Change Happening Faster Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer 2/24 2/25 4/25 75% 60% 10% 21% 15% 0% Details on Page 293 USA – LLM #1 China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Richard Hirsh; John McCallum; OpenAI Details on Page 138 0 Years 72 Years Electric Power Computer Memory AI Inference AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise

space below. The extra column contains more helpful tools: Layout contains information about the box model properties of the element. Note that flex and grid do not work yet. Computed, which contains all the can run unit tests with ./mach test-unit . Example calls: Running the whole test-suite can be very memory intensive, you can dampen this behaviour somewhat with with restricting the amount of compiled crates Servo's runs: Interval Profiling TSV Profiling Generating Timelines Built-in sampling profiler Memory Profiling Using macOS Instruments Interval Profiling Using the -p option followed by a number (time

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.6 Working with Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 32.5 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 32.6 External . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.6 Working with Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 32.5 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 32.6 External . . . . . . . . . . . . . . . . . 416 34.3 Measure performance with @time and pay attention to memory allocation . . . . . . 417 34.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 31.6 Working with Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 33.5 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 33.6 External . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 31.6 Working with Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 33.5 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 33.6 External . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 31.6 Working with Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 33.5 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 33.6 External . . . . . . . . . . . . . . . . . 444 35.3 Measure performance with @time and pay attention to memory allocation . . . . . . 445 35.4 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Wall-time Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 33.6 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 33.7 External Type inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Wall-time Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 33.6 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 33.7 External Type inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency

Calling Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Wall-time Profiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 33.6 Memory allocation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 33.7 External Type inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency