Jinbei HNS-LITE first release on the Internet

Due to the global chip production capacity, I haven't reviewed a new phone for you for a long time. Today I bring you a new phone from my old friend Jinbei, the HS LITE.

Look at the family design of Jinbei. Do you think it is a power-hungry beast? However, it is just as its name suggests. Although it is big, it has low power consumption. What's more surprising is that it is a silent machine.

The editor can't wait to see the performance of this new machine, so let's power it on and test it. As usual, find the machine IP first, then log in and configure it. After logging in to the backend, I found that Jinbei's UI has been optimized, and the overall style is simpler and clearer. First, we need to add a mining pool and click the menu mining machine configuration.

Then we can add it according to our own information. The official has already built-in some configurations, which is very convenient. Like it.

Everything is done, let's wait and see how the machine performs. After the machine ran smoothly for 2 hours, we started to measure the data (it is really silent, we will use the data to speak for ourselves). First, let's look at a set of temperature data. The room temperature is 22 degrees, the air inlet is 24.5 degrees, and the air outlet is 49.2 degrees.

From the temperature performance, the machine generates very little heat, and high temperature is absolutely non-existent. Next, let's look at the power and noise performance. The power is 1204.6W and the noise is 57.7 decibels. The data speaks for itself, it is undoubtedly a low-power and silent machine.

The machine has been running for a day. Let's take a look at the computing power performance and stability. The results are not surprising. It is in line with Jinbei's usual style, with sufficient computing power and stable operation.

Don’t forget that this is a dual-algorithm machine. HNS has been tested, so let’s switch the algorithm to see how SC performs. Switching the algorithm is very simple, miner settings -> algorithm settings -> apply.

After running smoothly for a period of time, first measure a set of temperature data, the room temperature is 22 degrees, the air inlet is 27.1 degrees, and the air outlet is 42.4 degrees.

Since the SC algorithm consumes less power, the heat generated is also relatively lower. Let's look at the power and noise performance, the power is 691.7W and the noise is 54.2 decibels.

The 24-hour computing power performance is still as stable as ever. The 24-hour average computing power of the SC algorithm is 2.97T.

Now that all the parameters have been run, I will continue to explore why it is so excellent. Now we are ready to disassemble the machine. Before disassembling it, let's take a look at the structural distribution of the mining machine.

First, we disassembled the control board cover and the power supply cover. The power supply bridged the two hash boards. Since there are two hash boards, the power consumption is naturally smaller. There is another obvious change in the control area, that is, all the wiring harnesses are bundled and organized. It seems that Jinbei manufacturers are getting better and better at handling details. Thumbs up.

Although we know the reason for the low power consumption, how to achieve quietness and heat? We continue to disassemble the machine with questions.

Have you seen the two hashboards above? I wonder if you can see some differences. I won’t keep you in suspense. I can see at a glance that the heat sink is obviously thickened. I have to praise the designer’s ingenuity here, using space to exchange time. In fact, not only is the heat sink enlarged, but the thermal grease is also full of details. Here are two close-ups of the hashboards for you to feel.

Summary: Jinbei is an old friend of everyone. It is not difficult to see that Jinbei is still making continuous progress in details. Its product strength is also obvious to all. There is no problem of false parameters. Although the noise data in this test is slightly different, it is definitely excellent noise data even according to the actual measured data. At the same time, this machine supports dual algorithms, and its risk resistance is greatly improved. Let's take a look at the evaluation data comparison below to give you a more intuitive feeling.