Map of SIMD Instruction Sets and CPUs

The original version of this article was written in 2000, when the practical SIMD landscape on x86 processors was still small enough to fit in a compact table.

At that time, the important questions were simple:

• Does the CPU support MMX?
• Does it support Extended MMX?
• Does it support SSE?
• Does it support SSE2?
• Does it support AMD 3DNow!?

That map was useful because the market was transitioning from scalar x86 code to the first generation of multimedia SIMD code.

Today the situation is much larger.

Modern x86 CPUs may support MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2, FMA, F16C, AVX-512, AVX-VNNI, AVX10, AMX, and many smaller specialized extensions. AMD also had its own historical path with 3DNow!, SSE4a, FMA4, XOP, and later AVX-512 support in Zen 4 and Zen 5.

This article updates the old map into a modern guide to SIMD instruction sets on Intel and AMD processors.

The goal is not to list every instruction mnemonic. The goal is to explain the major SIMD families, when they appeared, which processor generations support them, and what a programmer should check before using them.

Important Caveat: Always Check CPUID

Processor generation tables are useful, but they are not a replacement for runtime feature detection.

Different SKUs, steppings, BIOS settings, operating systems, virtual machines, and cloud instances may expose different instruction sets.

For production software, always check CPU capabilities at runtime using CPUID. For AVX, AVX2, AVX-512, and AVX10, also verify that the operating system supports saving and restoring the required extended register state. That usually means checking OSXSAVE and XGETBV, not only the raw CPU feature bit.

Use the tables in this article as a historical and practical map, not as the final authority for one specific machine.

What SIMD Means on x86

SIMD means:

Single Instruction, Multiple Data

A SIMD instruction applies one operation to several data elements packed into a vector register.

For example, instead of adding one pair of 32-bit integers:

a + b

a SIMD instruction can add several pairs at once:

a0 + b0
a1 + b1
a2 + b2
a3 + b3
...

The wider the vector register, the more elements can be processed by one instruction.

A simplified map looks like this:

SIMD family	Main register type	Register width	Typical use
MMX	mm0–mm7	64-bit	Packed integer multimedia
SSE	xmm0-…	128-bit	Single-precision floating point, later integer and double
AVX	ymm0-…	256-bit	Wider floating point
AVX2	ymm0-…	256-bit	Wider integer SIMD
AVX-512	zmm0-… plus mask registers	512-bit	HPC, AI, media, compression, crypto
AVX10	versioned AVX-family ISA	128/256/512-bit model	Future Intel converged vector ISA
AMX	tile registers	tile/matrix state	Matrix multiplication, AI acceleration

MMX, SSE, AVX, and AVX-512 are SIMD instruction sets.

AMX is different. It is not traditional lane-based SIMD. It is a tile/matrix extension. It belongs in the modern performance map because it is part of the same trend: moving more data-parallel work directly into the CPU.

The Short Timeline

Era	Intel	AMD	SIMD milestone
1997	Pentium MMX, Pentium II	K6	MMX
1998-1999	Pentium III	K6-2, Athlon	SSE on Intel, 3DNow! on AMD
2000-2001	Pentium 4	Athlon XP, Athlon 64 later	SSE2 starts becoming important
2004-2006	Prescott, Core, Core 2	Athlon 64, Opteron	SSE3, SSSE3
2007-2009	Penryn, Nehalem	Phenom, Phenom II	SSE4.1, SSE4.2, SSE4a on AMD
2011	Sandy Bridge	Bulldozer	AVX
2012-2013	Ivy Bridge, Haswell	Piledriver	F16C, FMA, AVX2
2015-2017	Skylake, Skylake-X, Xeon Phi	Zen	AVX2 becomes mainstream; AVX-512 appears on Intel high-end/server
2019-2021	Ice Lake, Tiger Lake, Rocket Lake	Zen 2, Zen 3	Wider AVX-512 coverage on some Intel CPUs
2022-2024	Sapphire Rapids, Meteor Lake, Arrow Lake, Xeon 6	Zen 4	Intel AMX; AMD adds AVX-512 in Zen 4
2024-2026	Granite Rapids, Xeon 6, Core Ultra generations	Zen 5, EPYC Turin	AVX-512 expands on AMD; Intel moves toward AVX10

MMX

MMX was the first widely adopted SIMD extension on x86.

It introduced eight 64-bit registers:

mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7

These registers can hold packed integers:

Data type	Elements per MMX register
8-bit integers	8
16-bit integers	4
32-bit integers	2
64-bit integer	1

MMX was useful for:

• image processing;
• audio processing;
• video decoding;
• graphics effects;
• color conversion;
• packed integer arithmetic.

The main limitation of MMX is that it aliases the old x87 floating-point register stack. After using MMX, code must execute EMMS, or the _mm_empty() intrinsic in C/C++, before returning to x87 floating-point code.

Typical processors with MMX:

Vendor	Processor generation
Intel	Pentium MMX, Pentium II, Celeron, Pentium III, Pentium 4
AMD	K6, K6-2, K6-III, Athlon, Duron, Athlon XP, Athlon 64, Opteron

MMX is now obsolete for new code, but it still matters when reading old multimedia libraries and early SIMD code.

AMD 3DNow!

3DNow! was AMD’s alternative SIMD extension introduced with the K6-2.

