Streaming engineers face constant pressure to produce more streams at a lower cost per stream and reduced power consumption. However, those considering new transcoding technologies need a solution that integrates with their existing workflows while delivering the quality and flexibility of software with the cost efficiency of ASIC-based hardware.
If this sounds like you, the US $21,000 NETINT Quadra Video Server could be the ideal solution. Combining the Supermicro 1114S-WN10RT AMD EPYC 7543P-powered server hosting ten NETINT Quadra T1U Video Processing Units (VPUs), is a power house. The server outputs H.264, HEVC, and AV1 streams at normal or low latency, and you can control operation via FFmpeg, GStreamer, or a low-level API. This makes the server a drop-in replacement for a traditional FFmpeg-based software or GPU-based encoding stack.
As you’ll see below, the 1RU form factor server can output up to 20 8Kp30 streams, 80 4Kp30 streams, up to 320 1080p30 streams, and 640 720p30 streams for live and interactive video streaming applications. For ABR production, the server can output over 120 encoding ladders in H.264, HEVC, and AV1 formats. This unparalleled density enables all video engineers to greatly expand capacity while shrinking the number of required servers and the associated power bills.
I’ll start this review with a technical description of the server and transcoding hardware. Then we’ll review some performance results for one-to-one streaming and H.264, HEVC, and AV1 ladder generation and finish with a look at the server’s AI-based features and output.
Figure 1. The Quadra Video Server powered by the Codensity G5 ASIC.
Hardware Specs - The Quadra Server
The NETINT Quadra Video Server uses the Supermicro 1114S-WN10RT server platform with a 32-core AMD EPYC 7543P CPU running Ubuntu 20.04.05 LTS. The server ships with 128 GB of DDR4-3200 RAM and a 400GB M.2 SSD drive with 3x PCIe slots and ten NVME slots that house the Quadra T1U VPUs. NETINT also offers the server with two other CPUs, the 64-core AMD EPYC 7713P processor ($24,000) for more demanding applications, and the economical 8-core AMD EPYC 7232P processor ($19,000) for pure transcoding applications that may not require a 32-core CPU.
Supermicro* is a leading server and storage vendor that designs, develops, and manufactures primarily in the United States. Supermicro* adheres to high-quality standards, with a quality management system certified to the ISO 9001:2015 and ISO 13485:2016 standards, and an environmental management system certified to the ISO 14001:2015 standard. Supermicro is also a leader in green computing and reducing data center footprints (see the white paper Green Computing: Top Ten Best Practices for a Green Data Center). As you’ll see below, this focus has resulted in an extremely power-efficient server to house the NETINT Quadra VPUs.
*We are the leading server and storage vendor that designs, develops, and manufactures the majority of our development in the United States – at our headquarters in San Jose, Calif. Our Quality Management System is certified to ISO 9001:2015 and ISO 13485:2016 standards and our Environmental Management System is certified to ISO 14001:2015 standard. In addition to that, the Supermicro Information Security Managemen
Hardware Specs – Quadra VPUs
The Quadra T1U VPUs are powered by the NETINT Codensity G5 ASIC and packaged in a U.2 form factor that plugs into the NVMe slots in the server and communicate via the ultra-high bandwidth PCIe 4.0 bus. Quadra VPUs can decode H.264, HEVC, and VP9 inputs and encode into the H.264, HEVC, and AV1 standards.
Beyond transcoding, Quadra VPUs house 2D processing engines that can crop, pad, and scale video, and perform video overlay, YUV and RGB conversion, reducing the load on the host CPU to increase overall throughput. These engines can perform xStack operations in hardware, making the server ideal for conferencing and security applications that combine multiple feeds into a multi-pane output mosaic window.
Each Quadra T1U in the server includes a 15 TOPS Deep Neural Network Inference Engine that can support models trained with all major deep learning frameworks, including Caffe, TensorFlow, TensorFlow Lite, Keras, Darknet, PyTorch, and ONNX. NETINT supplies several reference models, including a facial detection model that uses region of interest encoding to improve facial quality on security and other highly compressed streams. Another model provides background removal for conferencing applications.
We tested the server with FFmpeg and GStreamer. Operationally, both GStreamer and FFmpeg communicate with the libavcodec layer that functions between the Quadra NVME interface and the FFmpeg/GStreamer software layers. This allows existing FFmpeg and GStreamer-based transcoding applications to control server operation with minimal changes.
Figure 2. The software architecture for controlling the server.
To allocate jobs to the ten Quadra T1U VPUs, the Quadra device driver software includes a resource management module that tracks Quadra capacity and usage load to present inventory and status on available resources and enable resource distribution. There are several modes of operation, including auto, which automatically distributes the work among the available VPUs.
