedantjadhav.hashnode.dev  
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## Written by

[Vedant Jadhav](https://hashnode.com/@vedantjadhav)

@vedantjadhav · Pune, India

  
[View profile](https://hashnode.com/@vedantjadhav)

AI/ML Engineer exploring LLMs, Edge AI, and AI infrastructure

Building and researching LLMs, Edge AI, and efficient AI systems.

## Responses 3

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[Ahmet Özel](https://hashnode.com/@ahmetozel)

[Oct 4](https://hashnode.com/posts/amd-llm-lab/6ac15c1df8437aace82ad975/comment/6ac2939c02c54c62417e0a05)

The separation between MEASURED, ESTIMATED, and INTERPOLATED results gives the 57-run dataset a clearer scope. For the predictive layer, I would add a grouped holdout alongside the held-out configurations you describe: reserve an entire model size or precision/backend combination, then compare its error with ordinary configuration-level validation.

Nearby context lengths from the same model can make a split look reassuring without testing whether the predictor handles a genuinely new regime. Reporting those two errors separately would help users judge a recommendation outside the measured grid. A clear 'outside coverage' signal would also prevent a precise-looking prediction from implying stronger evidence than the dataset supplies.

[Vedant Jadhav](https://hashnode.com/@vedantjadhav)

[Oct 4](https://hashnode.com/posts/amd-llm-lab/6ac15c1df8437aace82ad975/comment/6ac2939c02c54c62417e0a05#reply-6ac2990f3d5c6f121817079a)

Thanks Ahmed. Very useful point. I agree that nearby configuration splits can overstate generalization. I’ll add grouped holdout experiments (model size / precision / backend) alongside the current validation, and introduce an explicit “outside coverage” signal for extrapolated predictions. This should make the uncertainty and deployment layer more rigorous.

[Ahmet Özel](https://hashnode.com/@ahmetozel)

[Oct 4](https://hashnode.com/posts/amd-llm-lab/6ac15c1df8437aace82ad975/comment/6ac2939c02c54c62417e0a05#reply-6ac29a19c53a302552cf1c20)

That would make the prediction layer easier to interpret. I would show the grouped holdout's coverage beside its error: which sizes, precisions, and backends were withheld, and which combinations still have no independent test. One overall grouped score could otherwise conceal a difficult regime behind easier ones.

For the outside-coverage signal, keep the trigger understandable to the user, such as an unmeasured backend or a size beyond the observed range. That explains why a number needs caution instead of displaying uncertainty as an unexplained badge.