Unlike MMX, which was mainly integer-oriented, 3DNow! added packed single-precision floating-point operations using MMX registers.

This gave AMD a way to accelerate 3D games and multimedia software before SSE was widely available.

Main 3DNow! variants:

Extension	Description
3DNow!	Original AMD packed floating-point SIMD extension
Enhanced 3DNow!	Added more DSP/media-oriented instructions
3DNow! Professional	Marketing name used around the Athlon XP era, including SSE support

Typical AMD processors with 3DNow!:

Processor generation	SIMD support
K6-2	MMX, 3DNow!
K6-III	MMX, 3DNow!
Athlon	MMX, Extended MMX, Enhanced 3DNow!
Duron	MMX, Extended MMX, Enhanced 3DNow!
Athlon XP	MMX, Extended MMX, Enhanced 3DNow!, SSE

3DNow! is no longer relevant for new code. Modern AMD processors support the Intel-compatible SSE/AVX instruction families instead.

SSE

SSE stands for:

Streaming SIMD Extensions

It was introduced by Intel with the Pentium III.

SSE added 128-bit XMM registers:

xmm0 xmm1 xmm2 xmm3 ...

The original SSE instruction set mainly focused on packed single-precision floating-point arithmetic.

One XMM register can hold four 32-bit floats:

float0 float1 float2 float3

That made SSE useful for:

• 3D graphics;
• geometry processing;
• game engines;
• audio processing;
• image filters;
• physics calculations.

SSE also added a few MMX-related integer instructions and memory/cache control instructions, but its most important architectural change was the introduction of the XMM register file.

Typical first-generation SSE processors:

Vendor	Processor generation
Intel	Pentium III, Celeron II
AMD	Athlon XP and later

SSE2

SSE2 was introduced with the Intel Pentium 4 and became one of the most important x86 SIMD extensions ever.

SSE2 expanded XMM usage to include:

• packed double-precision floating-point;
• packed integer operations;
• 128-bit integer SIMD;
• better replacement paths for old MMX code.

One XMM register can hold:

Data type	Elements per XMM register
8-bit integers	16
16-bit integers	8
32-bit integers	4
64-bit integers	2
32-bit floats	4
64-bit doubles	2

SSE2 is especially important because it is part of the baseline for x86-64. If you are writing 64-bit x86 code, SSE2 is normally assumed to be available.

Typical processors with SSE2:

Vendor	Processor generation
Intel	Pentium 4 and later
AMD	Athlon 64, Opteron, Sempron 64 and later

For new code, SSE2 is a much better minimum target than MMX.

SSE3

SSE3 was introduced with later Pentium 4 Prescott processors.

It added a small set of instructions, including:

• horizontal add/subtract operations;
• improved complex-number support;
• some thread synchronization and memory-related instructions.

SSE3 was not as large a jump as SSE2, but it filled practical gaps.

Typical processors with SSE3:

Vendor	Processor generation
Intel	Pentium 4 Prescott, Pentium D, Core, Core 2
AMD	Later Athlon 64, Opteron, Phenom and newer

SSSE3

SSSE3 stands for:

Supplemental Streaming SIMD Extensions 3

Despite the similar name, SSSE3 is not the same as SSE3.

SSSE3 added very useful integer SIMD instructions, especially for media processing. The most famous is probably PSHUFB, a byte shuffle instruction that became extremely valuable for:

• codecs;
• text processing;
• lookup-table tricks;
• cryptography;
• pixel manipulation;
• byte rearrangement.

Typical Intel processors with SSSE3:

Vendor	Processor generation
Intel	Core 2 and later
AMD	Later Bulldozer-family and Zen-family processors

AMD did not support SSSE3 in the earliest Athlon 64 and Phenom generations.

SSE4.1

SSE4.1 was introduced with Intel Penryn, the 45 nm Core 2 generation.

It added many practical instructions for integer and floating-point SIMD, including:

• packed blends;
• dot product;
• rounding;
• packed min/max improvements;
• integer widening and packing operations;
• better support for media and graphics workloads.

Typical processors with SSE4.1:

Vendor	Processor generation
Intel	Penryn Core 2, Nehalem and later
AMD	Bulldozer and later, Zen and later

SSE4.1 is very useful for image processing and video code because it reduces the number of instructions needed for common packed operations.

SSE4.2

SSE4.2 was introduced with Intel Nehalem.

It added:

• string and text comparison instructions;
• CRC32;
• additional comparison support.

SSE4.2 is especially associated with faster string processing and checksum code.

Typical processors with SSE4.2:

Vendor	Processor generation
Intel	Nehalem, Westmere and later
AMD	Bulldozer and later, Zen and later

SSE4a

SSE4a is an AMD-specific extension.

Despite the name, it is not the same as Intel SSE4.1 or SSE4.2.

SSE4a appeared in AMD K10-family processors such as Phenom and Opteron Barcelona. It added a small number of instructions, including unaligned streaming load/store support and extract/insert operations.

Typical AMD processors with SSE4a:

Vendor	Processor generation
AMD	K10 / Barcelona / Phenom / Phenom II / some Opteron generations
Intel	Not supported

Because SSE4a is AMD-specific and relatively small, portable software rarely targets it as a primary SIMD path.

