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Mapped: The Fastest (and Slowest) Internet Speeds in the World



worlds fastest and slowest internet speeds

Mapped: The World’s Fastest (and Slowest) Internet Speeds

How quickly did this page load for you?

The answer depends on the device you’re using, and where in the world you’re located. Average internet speeds vary wildly from country to country.

Which countries have the fastest internet connection? Using data from the, this map ranks the fastest (and slowest) internet speeds worldwide by comparing the fixed broadband speeds of over 200 countries.

What Factors Affect Internet Speed?

Before diving in, it’s important to understand the key factors that impact a country’s internet speed. Generally speaking, internet speed depends on:

  1. Infrastructure or the type of cabling (copper or fiber-optic) that a country’s utilizing to support their internet service. Typically, the newer the infrastructure, the faster the connection.
  2. Proximity/connection to submarine cables is important, as these massive undersea fiber-optic cables transmit about 97% of the world’s communication data.
  3. The size of a country, since landmass affects how much it costs to upgrade infrastructure. The smaller the country, the cheaper it is to upgrade cabling.
  4. Investment makes a difference, or how much a country’s government prioritizes internet accessibility.

Of course, other factors may influence a country’s internet speed too, such as government regulation and intentional bandwidth throttling, which is the case in countries like Turkmenistan.

Ranked: Fixed Broadband Speeds

To measure fixed broadband speeds across the globe, used more than 1.1 billion speed tests, sourced from over 200 countries.

The region with the fastest connection is Jersey, which is one of the islands that make up the British Isles. It has an average download speed of 274.27 mbps—almost 9x the overall average.

