---
title: "The Memory Wall"
subtitle: "Why 3.2× more FLOPS gives only 1.7× speedup — and how to know in advance."
description: "Compare A100 and H100 GPUs to discover that for memory-bound workloads, bandwidth — not compute — determines performance. The most important fallacy in ML systems."
categories: ["node", "beginner"]
---

## The Question

NVIDIA's H100 has **3.2× more FLOP/s** than the A100. So upgrading should give you a 3.2×
speedup, right?

**Wrong.** For the workloads that matter most in production — LLM inference, recommendation
models, any memory-bound task — you get closer to **1.7×**. This tutorial shows you exactly
why, and teaches you to predict the actual speedup before spending a dollar on hardware.

::: {.callout-note}
## Prerequisites
Complete [Tutorial 0: Hello, Roofline](00_hello_roofline.qmd). You should understand
memory-bound vs. compute-bound and the ridge point concept.
:::

::: {.callout-note}
## What You Will Learn

- **Calculate** the actual speedup between two GPUs for a given workload
- **Explain** why the binding constraint determines which spec matters
- **Predict** whether a hardware upgrade will help a specific model
- **Apply** the roofline model to hardware procurement decisions
:::

::: {.callout-tip}
## Background: The Two Specs That Matter

GPU vendors advertise peak FLOP/s prominently. But every GPU also has a memory bandwidth
spec (in TB/s) that is equally important. Which spec determines your actual performance
depends entirely on which **regime** your workload is in:

| Regime | Binding Constraint | Speedup Scales With |
|:-------|:-------------------|:--------------------|
| Memory-bound | HBM bandwidth (TB/s) | Bandwidth ratio between GPUs |
| Compute-bound | Peak arithmetic (FLOP/s) | FLOP/s ratio between GPUs |

The key numbers for this tutorial:

| Spec | A100 | H100 | Ratio |
|:-----|:-----|:-----|:------|
| Peak FP16 | 312 TFLOP/s | 989 TFLOP/s | **3.2×** |
| HBM Bandwidth | 2.0 TB/s | 3.35 TB/s | **1.7×** |

If your workload is memory-bound, the speedup ceiling is 1.7×, regardless of the 3.2× compute improvement.
:::

---

## 1. Setup

```{python}
#| echo: false
#| output: false
import mlsysim  # installed via `pip install mlsysim` (see workflow)
Engine = mlsysim.Engine
```

```python
import mlsysim
from mlsysim import Engine
```

---

## 2. Side-by-Side Hardware Comparison

Let's load both GPUs from the Silicon Zoo and confirm the specs:

```{python}
from mlsysim.show import table

a100 = mlsysim.Hardware.Cloud.A100
h100 = mlsysim.Hardware.Cloud.H100

flops_a = a100.compute.peak_flops.to("TFLOPs/s").magnitude
flops_h = h100.compute.peak_flops.to("TFLOPs/s").magnitude
bw_a = a100.memory.bandwidth.to("TB/s").magnitude
bw_h = h100.memory.bandwidth.to("TB/s").magnitude

table(
    ["Spec", "A100", "H100", "Ratio"],
    [
        ["Peak FP16 (TFLOP/s)", flops_a, flops_h, f"{flops_h/flops_a:.1f}x"],
        ["HBM BW (TB/s)", bw_a, bw_h, f"{bw_h/bw_a:.1f}x"],
        ["Ridge (FLOP/byte)", flops_a*1e12/(bw_a*1e12), flops_h*1e12/(bw_h*1e12), ""]
    ]
)
```

The FLOP/s ratio is 3.2× but the bandwidth ratio is only 1.7×. The ridge point also
shifts: the H100 has a *higher* ridge, meaning more workloads fall into the memory-bound
regime on the H100 than on the A100.

---

## 3. The Fallacy: LLM Inference Speedup

Let's test the "3.2× speedup" claim with a workload that dominates production
today — Llama-3 8B inference at batch size 1:

```{python}
model = mlsysim.Models.Llama3_8B

# Solve on both GPUs
prof_a100 = Engine.solve(model=model, hardware=a100, batch_size=1, precision="fp16")
prof_h100 = Engine.solve(model=model, hardware=h100, batch_size=1, precision="fp16")

lat_a = prof_a100.latency.to("ms").magnitude
lat_h = prof_h100.latency.to("ms").magnitude
speedup = lat_a / lat_h

table(
    ["", "A100", "H100", "Speedup"],
    [
        ["Bottleneck", prof_a100.bottleneck, prof_h100.bottleneck, ""],
        ["Latency", lat_a * mlsysim.core.constants.ureg.ms, lat_h * mlsysim.core.constants.ureg.ms, f"{speedup:.1f}x"],
        ["Throughput", prof_a100.throughput, prof_h100.throughput, ""]
    ]
)
```

Both GPUs report **memory-bound**. The actual speedup is approximately **1.7×** — matching
the bandwidth ratio, not the FLOP/s ratio. The extra 1.5× compute power of the H100 is
entirely wasted for this workload.

**Sanity check:** Llama-3 8B at FP16 = 8B params × 2 bytes = 16 GB of weights. On the A100
(2.0 TB/s), minimum decode latency ≈ 16 GB ÷ 2.0 TB/s = **8.0 ms**. On the H100
(3.35 TB/s), it is 16 GB ÷ 3.35 TB/s = **4.8 ms**. Speedup = 8.0 / 4.8 = **1.67×** —
matching the bandwidth ratio, as expected for a memory-bound workload.

