File size: 3,078 Bytes
998922f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Load required libraries
library(ggplot2)
library(ggrepel)
# Define the years to be included
years <- 2017:2025
# Create a data frame for GPU data (memory in GiB)
gpu_data <- data.frame(
Year = years,
Category = "GPU",
# Representative Nvidia GPUs:
# 2017: Titan Xp, 2018: Tesla V100, 2019: Quadro RTX 8000,
# 2020 & 2021: Nvidia A100, 2022: Nvidia H100, then hypothetical future GPUs.
Name = c("Titan Xp", "Tesla V100", "Quadro RTX 8000", "Nvidia A100",
"Nvidia A100", "Nvidia H100", "Nvidia H100", "Nvidia H200", "Nvidia B200"),
Value = c(12, 32, 48, 80, 80, 80, 96, 141, 192) # Memory in GiB
)
# Create a data frame for Model data (parameters in billions)
model_data <- data.frame(
Year = years,
Category = "Model",
# Representative large models:
# 2017: Transformer, 2018: BERT-large, 2019: GPT-2, 2020: GPT-3,
# 2021: Megatron-Turing NLG, 2022: PaLM, then hypothetical future models.
Name = c("Transformer", "BERT-large", "GPT-2", "GPT-3",
"OPT", "PaLM", "GPT-4", "GPT-4o", "Grok 3 Ultra"),
Value = c(0.07, 0.34, 1.5, 175, 530, 540, 1000, 1800, 3000) # Parameters in billions
)
# Combine the two data frames
data <- rbind(gpu_data, model_data)
data$Category <- factor(data$Category, levels = c("GPU", "Model"))
# Create a label column to annotate the points:
# GPUs will be labeled with "Name (XXGB)" and Models with "Name (XXB)"
data$Label <- ifelse(data$Category == "GPU",
paste0(data$Name, " (", data$Value, "GB)"),
paste0(data$Name, " (", data$Value, "B)"))
# Generate the plot
p <- ggplot(data, aes(x = Year, y = Value, color = Category)) +
# Plot the points
geom_point(size = 3) +
# Connect the points for each category with a dashed line
geom_line(aes(group = Category), linetype = "dashed") +
# Add non-overlapping text labels
geom_text_repel(aes(label = Label), size = 3, show.legend = FALSE) +
# Use a log10 scale on the y-axis
scale_y_log10(
name = "Model Size (Billion Parameters) / GPU Memory (GiB)",
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
) +
scale_x_continuous(
breaks = seq(2017, 2025, 1)
) +
# Manually specify colors (a palette reminiscent of Nature figures)
scale_color_manual(values = c("GPU" = "#1b9e77", "Model" = "#d95f02")) +
labs(x = "Year",
y = "Size (GB for GPUs; Billions of Parameters for Models)"
#title = "Growing Gap Between GPU Memory and Model Size (2017-2025)",
#subtitle = "Large neural model parameter counts vs. Representative Nvidia GPU memory",
#caption = "Note: The parameter count of Grok 3 Ultra is projection"
) +
theme_bw() +
theme(plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 12),
axis.title.x = element_blank(),
legend.position = "bottom",
legend.title = element_blank()
)
# Display the plot
print(p)
ggsave("pdfs/gpu-mem-lag.pdf", plot = p, width = 10, height = 6)
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