RankCountryMean download speed (Mbps)
1🇯🇪 Jersey274.27
2🇱🇮 Liechtenstein211.26
3🇮🇸 Iceland191.83
4🇦🇩 Andorra164.66
5🇬🇮 Gibraltar151.34
6🇲🇨 Monaco144.29
7🇲🇴 Macao SAR128.56
8🇱🇺 Luxembourg107.94
9🇳🇱 Netherlands107.3
10🇭🇺 Hungary104.07
11🇸🇬 Singapore97.61
12🇧🇲 Bermuda96.54
13🇯🇵 Japan96.36
14🇺🇸 United States92.42
15🇭🇰 Hong Kong SAR91.04
16🇪🇸 Spain89.59
17🇸🇪 Sweden88.98
18🇳🇴 Norway88.67
19🇫🇷 France85.96
20🇳🇿 New Zealand85.95
21🇲🇹 Malta85.2
22🇪🇪 Estonia84.72
23🇦🇽 Aland Islands81.31
24🇨🇦 Canada79.96
25🇧🇪 Belgium78.46
26🇻🇦 Vatican City73.49
27🇰🇾 Cayman Islands71.47
28🇦🇼 Aruba70.66
29🇷🇴 Romania67.4
30🇸🇮 Slovenia67.2
31🇵🇱 Poland63.84
32🇧🇬 Bulgaria63.41
33🇱🇻 Latvia63.28
34🇵🇹 Portugal63.02
35🇰🇷 Republic of Korea61.72
36🇩🇪 Germany60.55
37🇱🇹 Republic of Lithuania56.17
38🇧🇧 Barbados55.92
39🇫🇮 Finland55.08
40🇸🇰 Slovak Republic54.92
41🇹🇭 Thailand53.95
42🇮🇲 Isle of Man52.1
43🇬🇧 United Kingdom51.48
44🇮🇪 Ireland51.41
45🇨🇭 Switzerland50.83
46🇭🇷 Croatia49.77
47🇩🇰 Denmark49.24
48🇵🇲 Saint Pierre and Miquelon47.92
49🇹🇼 Taiwan46.43
50🇷🇪 Réunion43.62
51🇲🇾 Malaysia42.83
52🇬🇱 Greenland41.56
53🇸🇲 San Marino40.55
54🇵🇷 Puerto Rico40.52
55🇦🇺 Australia40.5
56🇲🇫 Saint Martin40.19
57🇲🇪 Montenegro40.14
58🇧🇸 Bahamas39.71
59🇦🇹 Austria37.99
60🇨🇿 Czechia37.23
61🇮🇹 Italy36.69
62🇷🇸 Serbia36.59
63🇲🇩 Republic of Moldova36.47
64🇹🇨 Turks and Caicos Islands36.09
65🇹🇹 Trinidad and Tobago35.81
66🇷🇺 Russian Federation35.73
67🇮🇱 Israel34.97
68🇧🇷 Brazil33.34
69🇳🇨 New Caledonia31.79
70🇧🇦 Bosnia and Herzegovina31.72
71🇬🇬 Guernsey31.2
72🇵🇦 Panama30.58
73🇦🇪 United Arab Emirates29.9
74🇬🇷 Greece29.76
75🇻🇮 Virgin Islands, U.S.29.34
76🇨🇾 Cyprus28.3
77🇺🇦 Ukraine25.26
78🇶🇦 Qatar24.16
79🇧🇿 Belize23.12
80🇮🇳 India22.53
81🇽🇰 Kosovo22.21
82🇺🇾 Uruguay21.73
83🇫🇴 Faroe Islands21.59
84🇬🇵 Guadeloupe21.32
85🇯🇲 Jamaica20.96
86🇬🇺 Guam20.76
87🇻🇳 Vietnam20.66
88🇬🇩 Grenada20.49
89🇨🇼 Curaçao20.18
90🇿🇦 South Africa19.94
91🇲🇶 Martinique19.88
92🇧🇾 Belarus19.86
93🇧🇶 Bonaire, Saint Eustatius and Saba19.6
94🇵🇾 Paraguay19.41
95🇻🇬 Virgin Islands, British19.4
96🇦🇱 Albania19.36
97🇨🇷 Costa Rica19.02
98🇲🇽 Mexico18.83
99🇸🇦 Saudi Arabia18.1
100🇰🇼 Kuwait18.06
101🇦🇲 Armenia18.05
102🇵🇭 Philippines16.84
103🇴🇲 Oman16.73
104🇧🇭 Bahrain16.37
105🇲🇬 Madagascar16.28
106🇧🇳 Brunei15.79
107🇲🇰 North Macedonia15.38
108🇯🇴 Hashemite Kingdom of Jordan15.25
109🇱🇨 Saint Lucia15.02
110🇲🇳 Mongolia14.94
111🇻🇨 Saint Vincent and the Grenadines14.32
112🇬🇪 Georgia13.83
113🇨🇱 Chile13.76
114🇲🇵 Northern Mariana Islands13.15
115🇨🇴 Colombia13.13
116🇰🇳 Saint Kitts and Nevis12.96
117🇩🇲 Dominica12.41
118🇧🇱 Saint Barthélemy12.25
119🇭🇹 Haiti12.12
120🇨🇬 Republic of the Congo12.07
121🇸🇨 Seychelles12.04
122🇩🇴 Dominican Republic11.87
123🇦🇸 American Samoa11.76
124🇹🇷 Turkey11.58
125🇵🇪 Peru11.35
126🇰🇪 Kenya11.27
127🇬🇫 French Guiana10.99
128🇧🇫 Burkina Faso10.73
129🇲🇦 Morocco10.33
130🇪🇨 Ecuador10.25
131🇸🇻 El Salvador9.95
132🇱🇰 Sri Lanka9.95
133🇬🇹 Guatemala9.85
134🇳🇮 Nicaragua9.75
135🇮🇩 Indonesia9.58
136🇨🇮 Cote D'Ivoire9.54
137🇫🇯 Fiji9.4
138🇬🇾 Guyana9.26
139🇬🇭 Ghana9.23
140🇦🇮 Anguilla9
141🇦🇬 Antigua and Barbuda8.69
142🇳🇬 Nigeria8.68
143🇦🇷 Argentina8.68
144🇹🇿 United Republic of Tanzania8.6
145🇲🇺 Mauritius8.53
146🇺🇬 Uganda8.52
147🇰🇭 Cambodia8.49
148🇱🇸 Lesotho8.46
149🇨🇻 Cape Verde7.94
150🇿🇼 Zimbabwe7.92
151🇾🇹 Mayotte7.7
152🇵🇫 French Polynesia7.67
153🇹🇳 Tunisia7.46
154🇲🇻 Maldives7.45
155🇰🇬 Kyrgyzstan7.44
156🇸🇷 Suriname7.44
157🇧🇴 Bolivia7.36
158🇲🇿 Mozambique7.17
159🇭🇳 Honduras7.17
160🇮🇷 Iran7.05
161🇸🇳 Senegal7.02
162🇪🇬 Egypt6.94
163🇳🇵 Nepal6.84
164🇼🇸 Samoa6.8
165🇲🇭 Marshall Islands6.71
166🇺🇿 Uzbekistan6.64
167🇦🇿 Azerbaijan6.63
168🇧🇹 Bhutan6.44
169🇷🇼 Rwanda6.29
170🇸🇽 Sint Maarten6.15
171🇱🇦 Lao People's Democratic Republic5.91
172🇦🇴 Angola5.88
173🇰🇿 Kazakhstan5.83
174🇱🇧 Lebanon5.67
175🇮🇶 Iraq5.58
176🇿🇲 Zambia5.48
177🇸🇧 Solomon Islands5.33
178🇱🇷 Liberia5.23
179🇵🇬 Papua New Guinea5.1
180🇬🇦 Gabon4.99
181🇲🇼 Malawi4.96
182🇵🇼 Palau4.84
183🇲🇱 Mali4.72
184🇲🇲 Myanmar4.58
185🇳🇦 Namibia4.42
186🇰🇲 Comoros3.99
187🇸🇿 Eswatini3.73
188🇱🇾 Libya3.73
189🇧🇼 Botswana3.65
190🇵🇸 Palestine3.65
191🇨🇩 DR Congo3.63
192🇹🇬 Togo3.54
193🇳🇪 Niger3.23
194🇩🇿 Algeria3.08
195🇨🇲 Cameroon3.04
196🇨🇺 Cuba2.92
197🇧🇩 Bangladesh2.9
198🇻🇺 Vanuatu2.9
199🇵🇰 Pakistan2.82
200🇧🇮 Burundi2.82
201🇻🇪 Venezuela2.62
202🇧🇯 Benin2.59
203🇲🇷 Mauritania2.54
204🇸🇹 São Tomé and Príncipe2.43
205🇪🇷 Eritrea2.41
206🇬🇳 Guinea2.39
207🇹🇩 Chad2.39
208🇸🇱 Sierra Leone2.19
209🇨🇳 China2.06
210🇬🇲 Gambia2.04
211🇹🇯 Tajikistan1.82
212🇸🇩 Sudan1.8
213🇸🇾 Syrian Arab Republic1.67
214🇫🇲 Federated States of Micronesia1.63
215🇸🇴 Somalia1.59
216🇩🇯 Djibouti1.46
217🇦🇫 Afghanistan1.41
218🇸🇸 South Sudan1.4
219🇹🇱 Democratic Republic of Timor-Leste1.33
220🇬🇶 Equatorial Guinea1.3
221🇬🇼 Guinea-Bissau1.24
222🇪🇹 Ethiopia1.2
223🇾🇪 Yemen0.68
224🇹🇲 Turkmenistan0.5