Alternatively, you can manually assign decoding and encoding tasks to different Quadra VPUs in the command line or application and even control which streams are decoded by the host CPU or a Quadra. With these and similar controls, you can most efficiently balance the overall transcoding load between the Quadra and host CPU and maximize throughput. We used auto distribution for all tests.
We tested running FFmpeg v 5.2.3 and GStreamer version 1.18 (with FFmpeg v 4.3.1), and with Quadra release 3.2.0. As you’ll see, we weren’t able to complete all tests in all modes with both software programs, so we presented the results we were able to complete.
In all tests, we configured the Quadra VPUs for maximum throughput as opposed to maximum quality. You can read about the configuration options and their impact on output quality and performance in Benchmarking Hardware Transcoder Performance. While quality will relate to each video and encoding configuration, the configuration used should produce quality at least equal to the veryfast x264 and x265 presets, with quality up to the slow presets available in configurations that optimize quality over throughput.
We tested multiple facets of system performance. The first series of tests involved a single stream in and single stream out, either at the same resolution as the incoming stream or scaled down and output at a lower resolution. Many applications use this mode of operation, including gaming, gambling, and auctions.
The second use case is ABR distribution, where a single input stream is transcoded to a full encoding ladder. Here we supplemented the results with software-only transcodes for comparison purposes. To assess AI-related throughput, we tested region-of-interest transcoding and background removal.
In most modes, we tested normal and low-latency performance. To simulate live streaming and minimize file I/O as a drag on system performance, we retrieved the source file from a RAM drive on the server and delivered the encoded file to RAM.
Table 1 shows transcoding results for 8K, 4K, 1080p, and 720p in latency tolerant and low-delay modes. The number represents the amount of full frame rate outputs produced by the system at each configuration.
These results are most relevant for interactive gambling and similar applications that input a single stream, transcode the stream at full resolution, and stream it out. You see that 8K streaming is not available in the AV1 format and that H.264 and HEVC are not available in low latency mode with either program. Interestingly, FFmpeg outperformed GStreamer at this resolution while the reverse was true at 1080p.
4K and 720p results were consistent for all input and output codecs and for normal and low delay modes. All output numbers are impressive, but the 640 720p streams for AV1, H.264, or HEVC is remarkable density for a 1RU rack server.
At 1080p there are minor output differences between normal and low-delay mode and the different codecs, though the codec-related differences aren’t that substantial. Interestingly, HEVC throughput is slightly higher than H.264, with AV1 about 16% behind HEVC.
Table 1. Same resolution transcoding results.
Table 2 shows a collection of maximum data points (worst case) from the transcoding results presented in Table 1. As you can see, both Max CPU and power consumption track upwards with the number of streams produced. Max latency (decode plus encode) in normal latency mode tracks downward with the stream resolution, becoming quite modest at 720p. Max latency (decode plus encode) in low-delay mode for both decoding and encoding starts and stays under 30.9 milliseconds, which is less than a single frame.
Table 2. Maximum CPU, power consumption, and latency data for pure transcoding.
As between FFmpeg and GStreamer, the latter proved more CPU and power efficient than the former, in both normal and low-delay modes. For example, in all tests, GStreamer’s CPU utilization was less than half of FFmpeg, through the power consumption delta was generally under 20%.
At 8K and 4K resolutions, the latency reported was about even between the two programs, but at the lower resolutions in low-delay mode, GStreamer’s latency was often half that of FFmpeg. You can see an example of these two observations in Table 3, reporting 720p HEVC input and output as HEVC. Though the throughput was identical, GStreamer used much less energy and produced much lower latency. As you’ll see in the next section, this dynamic stayed true in transcoding with scaling tests, making GStreamer the superior app for applications involving same-resolution transcoding and transcoding with scaling.
Figure 3. GStreamer was much more CPU and power efficient
and delivered substantially lower latency than FFmpeg
in these same resolution transcode tests.
Transcoding and Scaling
Table 3 shows transcoding while scaling results, first 8K input to 4K output, then 4K to 1080p, and lastly 1080p to 720p. If you compare Table 3 with Table 1, you’ll see that performance tracks the input resolution, not output, which makes sense because decoding is a separate operation that obviously involves its own hardware limits.
Table 3. Transcoding while scaling results.
As the Quadra VPUs perform scaling on-board, there was no drop in throughput with the scaling related tests; rather, there was a slight increase in 8K > 4K and 4K > 1080p outputs over the same resolution transcoding reported in Table 1. In terms of throughput, the results were consistent between the codecs and software programs.
Table 4 shows the max CPU and power usage for all the transcodes in Table 3, which increased somewhat from the low-quantity high-resolution transcodes to the high-quantity low-resolution transcodes but was well within the performance envelope for this 32-core server.
