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Ampere (microarchitecture)

GPU microarchitecture by Nvidia

Wikipedia contributorsAdapted for Fathomly · View authors
8 min read
Source updated September 1, 2026

Ampere is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020, and is named after French mathematician and physicist André-Marie Ampère.

Nvidia announced the Ampere architecture GeForce 30 series consumer GPUs at a GeForce Special Event on September 1, 2020. Nvidia announced the A100 80 GB GPU at SC20 on November 16, 2020. Mobile RTX graphics cards and the RTX 3060 based on the Ampere architecture were revealed on January 12, 2021.

Nvidia announced Ampere's successor, Hopper, at GTC 2022, and "Ampere Next Next" (Blackwell) for a 2024 release at GPU Technology Conference 2021.

01Details

Architectural improvements of the Ampere architecture include the following:

  • CUDA Compute Capability 8.0 for A100 and 8.6 for the GeForce 30 series
  • TSMC's 7 nm FinFET process for A100
  • Custom version of Samsung's 8 nm process (8N) for the GeForce 30 series
  • Third-generation Tensor Cores with FP16, bfloat16, TensorFloat-32 (TF32) and FP64 support and sparsity acceleration. The individual Tensor cores have with 256 FP16 FMA operations per clock 4x processing power (GA100 only, 2x on GA10x) compared to previous Tensor Core generations; the Tensor Core Count is reduced to one per SM.
  • Second-generation ray tracing cores; concurrent ray tracing, shading, and compute for the GeForce 30 series
  • High Bandwidth Memory 2 (HBM2) on A100 40 GB & A100 80 GB
  • GDDR6X memory for GeForce RTX 3090, RTX 3080 Ti, RTX 3080, RTX 3070 Ti
  • Double FP32 cores per SM on GA10x GPUs
  • NVLink 3.0 with a 50 Gbit/s per pair throughput
  • PCI Express 4.0 with SR-IOV support (SR-IOV is reserved only for A100)
  • Multi-instance GPU (MIG) virtualization and spatial GPU partitioning feature in A100 supporting up to seven instances
  • PureVideo feature set K hardware video decoding with AV1 hardware decoding for the GeForce 30 series and feature set J for A100
  • 5 NVDEC for A100
  • Adds new hardware-based 5-core JPEG decode (NVJPG) with YUV420, YUV422, YUV444, YUV400, RGBA. Should not be confused with Nvidia NVJPEG (GPU-accelerated library for JPEG encoding/decoding)

Chips

  • GA100
  • GA102
  • GA103
  • GA104
  • GA106
  • GA107
  • GA10B

Comparison of Compute Capability: GP100 vs GV100 vs GA100

GPU features Nvidia Tesla P100 Nvidia Tesla V100 Nvidia A100
GPU codename GP100 GV100 GA100
GPU architecture Pascal Volta Ampere
Compute capability 6.0 7.0 8.0
Threads / warp 32 32 32
Max warps / SM 64 64 64
Max threads / SM 2048 2048 2048
Max thread blocks / SM 32 32 32
Max 32-bit registers / SM 65536 65536 65536
Max registers / block 65536 65536 65536
Max registers / thread 255 255 255
Max thread block size 1024 1024 1024
FP32 cores / SM 64 64 64
Ratio of SM registers to FP32 cores 1024 1024 1024
Shared Memory Size / SM 64 KB Configurable up to 96 KB Configurable up to 164 KB

Comparison of Precision Support Matrix

Supported CUDA Core Precisions Supported Tensor Core Precisions
FP16 FP32 FP64 INT1 INT4 INT8 TF32 BF16 FP16 FP32 FP64 INT1 INT4 INT8 TF32 BF16
Nvidia Tesla P4 NoYesYesNoNoYesNoNoNoNoNoNoNoNoNoNo
Nvidia P100 YesYesYesNoNoNoNoNoNoNoNoNoNoNoNoNo
Nvidia Volta YesYesYesNoNoYesNoNoYesNoNoNoNoNoNoNo
Nvidia Turing YesYesYesNoNoNoNoNoYesNoNoYesYesYesNoNo
Nvidia A100 YesYesYesNoNoYesNoYesYesNoYesYesYesYesYesYes

Legend:

  • FPnn: floating point with nn bits
  • INTn: integer with n bits
  • INT1: binary
  • TF32: TensorFloat32
  • BF16: bfloat16

Comparison of Decode Performance

Concurrent streams H.264 decode (1080p30) H.265 (HEVC) decode (1080p30) VP9 decode (1080p30)
V100 16 22 22
A100 75 157 108