AES-NI and PCLMULQDQ

AES-NI and PCLMULQDQ are not usually described as “SSE versions,” but they are closely related to the SIMD evolution of x86.

AES-NI accelerates AES encryption and decryption.

PCLMULQDQ performs carry-less multiplication, useful for cryptography, CRC algorithms, and Galois field arithmetic.

Typical support:

Vendor	Processor generation
Intel	Westmere and later, broadly
AMD	Bulldozer and later, broadly in modern Ryzen and EPYC

These instructions are very important for cryptographic performance.

AVX

AVX stands for:

Advanced Vector Extensions

AVX introduced 256-bit YMM registers.

Conceptually, each YMM register extends an XMM register:

xmm0 = lower 128 bits of ymm0
ymm0 = 256-bit register

AVX also introduced the VEX instruction encoding, which improved instruction encoding and allowed three-operand non-destructive forms.

For example, older SSE code often overwrote one input:

a = a + b

AVX can encode:

c = a + b

This is a major improvement for register allocation and instruction scheduling.

AVX mainly extended floating-point SIMD to 256 bits:

Data type	Elements per YMM register
32-bit floats	8
64-bit doubles	4

Typical processors with AVX:

Vendor	Processor generation
Intel	Sandy Bridge and later
AMD	Bulldozer and later, Zen and later

Important note: AVX requires operating system support for saving and restoring YMM state. Runtime detection must check both CPU support and OS support.

F16C

F16C added conversion instructions between 16-bit half-precision floating-point and 32-bit single-precision floating-point.

It is not a full half-precision arithmetic extension. It is mainly a conversion extension.

Typical processors with F16C:

Vendor	Processor generation
Intel	Ivy Bridge and later
AMD	Piledriver and later, Zen and later

F16C became important for graphics, machine learning data conversion, and storage of compact floating-point data.

FMA3

FMA means:

Fused Multiply-Add

A fused multiply-add computes:

a * b + c

as one fused operation.

FMA improves both performance and numerical behavior for many floating-point workloads.

FMA3 is the dominant x86 FMA form today.

Typical processors with FMA3:

Vendor	Processor generation
Intel	Haswell and later
AMD	Piledriver and later, Zen and later

FMA3 is very important for:

• matrix multiplication;
• DSP;
• physics;
• machine learning;
• linear algebra;
• scientific computing.

FMA4

FMA4 was an AMD extension introduced around the Bulldozer era.

It used a four-operand form, which was elegant from a programmer’s point of view, but it did not become the long-term x86 standard.

Typical processors with FMA4:

Vendor	Processor generation
AMD	Bulldozer-family processors
Intel	Not supported

FMA4 is now considered a historical AMD-specific path. New software should use FMA3.

XOP

XOP was another AMD-specific SIMD extension from the Bulldozer era.

It added integer vector operations, including permutes, shifts, comparisons, and multiply-accumulate style operations.

Typical processors with XOP:

Vendor	Processor generation
AMD	Bulldozer-family processors
Intel	Not supported

XOP did not become a portable x86 SIMD target. Modern AMD Zen processors do not continue the XOP direction as a primary programming model.

AVX2

AVX2 was introduced by Intel Haswell.

It extended most 128-bit integer SIMD operations from SSE2/SSSE3/SSE4 to 256-bit YMM registers.

This was a major milestone because AVX1 was mainly about floating-point SIMD, while AVX2 made 256-bit integer SIMD broadly useful.

AVX2 added or expanded support for:

• 256-bit integer arithmetic;
• 256-bit logical operations;
• packed shifts;
• packed compares;
• gathers;
• wider byte/word/dword processing;
• stronger general-purpose SIMD support.

Typical processors with AVX2:

Vendor	Processor generation
Intel	Haswell, Broadwell, Skylake and later
AMD	Excavator partially, Zen and later

AVX2 is currently one of the best practical SIMD targets for portable high-performance x86 code because it is widely available across modern Intel and AMD processors.

AVX-VNNI

AVX-VNNI brings Vector Neural Network Instructions to AVX-width encodings, without requiring full AVX-512.

VNNI-style instructions are useful for integer dot products, especially in neural-network inference.

There are two related ideas:

Extension	Meaning
AVX-512 VNNI	VNNI instructions in the AVX-512 family
AVX-VNNI	VNNI-style instructions available without requiring the full AVX-512 programming model

AVX-VNNI matters because Intel client CPUs after the removal of client AVX-512 still needed efficient AI/inference instructions.

Typical support appears in newer Intel client and server generations, but exact support should be checked with CPUID.

AVX-IFMA

AVX-IFMA provides integer fused multiply-add style operations outside the older AVX-512-only naming path.

It is useful for big-number arithmetic, cryptography, and workloads that benefit from packed integer multiply-add operations.

As with other newer AVX-family subsets, support is generation- and SKU-dependent. Check CPUID.

AVX-VNNI-INT8 and AVX-VNNI-INT16

These are newer AVX-family extensions focused on integer dot-product operations for low-precision AI and inference workloads.

They target common neural-network data types:

Extension	Main focus
AVX-VNNI-INT8	8-bit integer neural-network operations
AVX-VNNI-INT16	16-bit integer neural-network operations

These are part of the modern trend toward CPU-side AI acceleration without requiring every workload to move to a GPU or NPU.