---

## 4. When DOES 3.2× Matter? The Batch Size Crossover

The FLOP/s advantage only kicks in when you cross into the compute-bound regime.
Let's sweep batch size on both GPUs and find the crossover:

```{python}
rows = []
for batch in [1, 4, 16, 32, 64, 128, 256]:
    pa = Engine.solve(model=model, hardware=a100, batch_size=batch, precision="fp16")
    ph = Engine.solve(model=model, hardware=h100, batch_size=batch, precision="fp16")

    la = pa.latency.to("ms").magnitude
    lh = ph.latency.to("ms").magnitude
    sp = la / lh if lh > 0 else 0

    rows.append([batch, pa.bottleneck, ph.bottleneck, f"{sp:.1f}x"])

table(["Batch", "A100 Bottleneck", "H100 Bottleneck", "Speedup"], rows)
```

We can visualize where Llama-3 8B sits on the H100's Roofline model. Note the high ridge point:

```{python}
from mlsysim.viz.plots import plot_roofline

# Plot the H100 roofline and see where Llama-3 8B (batch 1) falls
fig, ax = plot_roofline(h100, workloads=[model])
fig.show()
```

::: {.callout-important}
## Key Insight

**The binding constraint determines which hardware spec matters.** When you are memory-bound,
speedup scales with the bandwidth ratio (1.7×). When you are compute-bound, speedup scales
with the FLOP/s ratio (up to 3.2×). The transition happens at different batch sizes on each
GPU because the H100's higher ridge point means it *stays memory-bound longer*. If you are
making a procurement decision, the first question is not "how many FLOP/s?" but "which regime
will my production workload operate in?"
:::

---

## 5. The Procurement Table: Three Generations

Let's extend the analysis across three GPU generations to see the trend:

```{python}
gpus = [
    ("V100", mlsysim.Hardware.Cloud.V100),
    ("A100", mlsysim.Hardware.Cloud.A100),
    ("H100", mlsysim.Hardware.Cloud.H100),
]

rows = []
for name, hw in gpus:
    p = Engine.solve(model=model, hardware=hw, batch_size=1, precision="fp16")
    flops = hw.compute.peak_flops.to("TFLOPs/s").magnitude
    bw = hw.memory.bandwidth.to("TB/s").magnitude
    ridge = flops * 1e12 / (bw * 1e12)
    lat = p.latency.to("ms").magnitude
    rows.append([name, flops, bw, ridge, p.latency, p.bottleneck])

table(["GPU", "TFLOP/s", "BW (TB/s)", "Ridge", "Latency", "Bottleneck"], rows)
```

Across three generations, compute has grown faster than bandwidth. The ridge point keeps
rising, which means **more workloads are memory-bound on newer hardware**. This is the
memory wall — and it is getting worse, not better.

---

## Your Turn

::: {.callout-caution}
## Exercises

**Exercise 1: Predict before you compute.**
The B200 has ~8 TB/s HBM3e bandwidth and ~2250 TFLOP/s (FP16 dense). Before running any
code, predict: *write the speedup as a ratio (e.g., 2.3×)* for Llama-3 8B going from
H100 → B200 at batch size 1. Record your reasoning in one sentence. Then verify with
`mlsysim.Hardware.Cloud.B200`. How close were you?

**Exercise 2: Find the crossover batch size.**
For the A100, at what exact batch size does Llama-3 8B transition from memory-bound to
compute-bound? Write a loop that sweeps batch sizes from 1 to 512 in steps of 1 and
prints the first compute-bound batch size. Do the same for the H100. Why is the crossover
different?

**Exercise 3: Validate against published benchmarks.**
Look up the MLPerf Inference results for LLM workloads on A100 vs. H100 (available at
mlcommons.org). Compare the measured throughput ratio to our analytical prediction from
Section 3. What accounts for the difference? (Hint: real systems include software
optimizations like FlashAttention and continuous batching that our first-order model
does not capture. The gap between analytical prediction and measured performance is
itself informative.)

**Self-check:** If GPU-A has 500 TFLOP/s and 2 TB/s bandwidth, and GPU-B has 1000 TFLOP/s
and 4 TB/s bandwidth, what speedup do you expect for a memory-bound workload? For a
compute-bound workload? *(Write each answer as a ratio.)*
:::

---

## Key Takeaways

::: {.callout-tip}
## Summary

- **The memory wall is real**: HBM bandwidth has grown slower than compute across GPU generations
- **Speedup depends on regime**: memory-bound workloads scale with bandwidth ratio, not FLOP/s ratio
- **The ridge point rises each generation**: more production workloads are memory-bound on newer GPUs
- **Procurement decisions require regime analysis**: always check which wall binds before comparing specs
- **The roofline model predicts this**: `Engine.solve` tells you the regime before you spend a dollar
:::

---

## Next Steps

- **[Two Phases, One Request](02_two_phases.qmd)** — Discover that LLM serving hits *both* ceilings in a single request
- **[Quantization: Not a Free Lunch](05_quantization.qmd)** — Learn when reducing precision helps (memory-bound) vs. when it doesn't (compute-bound)
- **[Where to Invest](09_sensitivity.qmd)** — Use sensitivity analysis to quantify exactly how much each spec matters
- **[Silicon Zoo](../zoo/hardware.qmd)** — Browse all GPU specs including V100, A100, H100, H200, B200, MI300X, and TPUs