The Max latency for all normal encodes was relatively consistent between five and six frames. With low delay engaged, 8K > 4K latency didn’t drop that significantly, though you’d assume that 8K to 4K transcodes are uncommon. Latency dropped to below a single frame in the two lower resolution transcodes.
Table 4. Maximum CPU, power consumption, and latency data for transcoding while scaling.
As between FFmpeg and GStreamer we saw the same dynamic as with full resolution transcodes; in most tests, GStreamer consumed significantly less power and produced sharply lower latency. You can see an example of this in Table 5, reporting the results of 1080p incoming HEVC output to AV1 at 720p.
Figure 4. GStreamer was much more CPU and power efficient
and delivered much lower latency than FFmpeg in this scale then transcode tests.
Encoding Ladder Testing
Table 5 shows the results of full ladder testing with CPU, latency, and power consumption embedded in the output instances. Note that we tested a five-rung ladder for H.264, and four-rung ladders for HEVC and AV1. We didn’t test 4K H.264 output because few services would deploy this configuration. Also, we didn’t test with GSteamer because NETINT’s current GStreamer implementation can’t use Quadra’s internal scalers when producing more than a single file, an issue that the NETINT engineering team will resolve soon. Also, as you can see, low-delay mode wasn’t available for 4K testing.
This fine print behind us, as with the single file testing, throughput was impressive. The ability to deliver up to 140 HEVC 4-rung ladders from a single 1RU rack, in either normal or low-latency mode, is remarkable.
Table 5: Encoding ladder throughput.
For comparison purposes, we produced the equivalent encoding ladders on the same server using software-only encoding with FFmpeg and the x264, x265, and SVT-AV1 codecs. To match the throughput settings used for Quadra, we used the ultrafast preset for x264 and x265, and preset eleven for SVT-AV1. You see the results in Table 6.
Note that these numbers over-represent software-based output since no engineer would produce a live stream with CPU utilization over 60 – 65%, since a sudden spike in CPU usage would crash all the streams. Not only is CPU utilization much lower for the Quadra-driven encodes, minimizing the risk of exceeding CPU capacity, Quadra-based transcoding is much more determinist than CPU-based transcoding, so CPU requirements don’t typically change in midstream.
All that said, Quadra proved much more efficient than software-based encoding for all codecs, particularly HEVC and AV1. In Table 5, the Multiple column shows the number of servers required to produce the same output as the Quadra server, plus the power consumed by all these servers. For H.264, you would need six servers instead of a single Quadra server to produce the 120 instances, and power costs would be nearly six times higher. That’s running each server at 98.3% CPU utilization. Running at a more reasonable 60% utilization would translate to ten servers and 4,287 watts per hour.
Table 6. Ladders, CPU utilization, and power consumed for CPU-only transcoding.
Even without factoring in the 60% CPU-utilization limits, the comparison reaches untenable levels with HEVC and AV1. As the data shows, CPU-based transcoding simply can’t keep up with these more complex codecs, while the ASIC-driven Quadra remains relatively consistent.
The next two tables benchmark AI-related functions, first region of interest encoding, then background removal. Briefly, region of interest encoding uses AI to search for faces in a stream and then increases the bits assigned to those faces to increase quality. This is useful in surveillance videos or any low-bitrate video environment where facial quality is important.
We tested 1080p AVC input and output with FFmpeg only, and the system delivered sixty outputs in both normal and low-delay modes, with very modest CPU utilization and power consumption. For more on Quadra’s AI-related functions, and for an example of the region of interest filter, see an Introduction to AI Processing on Quadra.
Table 7. Throughput for Region of Interest transcoding via Artificial Intelligence.
Table 8 shows 1080p input/output using the AVC codec with background removal, which is useful in conferencing and other applications to composite participants in a virtual environment (see Figure 2). This task involves considerably more CPU but delivers slightly greater throughput.
Table 8. Throughput for background removal and transcoding via Artificial Intelligence.
As you can read about in the Introduction to AI Processing on Quadra, Quadra comes with these and other AI-based applications and can deploy AI-based models developed in most machine learning programs. Over time, AI-based operations will become increasingly integral to video transcoding functions, and the Quadra Video Server provides a future-proof platform for that integration.
Figure 4. Throughput for background removal and transcoding via Artificial Intelligence.
While there’s a compelling case for ASIC-based transcoding solely for H.264 production, these tests show that as applications migrate to more complex codecs like HEVC and AV1, CPU-based transcoding is untenable economically and for the environment. Beyond pure transcoding functionality, if there’s anything that the ChatGPT-era has proven, it’s that AI-based transcoding-related functions will become mainstream much sooner than anyone might have thought. With highly efficient ASIC-based transcoding hardware and AI engines, the Quadra Video Server checks all the boxes for a server to strongly consider for all high-volume live streaming applications.