AVX-NE-CONVERT

AVX-NE-CONVERT is another newer extension aimed at efficient conversion involving low-precision numerical formats.

This belongs to the same broad family of AI-oriented CPU instructions as VNNI, BF16, FP16, and future AVX10/ACE work.

For portable software, this is not a baseline assumption. It is a specialized path selected by CPUID.

AVX-512

AVX-512 is not a single instruction set in the same simple sense as SSE2 or AVX2.

It is a family of extensions based around 512-bit ZMM registers and mask registers.

A 512-bit ZMM register can hold:

Data type	Elements per ZMM register
8-bit integers	64
16-bit integers	32
32-bit integers	16
64-bit integers	8
32-bit floats	16
64-bit doubles	8

AVX-512 also introduced mask registers:

k0 k1 k2 k3 k4 k5 k6 k7

These mask registers allow predicated operations, meaning each lane can be conditionally written without needing separate blend instructions.

AVX-512 is important for:

• high-performance computing;
• scientific simulations;
• AI inference;
• data compression;
• database processing;
• media processing;
• cryptography;
• genomics;
• large-scale analytics.

AVX-512 Variants

AVX-512 is made of many feature subsets. A CPU can support some subsets and not others.

This is one of the reasons AVX-512 software often needs careful runtime dispatch.

Core AVX-512 subsets

Extension	Description
AVX-512F	Foundation; required base for AVX-512 implementations
AVX-512CD	Conflict detection
AVX-512ER	Exponential and reciprocal instructions, mainly Xeon Phi
AVX-512PF	Prefetch instructions, mainly Xeon Phi
AVX-512DQ	Doubleword and quadword instructions
AVX-512BW	Byte and word instructions
AVX-512VL	Allows many AVX-512 instructions to operate on 128-bit and 256-bit vectors

Integer, byte, and bit manipulation subsets

Extension	Description
AVX-512IFMA	Integer fused multiply-add
AVX-512VBMI	Vector byte manipulation
AVX-512VBMI2	Additional byte/word manipulation
AVX-512BITALG	Bit algorithms
AVX-512VPOPCNTDQ	Vector population count for doubleword/quadword elements

AI and numerical-format subsets

Extension	Description
AVX-512VNNI	Vector neural-network instructions
AVX-512BF16	bfloat16 dot-product and conversion support
AVX-512FP16	Half-precision floating-point arithmetic

Crypto and Galois-field related vector extensions

Extension	Description
VAES	Vector AES
VPCLMULQDQ	Vector carry-less multiply
GFNI	Galois Field New Instructions

These may be available with AVX, AVX2, or AVX-512 encodings depending on the processor.

Specialized or limited AVX-512 subsets

Extension	Description
AVX-5124VNNIW	Xeon Phi-oriented neural-network instructions
AVX-5124FMAPS	Xeon Phi-oriented fused multiply-accumulate instructions
AVX-512VP2INTERSECT	Vector pair intersection

Some of these appeared only in narrow product families or were not widely adopted.

AVX-512 on Intel CPUs

Intel introduced AVX-512 first in Xeon Phi and then in Xeon server and high-end desktop processors.

A simplified Intel AVX-512 map:

Intel generation	AVX-512 status
Xeon Phi Knights Landing	Early AVX-512: F, CD, ER, PF
Xeon Phi Knights Mill	Added specialized AI/HPC subsets such as 4VNNIW and 4FMAPS
Skylake-X / Skylake-SP	AVX-512F, CD, BW, DQ, VL
Cascade Lake Xeon	Added AVX-512VNNI
Cooper Lake Xeon	Added AVX-512BF16
Ice Lake client/server	Broader AVX-512 support including VNNI and byte/bit manipulation subsets
Tiger Lake	Client AVX-512 on many SKUs
Rocket Lake	Desktop AVX-512 on supported SKUs
Alder Lake and later mainstream hybrid client CPUs	AVX-512 not officially supported
Sapphire Rapids Xeon	AVX-512 plus AMX, BF16, FP16 support
Emerald Rapids Xeon	Similar server-class AVX-512/AMX direction
Granite Rapids / Xeon 6 P-core	Server/workstation AVX-512 and transition toward AVX10
Xeon 6 E-core-only lines	AVX-512 support is not the same as P-core Xeon; check SKU documentation

The most important practical point is that Intel client CPUs and Intel server CPUs diverged.

For several years, high-end Intel servers had strong AVX-512 support while many mainstream client CPUs did not.

AVX-512 on AMD CPUs

AMD did not support AVX-512 in Zen, Zen+, Zen 2, or Zen 3.

AMD added AVX-512 support with Zen 4.

A simplified AMD AVX-512 map:

AMD generation	AVX-512 status
Zen / Zen+	No AVX-512
Zen 2	No AVX-512
Zen 3	No AVX-512
Zen 4	AVX-512 support added
Zen 5 / EPYC Turin	AVX-512 support continues and becomes stronger

AMD Zen 4 processors support a practical subset of AVX-512 that includes important features such as AVX-512F, DQ, IFMA, CD, BW, VL, VBMI, VNNI, BITALG, VPOPCNTDQ, BF16, and related vector crypto/Galois-field extensions depending on the exact model.

