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In the end, the RTX 3090 is still better at ML workloads than every card other than the RTX A6000 or the A100. Although the 3090 theoretically falls behind the V100, Titan V and Titan RTX in terms of pure mixed-precision matrix math, in practice the 3090 still performs better on almost all workloads.

So maybe not 100% designed for ML, but better than anything else out there unless you want to sell a kidney.

The Volt and Prius Prime are plug-in hybrids, not pure electric vehicles. If you take those out (which I think was the OP's intent - driving a plug-in hybrid doesn't make you look as "green" as driving a true EV) Tesla is clearly the majority.

1 GB = 8 Gb, not 80 Gb

If you're playing at high framerates (120Hz+) you'll find that your CPU is inadequate for many recent games.

(playing games on a 1070 and a 6 year old Xeon and feeling the lack of single-threaded performance)

> The most popular languages are the least energy efficient and most resource intensive.

This is mostly incorrect. Of the top 10 programming languages on GitHub [1] only Python, Ruby and PHP are commonly used with an interpreter. The rest are all AOT or JIT compiled. I also suspect a large fraction of the Python projects are also data science / ML projects that heavily use packages like NumPy and TensorFlow that offload most of the work to highly optimized math libraries.

I also suspect if you were to look into the programming languages used by the companies with the most servers, they would skew more towards languages like Java and C++, or custom things like Facebook's Hack / HHVM.

[1] https://madnight.github.io/githut/#/pull_requests/2020/2

$25k/year is enough to live on even in the Bay Area if you have roommates and work at a big tech company with good benefits. It's not too hard to find a 3 bedroom house for $4,000/month in somewhere like Santa Clara. Split that 3 ways and that's only $1,300/month. Food and transportation is free during the week, leaving $700 / month for all other expenses (old car, food on weekends, some entertainment). Definitely would not be the most exciting life ever, but it's doable. Very effective if you want to quickly save $500k-1M for a home down payment or retire somewhere to a low COL area by the time you're 30.

Power density for supercapacitors is higher than batteries, but there aren't many applications that need more power than what the best lithium polymer cells that provide. 100C cells are readily available, which can discharge their full capacity about 1/100 hour ~= 36 seconds. These are the cells that power high-speed RC planes and handheld power banks that can jumpstart your car. One caveat with high discharge rate batteries is that cycle life is not necessarily any better, and charge rates are usually much lower than discharge rates, so sometimes you will see supercapacitors used to temporarily hold a large amount of energy.

Hydrogen has high power density in the sense that you can let it out of the tank very quickly and ignite it, but unless you want an explosion you are still constrained by an ICE motor or fuel cell. Those are much less power dense than batteries.

Nvidia does publicize benchmarks in its marketing, but many people (correctly) are skeptical of benchmarks published by the same company that makes the product. The number of CUDA cores and other hardware resources is a number that feels a bit more objective, even though it is hard to understand the direct implications on performance.

Apple is a good example of a company that just doesn't really talk too much about the low-level details of their chips. People buy their products anyway.

Nvidia actually did try to license out its GPU IP a few years back: https://www.anandtech.com/show/7083/nvidia-to-license-kepler...

I don't believe they ever closed a deal, but clearly Nvidia had some interest in becoming an IP vendor. Perhaps the terms were too onerous or the price too high.

As of their last earnings report Nikola had almost exactly $700M in cash / cash equivalents. Maybe GM just wants that :)

It's not uncommon to have RT workloads where the "hard" RT part can fit in a small amount of tightly coupled memory, but the same processor is also used for "soft" RT workloads that need a much larger amount of memory. 64-bit addressing makes developing this kind of system a lot easier.

Many MCUs / SoCs support DMA into tightly coupled memories. Very useful for "ping-pong buffering" to hide memory copying overhead - copying one buffer into a TCM bank while the processor works on a different TCM bank.

They might not be #1, #2 and #3, but they're certainly in the top 7 (there's also Facebook and the three huge Chinese internet companies). It's well known that those companies (sometimes called the 'Super Seven') account for a majority of Intel's datacenter revenue.

GDDR also uses a ton of power (per gigabyte) and has very tight signaling tolerances so it can't be socketed. Not a good choice for laptops (power), desktops (not socketed) or servers (both reasons).

Unfortunately, they're not thin compared to a laptop lid. Especially with the taper near the edges that Macbooks and many other high-end laptops have. There are some exotic optical solutions out there that allow for thinner lenses but I doubt we'll see those in laptops anytime soon.

Betterment is easy but if you have more than a few thousand bucks to invest I would just open a Vanguard account and put your money there. If you have no idea what you're doing just use a target date fund, otherwise just buy stock/bond ETFs in the mix that you want. Not worth ~0.4%/year in fees (doesn't sound like a lot until you realize it's $400/year on $100K) to have someone else do that for you.

The common thinking is that it will reduce pay, but it may not be as much as most people think. A good example of this is Facebook's new compensation structure for remote work. For engineers remotely working from low cost of living areas base salaries are only 10-15% lower than in the Bay Area, and stock compensation is the same. So a senior engineer (E5) might go from making $400k to $370k in total compensation, while also substantially saving on California's income taxes.

The quarterly rebalancing of the S&P 500 is only relevant when companies already in the index change their free float (usually by issuing / buying back shares). New companies can be added at any time (with a few days advance notice).

USB-C hubs nicely solve the problem of plugging USB devices, a monitor and a charger into a laptop. Like the docking stations in old laptops, but far cheaper. If you have multiple 4K screens or very high speed I/O devices you may need Thunderbolt, but for most people USB-C is fine.

The architecture here is similar to old PCs (roughly before 2010) that had integrated graphics in the northbridge. The memory controller also resides in the northbridge. The CPU communicates with the northbridge through the front-side bus. Incidentally the northbridge also used to be responsible for high-speed I/O such as PCIe, so even if you had a discrete GPU it would not be connected directly to the CPU.

Over time CPUs have integrated all those features on-die, resulting in today's SoC-like processors where the "chipset" is merely an I/O expander connected over a PCIe-like link.