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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.

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What 57 LLM Benchmark Runs on an AMD Instinct MI300X Taught Me About VRAM, Throughput, and Quantization
V
Vedant Jadhav
Building and researching LLMs, Edge AI, and efficient AI systems.

How much VRAM does a model actually need?

How does model size affect generation speed?

Does quantization always reduce memory?

Does lower precision always make inference faster?

I wanted to answer these questions using measured data rather than relying only on theoretical parameter-count calculations.

That led me to build AMD LLM Lab, an open-source research platform for benchmarking and modeling LLM inference performance.

Live Lab: https://amd-llm-lab.vercel.app/

GitHub: https://github.com/VedantJadhav701/AMD\_LLM\_LAB

The Experiment

The benchmark environment was an AMD Instinct MI300X with approximately 192 GB of GPU memory, running the ROCm software stack.

I benchmarked multiple Qwen2.5 model sizes and different precision configurations, measuring:

  • Peak GPU memory

  • Generation throughput

  • Model size

  • Precision

  • Quantization configuration

  • Context length

  • Inference backend

The resulting dataset contains 57 measured configurations.

The model sizes ranged from 0.5B → 1.5B → 3B → 7B → 14B → 32B.

The benchmark included configurations using BF16, FP16, INT8, INT4 and INT2, with context lengths from 512 to 8192 tokens.

The goal wasn't simply to find the fastest configuration. It was to understand the relationship between model size, memory, precision and inference throughput.

Why Parameter Count Isn't Enough

A common way to estimate whether a model will fit into GPU memory is:

Parameters × Bytes per Parameter

For example, a 7B model in FP16 requires roughly:

7 billion × 2 bytes
≈ 14 GB

But that isn't the complete inference memory requirement.

Actual inference can also involve:

  • KV cache

  • Activations

  • Runtime allocations

  • Framework overhead

  • ROCm allocator behavior

  • Context length

  • Quantization implementation

So two configurations with the same number of parameters can behave differently in practice.

That's why I wanted to measure actual inference behavior.

What I Measured

For every benchmark configuration, I collected runtime characteristics and stored them in a structured dataset.

Peak VRAM

The maximum GPU memory allocated during inference.

Generation Throughput

Measured in:

tokens / second

Context Length

The benchmark covered:

512
1024
2048
4096
8192

This allowed me to investigate both model scaling and context effects.

Model Scaling

One of the clearest patterns was the relationship between model size and memory consumption.

As model size increased, peak VRAM generally increased as well.

Across the benchmark dataset, measured peak VRAM ranged from approximately:

4.20 GB → 68.77 GB

Measured generation throughput ranged from approximately:

4.27 → 78.32 tokens/sec

This gave me two different dimensions to analyze:

Model size
     ↓
VRAM requirement

Model size
     ↓
Generation throughput

A configuration can therefore fit comfortably in memory while still being relatively slow.

Quantization Was More Interesting Than Expected

One of the questions I wanted to investigate was whether lower-bit quantization always produces better inference characteristics.

The intuitive assumption is:

FP16
 ↓
INT8
 ↓
INT4
 ↓
INT2

Lower precision
      ↓
Less memory
      ↓
Faster inference

The measurements did not support such a simple rule.

In the tested environment, some lower-bit configurations reduced memory while also substantially reducing generation throughput.

This means that quantization is not simply a memory optimization problem.

Its behavior depends on factors such as:

  • Quantization method

  • Backend

  • Model architecture

  • Kernel implementation

  • Hardware

  • Precision

  • Runtime

This is one of the reasons empirical benchmarking is useful.

From Benchmarking to Prediction

Once I had the measured dataset, I wanted AMD LLM Lab to do something more useful than display benchmark tables.

The next question became:

What happens when a user selects a configuration that hasn't been directly benchmarked?

For example:

Model:       Qwen2.5 7B
Precision:   FP16
Context:     4096

If that exact configuration isn't present in the dataset, the application shouldn't pretend that a measured result exists.

Instead, AMD LLM Lab distinguishes between three types of results.