Zen 5, including 5th Gen AMD EPYC Turin, continues the AVX-512 direction and is especially relevant for HPC, AI, analytics, and cloud workloads.

The important lesson is that AVX-512 is no longer Intel-only. Modern portable high-performance code should consider AVX-512 dispatch paths for both recent Intel server CPUs and recent AMD Zen 4 / Zen 5 CPUs.

AVX10

AVX10 is Intel’s attempt to simplify the future of the AVX family.

The problem with AVX-512 is fragmentation. There are many feature bits, and software has to check which subset is available.

AVX10 moves toward a versioned model.

Instead of thinking only in terms of dozens of separate AVX-512 feature flags, AVX10 introduces an AVX10 version number.

The idea is:

AVX10.1
AVX10.2
future AVX10 versions

A later version is expected to include the earlier version’s capabilities.

AVX10 is also designed to make the AVX-512-style programming model available across future Intel P-core and E-core processors.

Important AVX10 points:

• AVX10 is based on the AVX-512 programming model.
• It uses versioned enumeration.
• It is intended to reduce feature-detection complexity.
• Future new Intel vector instructions are expected to be enumerated under AVX10 rather than by adding more AVX-512 feature flags.
• AVX10.1 is a transition version.
• AVX10.2 adds new instructions, including AI data type and conversion support.

For developers, AVX10 is important because it points to the future direction of Intel vector programming.

However, for software that must run on existing machines, AVX2 and AVX-512 runtime dispatch remain essential.

AMX

AMX stands for:

Advanced Matrix Extensions

AMX is not traditional SIMD. It introduces tile registers and tile operations designed for matrix multiplication and AI workloads.

Important AMX subsets include:

Extension	Description
AMX-TILE	Tile register architecture
AMX-INT8	8-bit integer matrix operations
AMX-BF16	bfloat16 matrix operations
AMX-FP16	FP16 matrix operations on newer/future processors
AMX-COMPLEX	Complex-number tile operations on newer/future processors

AMX is especially relevant for:

• neural-network inference;
• matrix multiplication;
• deep learning kernels;
• server-side AI;
• dense numerical compute.

Intel introduced AMX in Sapphire Rapids Xeon processors.

AMX requires operating system support because it adds new architectural state. Like AVX and AVX-512, detecting the CPU feature alone is not enough; the OS must support saving and restoring the state.

ACE: AI Compute Extensions

ACE, or AI Compute Extensions, is a newer x86 ecosystem direction jointly associated with Intel and AMD.

ACE is focused on AI-oriented matrix acceleration and reduced-precision numerical formats. It is intended to provide a more consistent cross-vendor target for future x86 AI workloads.

ACE is not a replacement for SSE, AVX, or AVX-512. It belongs to the same broader evolution: the CPU is gaining more native support for dense data-parallel and matrix-heavy workloads.

For the purpose of a SIMD map, ACE should be considered a future-facing matrix/AI extension rather than a classic lane-based SIMD family.

Intel Processor Generations and SIMD Support

The following table summarizes major Intel generations and their most important SIMD support.

This table is intentionally practical rather than exhaustive. Some product lines, steppings, low-end SKUs, embedded parts, and disabled features differ.

Intel processor generation	Approx. era	Important SIMD support
Pentium	1993	No MMX in original Pentium
Pentium MMX	1997	MMX
Pentium II	1997	MMX
Original Celeron	1998	MMX
Pentium III	1999	MMX, SSE
Celeron II	2000	MMX, SSE
Pentium 4 Willamette / Northwood	2000-2002	MMX, SSE, SSE2
Pentium 4 Prescott	2004	MMX, SSE, SSE2, SSE3
Pentium D	2005	SSE2, SSE3
Pentium M	2003-2005	SSE2; later models added SSE3
Core Solo / Core Duo	2006	SSE2, SSE3
Core 2 Merom / Conroe	2006	SSE2, SSE3, SSSE3
Core 2 Penryn	2007	SSSE3, SSE4.1
Nehalem	2008	SSE4.1, SSE4.2
Westmere	2010	SSE4.2, AES-NI, PCLMULQDQ
Sandy Bridge	2011	AVX
Ivy Bridge	2012	AVX, F16C
Haswell	2013	AVX2, FMA3
Broadwell	2014-2015	AVX2, FMA3
Skylake client	2015	AVX2, FMA3
Skylake-X / Skylake-SP	2017	AVX-512F, CD, BW, DQ, VL
Kaby Lake / Coffee Lake / Comet Lake	2016-2020	AVX2, FMA3
Cannon Lake	2018	AVX-512 on limited client products
Cascade Lake Xeon	2019	AVX-512VNNI
Cooper Lake Xeon	2020	AVX-512BF16
Ice Lake client/server	2019-2021	Broad AVX-512 support on many SKUs
Tiger Lake	2020	AVX-512 on many client SKUs
Rocket Lake	2021	AVX-512 on supported desktop SKUs
Alder Lake	2021	AVX2, FMA3, AVX-VNNI on many SKUs; AVX-512 not officially supported
Raptor Lake	2022-2023	AVX2, FMA3, AVX-VNNI on many SKUs; no official AVX-512
Sapphire Rapids Xeon	2023	AVX-512, BF16, FP16, AMX
Emerald Rapids Xeon	2023	AVX-512, AMX server-class support
Meteor Lake / Core Ultra Series 1	2023-2024	AVX2/FMA-class client SIMD; no mainstream AVX-512
Sierra Forest Xeon 6 E-core	2024	E-core server line; AVX-512 support differs from P-core Xeon
Granite Rapids Xeon 6 P-core	2024-2025	AVX-512, AMX, AVX10 transition generation
Arrow Lake / Core Ultra 200	2024-2025	AVX2/FMA-class client SIMD; no mainstream AVX-512
Lunar Lake / Core Ultra 200V	2024-2025	AVX2/FMA-class client SIMD; AI acceleration also via NPU
Xeon 600 workstation / Xeon 6 workstation	2026	Server/workstation-class AVX-512 and AMX on P-core products
Panther Lake / Core Ultra Series 3	2026 generation	Check final SKU documentation; client hybrid direction, not a simple AVX-512 baseline

