In 1996, Intel released the Pentium MMX, and with it introduced something that would reshape how processors handle parallel work: SIMD, or Single Instruction, Multiple Data. The concept wasn't new, but bringing it to consumer CPUs was a revolution that laid the groundwork for the GPU era and modern AI acceleration.
What Is SIMD?
SIMD allows a single instruction to operate on multiple data points simultaneously. Instead of adding two numbers one at a time, a SIMD instruction can add eight pairs of numbers in a single clock cycle. For multimedia applications, which process large arrays of pixels, audio samples, or video frames, this was transformative.
Intel's MMX (MultiMedia eXtensions) introduced 57 new instructions that operated on 64-bit registers. These registers could be packed with data in different configurations: eight bytes, four 16-bit words, or two 32-bit doublewords. A single instruction could then perform the same operation on all elements simultaneously.
Why It Mattered in the 90s
The 90s were the multimedia boom. CD-ROM drives, digital audio, video playback, and 3D gaming were pushing CPUs to their limits. Before SIMD, processors handled multimedia data one element at a time. A 640x480 image required 307,200 pixel operations. Without SIMD, that meant 307,200 individual instructions.
With MMX, those same operations could be batched. Eight pixels at a time meant 38,400 instructions. That's an 8x speedup for image processing, color conversion, and video decoding. For consumers, this meant smoother video playback, better game performance, and real-time audio effects without dedicated hardware.
The Technical Implementation
MMX reused the floating-point registers, which was a clever but controversial design decision. This meant you couldn't mix floating-point and MMX operations freely. You had to execute an EMMS instruction to switch modes, and the transition was costly. Intel later addressed this with SSE (Streaming SIMD Extensions) in 1999, which introduced separate 128-bit registers.
The programming model was assembly-level. Developers had to write inline assembly or use compiler intrinsics. There was no auto-vectorization to speak of. If you wanted SIMD performance, you wrote SIMD code by hand.
The Legacy
MMX was just the beginning. The SIMD lineage continued through:
- SSE (1999): 128-bit registers, single-precision floating point
- SSE2 (2001): Double-precision floating point, integer operations
- SSE3/SSSE3/SSE4 (2004-2007): Specialized operations, string processing
- AVX (2011): 256-bit registers, more complex operations
- AVX-512 (2016): 512-bit registers, mask registers, conflict detection
Each generation doubled the register width and added more sophisticated operations. The same concept that started with 64-bit MMX now operates on 512-bit vectors in modern processors.
The Connection to Modern AI
If you're working with AI models, you're using SIMD whether you know it or not. The matrix multiplications that power neural networks are fundamentally SIMD operations. GPUs took the SIMD concept to extremes, with thousands of SIMD lanes operating in parallel.
When you run a transformer model, the attention mechanism is essentially a massive series of dot products. Each dot product is a SIMD multiply-accumulate operation. The AVX-512 VNNI instructions in modern Intel processors can do integer matrix multiply-accumulate in a single instruction per element.
The journey from MMX's 64-bit registers to today's 512-bit AVX-512 and GPU tensor cores represents one of the most important architectural lineages in computing. It started with making video playback smoother in 1996 and ended with making AI inference practical in 2026.
Lessons for Today
The MMX story has lessons for modern developers working with AI and ML:
Parallelism is always the answer. When data can be processed independently, it should be processed simultaneously. This is as true for LLM inference as it was for 90s video decoding.
Hardware follows software. Intel built MMX because multimedia software needed it. Today's tensor cores exist because ML software needs them. The hardware accelerator always follows the software workload.
APIs matter. MMX was hard to use because it required assembly programming. Modern SIMD is easier with intrinsics and auto-vectorizing compilers, but there's still a gap between what hardware can do and what software easily expresses.
The Pentium MMX may seem like ancient history, but it was the first step on a path that leads directly to the AI revolution we're living through today. Understanding that path helps us understand where computing is going next.