Infrastructure is a major reason behind Jersey’s speedy internet. It’s the first jurisdiction in the world to upgrade its entire system to pure fibre (FTTP). But the region’s size also plays a factor, since its landmass and population size are both relatively small compared to the rest of the world.

Second on the list is another small region, Liechtenstein, with an average download speed of 211.26 mbps. Liechtenstein is one of the richest countries in the world per capita, and its government has invested heavily in its telecommunications infrastructure, aiming to be fully fibre optic by 2022.

Like Jersey, Liechtenstein also has a relatively small population. At the time of this article’s publication, the region is home to approximately 38,000 people. In fact, it’s worth noting that of the top ten regions, only two have populations over one million—the Netherlands, and Hungary.

At the opposite end of the spectrum, Turkmenistan has the slowest fixed broadband, with a speed of 0.5 mbps. As mentioned above, this is largely because of government regulation and intervention.

The Future is 5G

Innovation and new technologies are changing the digital landscape, and things like 5G networks are becoming more mainstream across the globe.

Because of the rapidly changing nature of this industry, the data behind this ranking is updated monthly to provide the latest look at internet speeds across the globe.

This means the bar is gradually raising when it comes to internet speed, as faster, stronger internet connections become the norm. And countries that aren’t equipped to handle these souped-up networks will lag behind even further.

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Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.



A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

PeriodEraCompute Doubling
1950–2010Pre-Deep Learning18–24 months
2010–2016Deep Learning5–7 months
2016–2022Large-scale models11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

Perceptron Mark I1957–58695,000
Neocognitron1980228 million
NetTalk198781 billion
TD-Gammon199218 trillion
NPLM20031.1 petaFLOPs
AlexNet2012470 petaFLOPs
AlphaGo20161.9 million petaFLOPs
GPT-32020314 million petaFLOPs
Minerva20222.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.

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