AMD Processor Generations and SIMD Support

The following table summarizes major AMD generations and their most important SIMD support.

Again, check CPUID for exact systems.

AMD processor generation	Approx. era	Important SIMD support
K5	1996	No MMX baseline
K6	1997	MMX
K6-2	1998	MMX, 3DNow!
K6-III	1999	MMX, 3DNow!
Athlon	1999	MMX, Enhanced 3DNow!, Extended MMX
Duron	2000	MMX, Enhanced 3DNow!, Extended MMX
Athlon XP	2001	MMX, Enhanced 3DNow!, SSE
Athlon 64 / Opteron	2003	SSE, SSE2; later revisions added SSE3
Sempron 64	2004	SSE, SSE2; later SSE3 depending model
K10 / Barcelona / Phenom	2007	SSE3, SSE4a, 3DNow! legacy support
Phenom II / Athlon II	2008-2009	SSE3, SSE4a
Bobcat	2011	SSE2/SSE3/SSSE3-class low-power support depending model
Bulldozer	2011	AVX, SSE4.1, SSE4.2, XOP, FMA4
Piledriver	2012	AVX, FMA3, FMA4, F16C, XOP
Steamroller	2014	AVX/FMA-class Bulldozer-family SIMD
Excavator	2015	AVX2 support in some products
Jaguar / Puma	2013-2014	SSE4.x/AVX-class low-power SIMD, depending model
Zen	2017	SSE4.2, AVX, AVX2, FMA3, AES, F16C
Zen+	2018	Similar to Zen
Zen 2	2019	AVX2, FMA3; no AVX-512
Zen 3	2020	AVX2, FMA3; no AVX-512
Zen 4	2022	AVX-512 support added
Zen 4c	2023	AVX-512 support in dense-core server/client variants where exposed
Zen 5	2024	AVX-512 support continues; stronger vector capability
5th Gen EPYC Turin	2024-2026	AVX-512, including BF16/FP16-related support depending SKU/platform
Future Zen generations	2026+	Check AMD documentation and CPUID; ACE/AVX10 ecosystem direction may matter in future

Practical SIMD Baselines for Software

The best SIMD target depends on what kind of software you are writing.

If you need maximum compatibility

Use scalar code plus SSE2.

SSE2 is a safe baseline for x86-64 and works on a very wide range of Intel and AMD processors.

Good for:

• general-purpose libraries;
• small utilities;
• long-tail compatibility;
• software that must run on old machines.

If you target reasonably modern desktops and laptops

Use AVX2 plus a fallback.

AVX2 is a strong practical baseline for many modern machines from the last decade.

Good for:

• image processing;
• compression;
• video processing;
• game engines;
• data scanning;
• numerical kernels;
• DSP;
• high-performance C/C++ libraries.

If you target recent servers or high-performance workstations

Use AVX-512 with runtime dispatch.

AVX-512 can provide significant gains, but support varies across generations and vendors.

Good for:

• HPC;
• machine learning;
• analytics;
• genomics;
• cryptography;
• compression;
• vectorized database operations;
• scientific computing.

If you target AI matrix workloads on recent Intel Xeon

Consider AMX.

AMX is not a general replacement for AVX-512. It is especially useful for matrix multiplication and deep learning kernels.

Good for:

• neural-network inference;
• BF16 matrix multiplication;
• INT8 inference;
• server-side AI.

If you target future Intel vector code

Track AVX10.

AVX10 is designed to simplify the future AVX programming model, but existing deployed systems still require AVX2 and AVX-512 dispatch paths.

Why Width Is Not Everything

It is tempting to rank SIMD instruction sets only by vector width:

MMX    = 64-bit
SSE    = 128-bit
AVX2   = 256-bit
AVX-512 = 512-bit

But real performance depends on much more than width.

Important factors include:

• instruction latency;
• instruction throughput;
• number of execution ports;
• load/store bandwidth;
• cache behavior;
• memory alignment;
• downclocking behavior;
• register pressure;
• compiler quality;
• data layout;
• branch behavior;
• whether the workload is compute-bound or memory-bound.

A 512-bit instruction is not automatically twice as fast as a 256-bit instruction. If the workload is memory-bound, wider vectors may not help much. If the CPU executes a 512-bit operation internally as multiple narrower operations, peak throughput may be different from the architectural width.

Always benchmark on the target CPU.