MEASURED

A configuration directly observed in the benchmark dataset.

ESTIMATED

A prediction generated from the performance models.

INTERPOLATED

A value derived from nearby measured configurations.

This provenance is displayed directly in the application.

Building the Predictive Layer

I trained models to estimate two important quantities:

Peak VRAM
Generation throughput

The input features include:

  • Model parameter count

  • Context length

  • Precision

  • Quantization

  • Backend

  • GPU characteristics

The purpose is not to claim that a relatively small benchmark dataset can perfectly predict every possible LLM workload.

Instead, the predictor acts as a planning tool grounded in the measured MI300X observations.

The evaluation uses held-out configurations rather than simply reporting performance on the same observations used for training.

Turning the Research Into a Tool

The project eventually became more than a collection of notebooks.

I built AMD LLM Lab as a complete application:

Benchmark Dataset
       ↓
Predictive Models
       ↓
Estimation Layer
       ↓
Recommendation Engine
       ↓
FastAPI
       ↓
Next.js
       ↓
Interactive Research Lab

The platform currently provides:

  • Benchmark Explorer

  • VRAM Predictor

  • Throughput Predictor

  • Configuration Recommender

  • Quantization Analysis

  • Scaling Analytics

  • Local Hardware Detection

  • Device-aware planning

The goal is to make the benchmark data useful to someone who wants to answer:

"What should I run on my hardware?"

rather than requiring them to inspect a CSV manually.

Your Hardware Matters Too

A benchmark on an MI300X doesn't mean the same configuration will behave identically on a smaller consumer GPU.

AMD LLM Lab can detect local hardware such as:

  • GPU

  • GPU memory

  • System RAM

  • CPU

  • Operating system

and use those constraints when planning configurations.

The application therefore separates:

Benchmark observation
        ≠
Device-specific prediction

This distinction is important because the current benchmark dataset is based on the MI300X environment.

What I Learned

The biggest lesson from the project is that LLM inference performance is a configuration problem, not simply a parameter-count problem.

Model size matters.

But so do:

  • Precision

  • Quantization

  • Context length

  • Backend

  • Runtime implementation

  • Hardware

A 7B model is not simply "a 7B model."

Its actual inference characteristics depend on how and where it is executed.

Limitations

There are several limitations to the current work.

The benchmark dataset is currently focused on AMD Instinct MI300X and a limited set of model configurations.

Therefore, these results should not be interpreted as universal performance laws for all GPUs, models or inference frameworks.

The predictive models are also constrained by the coverage and diversity of the underlying measurements.

More measurements across:

  • Additional model families

  • Additional AMD GPUs

  • Additional context lengths

  • Batch sizes

  • Inference backends

  • Repeated benchmark runs

would make the system substantially stronger.

These limitations are part of the research rather than something I want to hide.

What's Next?

The next stage of AMD LLM Lab is focused on expanding the benchmark ecosystem.

The longer-term direction is:

More Models
     +
More AMD GPUs
     +
More Configurations
     +
Community Benchmarks
     ↓
Larger Dataset
     ↓
Better Performance Models
     ↓
Better Hardware Recommendations

I also plan to turn the current benchmark work into a formal research paper focused on empirical modeling of LLM memory and inference throughput on AMD Instinct hardware.

Try AMD LLM Lab

The complete project is open source.

Live Lab

https://amd-llm-lab.vercel.app/

GitHub

https://github.com/VedantJadhav701/AMD\_LLM\_LAB

The repository contains the benchmark dataset, backend, prediction models, dashboard and reproducibility instructions.

Final Note

AMD LLM Lab is an independent open-source research project, not an official AMD product.

The purpose is straightforward:

Measure what we can. Model what we haven't measured. Make the uncertainty visible.

#ai#llm#machine-learning#machine-learning-models#machine-learning-algorithm#rocm#gpu#amd-mi300x#amd#amd-processor
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Comments (1)

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A
Ahmet Özel7d ago

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.

V
Vedant Jadhav7d ago

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.

A
Ahmet Özel7d ago

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.