02Ampere dies

Die GA100 GA102 GA103 GA104 GA106 GA107 GA10B GA10F
Die size 826 mm2 628 mm2 496 mm2 392 mm2 276 mm2 200 mm2 448 mm2 ?
Transistors 54.2B 28.3B 22B 17.4B 12B 8.7B 21B ?
Transistor density 65.6 MTr/mm2 45.1 MTr/mm2 44.4 MTr/mm2 44.4 MTr/mm2 43.5 MTr/mm2 43.5 MTr/mm2 46.9 MTr/mm2 ?
Graphics processing clusters 8 7 6 6 3 2 2 1
Streaming multiprocessors 128 84 60 48 30 20 16 12
CUDA cores 8192 10752 7680 6144 3840 2560 2048 1536
Texture mapping units 512 336 240 192 120 80 64 48
Render output units 192 112 96 96 48 32 32 16
Tensor cores 512 336 240 192 120 80 64 48
RT cores N/A 84 60 48 30 20 8 12
L1 cache 24 MB 10.5 MB 7.5 MB 6 MB 3 MB 2.5 MB 3 MB 1.5 MB
192 KB
per SM		 128 KB per SM 192 KB
per SM		 128 KB
per SM
L2 cache 40 MB 6 MB 4 MB 4 MB 3 MB 2 MB 4 MB 1 MB

03A100 accelerator and DGX A100

The Ampere-based A100 accelerator was announced and released on May 14, 2020. The A100 features 19.5 teraflops of FP32 performance, 6912 FP32/INT32 CUDA cores, 3456 FP64 CUDA cores, 40 GB of graphics memory, and 1.6 TB/s of graphics memory bandwidth. The A100 accelerator was initially available only in the 3rd generation of DGX server, including 8 A100s. Also included in the DGX A100 is 15 TB of PCIe gen 4 NVMe storage, two 64-core AMD Rome 7742 CPUs, 1 TB of RAM, and Mellanox-powered HDR InfiniBand interconnect. The initial price for the DGX A100 was $199,000.

Starting from P100, to V100, to A100, to H100, to B200 and to R100; the comparison of accelerators used in DGX:

General & Architecture

Model Architecture Socket GPU Fabrication Process Transistor count

(billion)

 Die size

(mm2)

 Launched
P100 Pascal SXM/SXM2 GP100 TSMC 16FF+ 15.3 610 Q2 2016
V100 16GB Volta SXM2 GV100 TSMC 12FFN 21.1 815 Q3 2017
V100 32GB SXM3
A100 40GB Ampere SXM4 GA100 TSMC N7 54.2 826 Q1 2020
A100 80GB Q4 2020
H100 Hopper SXM5 GH100 TSMC 4N 80 814 Q3 2022
H200 Q3 2023
B100 Blackwell SXM6 GB100 TSMC 4NP 208 N/A Q4 2024
B200
R100 Rubin SXM7, N/a TSMC 3N 338, N/a H2 2026

Cores, Clock & Power

Model Boost clock

(MHz)

 #SM Cores

(FP32 CUDA)

 Cores

(FP64 excl. tensor)

 Cores

(Mixed INT32/FP32)

 Cores

(INT32)

 TDP

(W)

P100 1480 56 3584 1792, N/a, N/a 300
V100 16GB 1530 80 5120 2560 N/A 5120 300
V100 32GB 350
A100 40GB 1410 108 6912 3456 6912 N/A 400
A100 80GB
H100 1980 132 16896 4608 16896 N/A 700
H200 1000
B100 , N/a, N/a, N/a, N/a, N/a, N/a 700
B200 , N/a, N/a, N/a, N/a, N/a, N/a 1000
R100 , N/a, N/a, N/a, N/a, N/a, N/a 2300

Memory & Cache

Model Memory Type

(HBM)

 VRAM Size

(GB)

 Memory Speed

(Gb/s)

 Bus width

(bits)

 Bandwidth

(TB/s)

 L1 Cache

Per SM (KB)

 L1 Cache

Total (KB)

 L2 Cache

(KB)

P100 HBM2 16 1.4 4096 0.72 24 1344 4096
V100 16GB HBM2 16 1.75 4096 0.9 128 10240 6144
V100 32GB 32
A100 40GB HBM2 40 2.4 5120 1.52 192 20736 40960
A100 80GB HBM2e 80 3.2
H100 HBM3 80 5.2 5120 3.35 192 25344 51200
H200 HBM3e 141 6.3 6144 4.8
B100 HBM3e 192 8 8192 8 N/A N/A N/A
B200
R100 HBM4, N/a, N/a, N/a, N/a, N/a, N/a, N/a

Compute Performance, Interconnect & Networking

Model FP32

(TFLOPS)

 FP64

(TFLOPS)

 INT8

dense tensor

 FP16

dense tensor

 bfloat16

dense tensor

 TF32

dense tensor

 FP64

dense tensor

 Interconnect

(NVLink; TB/s)

 Networking
P100 10.6 5.3, N/a 21.2, N/a, N/a, N/a 0.16 ConnectX-4

(100 Gb/s)

V100 16GB 15.7 7.8 N/A 125 TFLOPS N/A N/A N/A 0.3 ConnectX-5

(100 Gb/s)

V100 32GB
A100 40GB 19.5 9.7 624 TOPS 312 TFLOPS 312 TFLOPS 156 TFLOPS 19.5 TFLOPS 0.6 ConnectX-6

(200 Gb/s)