Data Layout Matters

SIMD code works best when data is arranged in a vector-friendly layout.

For example, consider pixels stored as:

RGB RGB RGB RGB

This is convenient for scalar code, but it can be awkward for SIMD code if you want to process all red values together, then all green values, then all blue values.

A SIMD-friendly layout may look like this:

RRRR GGGG BBBB

or use separate arrays:

R[] G[] B[]

This is the classic difference between:

Array of Structures

and:

Structure of Arrays

Instruction set support matters, but data layout often matters just as much.

Runtime Dispatch Strategy

A good modern x86 library often contains multiple implementations of the same hot loop.

For example:

scalar
SSE2
SSSE3 or SSE4.1
AVX2
AVX-512
AMX or specialized AI path

At startup or first use, the library checks CPU features and selects the best implementation.

A simplified dispatch order might be:

if AMX and workload is matrix-heavy:
    use AMX path
else if AVX-512 suitable subset is available:
    use AVX-512 path
else if AVX2 and FMA are available:
    use AVX2/FMA path
else if SSE4.1 is available:
    use SSE4.1 path
else if SSSE3 is available:
    use SSSE3 path
else if SSE2 is available:
    use SSE2 path
else:
    use scalar path

For AVX and later, remember that CPU support alone is not enough. The operating system must support the extended register state.

Recommended Feature Checks

For old SSE code, checking the CPUID feature bit is usually enough.

For AVX and later, check both the CPU and the OS.

A practical checklist:

Feature family	What to check
MMX	CPUID MMX
SSE	CPUID SSE
SSE2	CPUID SSE2
SSE3/SSSE3/SSE4.x	CPUID feature bits
AVX	CPUID AVX, OSXSAVE, XGETBV for XMM/YMM state
AVX2	AVX checks plus CPUID AVX2
FMA/F16C	AVX checks plus CPUID FMA/F16C
AVX-512	AVX checks plus CPUID AVX-512 bits plus XGETBV for opmask/ZMM state
AMX	CPUID AMX bits plus OS support for tile state
AVX10	CPUID AVX10 bit and AVX10 version enumeration

Do not assume that a CPU family name is enough.

Complete SIMD Family Map

The following table summarizes the major x86 SIMD and SIMD-adjacent instruction-set families.

Instruction set	Vendor origin	Register width/model	Main purpose	Modern relevance
MMX	Intel	64-bit MMX	Packed integer multimedia	Legacy
Extended MMX / MMX+	AMD / SSE-era	64-bit MMX	Extra integer/media operations	Legacy
3DNow!	AMD	64-bit MMX	Packed floating-point	Obsolete
Enhanced 3DNow!	AMD	64-bit MMX	More media/DSP operations	Obsolete
SSE	Intel	128-bit XMM	Packed single-precision FP	Historical baseline
SSE2	Intel	128-bit XMM	Integer and double FP SIMD	x86-64 baseline
SSE3	Intel	128-bit XMM	Horizontal ops, complex arithmetic helpers	Common
SSSE3	Intel	128-bit XMM	Byte shuffle, integer media ops	Very useful
SSE4a	AMD	128-bit XMM	Small AMD-specific extension	Niche
SSE4.1	Intel	128-bit XMM	Blends, dot product, rounding, media ops	Useful
SSE4.2	Intel	128-bit XMM	Text/string compare, CRC32	Common
AES-NI	Intel	XMM-based	AES crypto acceleration	Very important
PCLMULQDQ	Intel	XMM-based	Carry-less multiply	Very important
AVX	Intel	256-bit YMM	Wider FP SIMD, VEX encoding	Common
F16C	Intel	XMM/YMM	FP16/FP32 conversion	Common
FMA3	Intel/AMD	XMM/YMM	Fused multiply-add	Common
FMA4	AMD	XMM/YMM	Four-operand FMA	Historical
XOP	AMD	XMM/YMM	AMD-specific vector ops	Historical
AVX2	Intel	256-bit YMM	256-bit integer SIMD	Modern baseline
AVX-VNNI	Intel	XMM/YMM	Neural-network dot products	Newer client/server
AVX-IFMA	Intel	XMM/YMM	Integer multiply-add	Specialized
AVX-VNNI-INT8	Intel	XMM/YMM	INT8 AI operations	New/future-facing
AVX-VNNI-INT16	Intel	XMM/YMM	INT16 AI operations	New/future-facing
AVX-NE-CONVERT	Intel	XMM/YMM	Low-precision conversion	New/future-facing
AVX-512F	Intel	512-bit ZMM	AVX-512 foundation	Server/HPC/AI
AVX-512CD	Intel	512-bit ZMM	Conflict detection	Server/HPC
AVX-512ER	Intel	512-bit ZMM	Exp/reciprocal, Xeon Phi	Limited
AVX-512PF	Intel	512-bit ZMM	Prefetch, Xeon Phi	Limited
AVX-512DQ	Intel	512-bit ZMM	Dword/qword operations	Common AVX-512 subset
AVX-512BW	Intel	512-bit ZMM	Byte/word operations	Common AVX-512 subset
AVX-512VL	Intel	128/256-bit forms of AVX-512 ops	Makes AVX-512 more flexible	Important
AVX-512IFMA	Intel	512-bit ZMM	Integer fused multiply-add	Crypto/bignum
AVX-512VBMI	Intel	512-bit ZMM	Byte manipulation	Text/media
AVX-512VBMI2	Intel	512-bit ZMM	More byte/word manipulation	Text/media
AVX-512VNNI	Intel	512-bit ZMM	Neural-network inference	AI
AVX-512BITALG	Intel	512-bit ZMM	Bit algorithms	Specialized
AVX-512VPOPCNTDQ	Intel	512-bit ZMM	Vector popcount	Data/search/analytics
AVX-512BF16	Intel/AMD	512-bit ZMM	bfloat16 operations	AI
AVX-512FP16	Intel/AMD	512-bit ZMM	FP16 arithmetic	AI/HPC/media
AVX-512VP2INTERSECT	Intel	512-bit ZMM	Vector pair intersection	Limited
AVX-5124VNNIW	Intel	512-bit ZMM	Xeon Phi AI instructions	Historical/limited
AVX-5124FMAPS	Intel	512-bit ZMM	Xeon Phi FMA instructions	Historical/limited
VAES	Intel/AMD	XMM/YMM/ZMM depending CPU	Vector AES	Crypto
VPCLMULQDQ	Intel/AMD	XMM/YMM/ZMM depending CPU	Vector carry-less multiply	Crypto
GFNI	Intel/AMD	XMM/YMM/ZMM depending CPU	Galois-field operations	Crypto/coding
AVX10.1	Intel	AVX-512-style versioned ISA	Transition from AVX-512	Future/current transition
AVX10.2	Intel	AVX10 versioned ISA	New AI/data movement/conversion ops	Future-facing
AMX-TILE	Intel	Tile state	Matrix/tile base	AI/server
AMX-INT8	Intel	Tile state	INT8 matrix operations	AI/server
AMX-BF16	Intel	Tile state	BF16 matrix operations	AI/server
AMX-FP16	Intel	Tile state	FP16 matrix operations	Newer/future server
ACE	Intel/AMD ecosystem	Matrix/AVX10-related model	AI matrix acceleration	Future-facing

Practical Recommendations

For old code

If the code uses MMX or 3DNow!, consider rewriting it using SSE2 or AVX2.

MMX and 3DNow! were important historically, but they are poor targets for modern code.

For portable 64-bit x86 code

Use SSE2 as the minimum SIMD baseline.

SSE2 is available on x86-64 and avoids the old MMX/x87 state-sharing problem.

For modern desktop software

Use AVX2 and FMA when available.

AVX2 is widely supported across Intel Haswell-and-newer and AMD Zen-and-newer processors.

For high-performance server software

Add AVX-512 dispatch paths.

Recent Intel Xeon and AMD EPYC processors can benefit significantly from AVX-512, especially for compute-heavy workloads.

For AI and matrix-heavy workloads

Consider AMX on supported Intel Xeon processors, and track future ACE developments.

For AMD Zen 4 and Zen 5, AVX-512 is the important CPU-side vector path today.

For future Intel vector code

Track AVX10.

AVX10 is intended to reduce fragmentation and provide a more consistent future AVX programming model.

Summary

The x86 SIMD map has grown from a simple MMX/SSE/3DNow! table into a complex family tree.

The big historical steps are:

1. MMX introduced packed integer SIMD on x86.
2. 3DNow! gave AMD an early packed floating-point SIMD path.
3. SSE introduced 128-bit XMM registers.
4. SSE2 made 128-bit integer and double-precision SIMD central to x86-64.
5. SSSE3 and SSE4.x added many practical media, text, and integer operations.
6. AVX introduced 256-bit registers and better instruction encoding.
7. AVX2 made 256-bit integer SIMD broadly useful.
8. FMA and F16C improved numerical and conversion-heavy workloads.
9. AVX-512 introduced 512-bit vectors, mask registers, and many specialized subsets.
10. AMX added matrix/tile acceleration for AI workloads.
11. AVX10 points toward a more unified future Intel vector ISA.
12. ACE points toward future cross-vendor AI matrix acceleration.

For developers, the most important practical lesson is simple:

Do not choose a SIMD path from the processor name alone. Detect the actual instruction sets at runtime.

For broad compatibility, start with scalar and SSE2.

For modern performance, add AVX2.

For recent servers and high-end compute, add AVX-512.

For AI matrix workloads, consider AMX where available.

And for future Intel platforms, keep an eye on AVX10.

References

• Original article: Map of Instruction sets / CPU
• Intel Intrinsics Guide
• Intel 64 and IA-32 Architectures Software Developer’s Manual
• Intel Architecture Instruction Set Extensions and Future Features Programming Reference
• Intel AVX-512 Instructions
• Intel AVX10 Technical Paper: The Converged Vector ISA
• Intel Xeon 6 Product Brief
• AMD64 Architecture Programmer’s Manual
• AMD Software Optimization Guide for the Zen 4 Microarchitecture
• AMD Software Optimization Guide for the Zen 5 Microarchitecture
• AMD: Understanding AVX-512 and Validating Usage on AMD EPYC
• x86 AI Compute Extensions ACE Whitepaper
• Agner Fog: Instruction Tables and Optimization Manuals
• GCC x86 Options
• LLVM X86 Backend Documentation