A100 80GB
H100 67 34 1.98 POPS 990 TFLOPS 990 TFLOPS 495 TFLOPS 67 TFLOPS 0.9 ConnectX-7

(400 Gb/s)

H200
B100 , N/a, N/a 3.5 POPS 1.98 PFLOPS 1.98 PFLOPS 989 TFLOPS 30 TFLOPS 1.8 ConnectX-7

(400 Gb/s)

B200 , N/a, N/a 4.5 POPS 2.25 PFLOPS 2.25 PFLOPS 1.2 PFLOPS 40 TFLOPS
R100 , N/a, N/a, N/a, N/a, N/a, N/a, N/a, N/a ConnectX-9

(1600 Gb/s)

04Products using Ampere

  • GeForce MX series
    • GeForce MX570 (mobile) (GA107)
  • GeForce 20 series
    • GeForce RTX 2050 (mobile) (GA107)
  • GeForce 30 series
    • GeForce RTX 3050 Laptop GPU (GA107)
    • GeForce RTX 3050 (GA106 or GA107)
    • GeForce RTX 3050 Ti Laptop GPU (GA107)
    • GeForce RTX 3060 Laptop GPU (GA106)
    • GeForce RTX 3060 (GA106 or GA104)
    • GeForce RTX 3060 Ti (GA104 or GA103)
    • GeForce RTX 3070 Laptop GPU (GA104)
    • GeForce RTX 3070 (GA104)
    • GeForce RTX 3070 Ti Laptop GPU (GA104)
    • GeForce RTX 3070 Ti (GA104 or GA102)
    • GeForce RTX 3080 Laptop GPU (GA104)
    • GeForce RTX 3080 (GA102)
    • GeForce RTX 3080 12 GB (GA102)
    • GeForce RTX 3080 Ti Laptop GPU (GA103)
    • GeForce RTX 3080 Ti (GA102)
    • GeForce RTX 3090 (GA102)
    • GeForce RTX 3090 Ti (GA102)
  • Nvidia Workstation GPUs (formerly Quadro)
    • RTX A1000 (mobile) (GA107)
    • RTX A2000 (mobile) (GA106)
    • RTX A2000 (GA106)
    • RTX A3000 (mobile) (GA104)
    • RTX A4000 (mobile) (GA104)
    • RTX A4000 (GA104)
    • RTX A5000 (mobile) (GA104)
    • RTX A5500 (mobile) (GA103)
    • RTX A4500 (GA102)
    • RTX A5000 (GA102)
    • RTX A5500 (GA102)
    • RTX A6000 (GA102)
    • A800 Active
  • Nvidia Data Center GPUs (formerly Tesla)
    • Nvidia A2 (GA107)
    • Nvidia A10 (GA102)
    • Nvidia A16 (4 × GA107)
    • Nvidia A30 (GA100)
    • Nvidia A40 (GA102)
    • Nvidia A100 (GA100)
    • Nvidia A100 80 GB (GA100)
    • Nvidia A100X
    • NVIDIA A30X
Products using Ampere (per Chip)
TypeGA10BGA107GA106GA104GA103GA102GA100
GeForce MX series , N/aGeForce MX570 (mobile), N/a, N/a, N/a, N/a, N/a
GeForce 20 series , N/aGeForce RTX 2050 (mobile), N/a, N/a, N/a, N/a, N/a
GeForce 30 series , N/aGeForce RTX 3050 Laptop
GeForce RTX 3050
GeForce RTX 3050 Ti Laptop		 GeForce RTX 3050
GeForce RTX 3060 Laptop
GeForce RTX 3060		 GeForce RTX 3060
GeForce RTX 3060 Ti
GeForce RTX 3070 Laptop
GeForce RTX 3070
GeForce RTX 3070 Ti Laptop
GeForce RTX 3070 Ti
GeForce RTX 3080 Laptop		 GeForce RTX 3060 Ti
GeForce RTX 3080 Ti Laptop		 GeForce RTX 3070 Ti
GeForce RTX 3080
GeForce RTX 3080 Ti
GeForce RTX 3090
GeForce RTX 3090 Ti		 , N/a
Nvidia Workstation GPUs , N/aRTX A1000 (mobile)RTX A2000 (mobile)
RTX A2000		RTX A3000 (mobile)
RTX A4000 (mobile)
RTX A4000
RTX A5000 (mobile)		RTX A5500 (mobile)RTX A4500
RTX A5000
RTX A5500
RTX A6000		, N/a
Nvidia Data Center GPUs , N/aNvidia A2
Nvidia A16		, N/a, N/a, N/aNvidia A10
Nvidia A40		Nvidia A30
Nvidia A100
Tegra SoCs AGX Orin
Orin NX
Orin Nano		, N/a, N/a, N/a, N/a, N/a, N/a
Watch videos about Ampere (microarchitecture)Explainers and documentaries on YouTube (opens in a new tab)

Sources and credits

This article is adapted from the Wikipedia article “Ampere (microarchitecture)”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.

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