Which technology reigns supreme in transcoding: CPU-only, GPU, or ASIC-based? Kenneth Robinson’s incisive analysis from the recent symposium makes a compelling case for ASIC-based transcoding, particularly NETINT’s Quadra. Robinson’s metrics prioritized viewer experience, power efficiency, and cost. While CPU-only systems appear initially economical, they falter with advanced codecs like HEVC. NVIDIA’s GPU transcoding offers more promise, but the Quadra system still outclasses both in quality, cost per stream, and power consumption. Furthermore, Quadra’s adaptability allows a seamless switch between H.264 and HEVC without incurring additional costs. Independent assessments, such as Ilya Mikhaelis’, echo Robinson’s conclusions, cementing ASIC-based transcoding as the optimal choice.
During the recent symposium, Kenneth Robinson, NETINT’s manager of Field Application Engineering, compared three transcoding technologies: CPU-only, GPU, and ASIC-based. His analysis, which incorporated quality, throughput, and power consumption, is useful as a template for testing methodology and for the results. You can watch his presentation here and download a copy of his presentation materials here.
Figure 1. Overall savings from ASIC-based transcoding (Quadra) over GPU (NVIDIA) and CPU.
As a preview of his findings, Kenneth found that when producing H.264, ASIC-based transcoding delivered CAPEX savings of 86% and 77% compared to CPU and GPU-based transcoding, respectively. OPEX savings were 95% vs. CPU-only transcoding and 88% compared to GPU.
For the more computationally complex HEVC codec, the savings were even greater. As compared to CPU-based transcoding, ASICs saved 94% on CAPEX and 98% on OPEX. As compared to GPU-based transcoding, ASICs saved 82% on CAPEX and 90% on OPEX. These savings are obviously profound and can make the difference between a successful and profitable service and one that’s mired in red ink.
Let’s jump into Kenneth’s analysis.
Digging into the transcoding alternatives, Kenneth described the three options. First are CPUs from manufacturers like AMD or Intel. Second are GPUs from companies like NVIDIA or AMD. Third are ASICs, or Application Specific Integrated Circuits, from manufacturers like NETINT. Kenneth noted that NETINT calls its Quadra devices Video Processing Units (VPU), rather than transcoders because they perform multiple additional functions besides transcoding, including onboard scaling, overlay, and AI processing.
He then outlined the factors used to determine the optimal choice, detailing the four factors shown in Figure 2. Quality is the average quality as assessed using metrics like VMAF, PSNR, or subjective video quality evaluations involving A/B comparisons with viewers. Kenneth used VMAF for this comparison. VMAF has been shown to have the highest correlation with subjective scores, which makes it a good predictor of viewer quality of experience.
Figure 2. How Kenneth compared the technologies.
Low-frame quality is the lowest VMAF score on any frame in the file. This is a predictor for transient quality issues that might only impact a short segment of the file. While these might not significantly impact overall average quality, short, low-quality regions may nonetheless degrade the viewer’s quality of experience, so are worth tracking in addition to average quality.
Server capacity measures how many streams each configuration can output, which is also referred to as throughput. Dividing server cost by the number of output streams produces the cost per stream, which is the most relevant capital cost comparison. The higher the number of output streams, the lower the cost per stream and the lower the necessary capital expenditures (CAPEX) when launching the service or sourcing additional capacity.
Power consumption measures the power draw of a server during operation. Dividing this by the number of streams produced results in the power per stream, the most useful figure for comparing different technologies.
Detailing his test procedures, Kenneth noted that he tested CPU-only transcoding on a system equipped with an AMD Epic 32-core CPU. Then he installed the NVIDIA L4 GPU (a recent release) for GPU testing and NETINT’s Quadra T1U U.2 form factor VPU for ASIC-based testing.
He evaluated two codecs, H.264 and HEVC, using a single file, the Meridian file from Netflix, which contains a mix of low and high-motion scenes and many challenging elements like bright lights, smoke and fog, and very dark regions. If you’re testing for your own deployments, Kenneth recommended testing with your own test footage.
Kenneth used FFmpeg to run all transcodes, testing CPU-only quality using the x264 and x265 codecs using the medium and very fast presets. He used FFmpeg for NVIDIA and NETINT testing as well, transcoding with the native H.264 and H.265 codec for each device.
H.264 Average, Low-Frame, and Rolling Frame Quality
The first result Kenneth presented was average H.264 quality. As shown in Figure 3, Kenneth encoded the Meridian file to four output files for each technology, with encodes at 2.2 Mbps, 3.0 Mbps, 3.9 Mbps, and 4.75 Mbps. In this “rate-distortion curve” display, the left axis is VMAF quality, and the bottom axis is bitrate. In all such displays, higher results are better, and Quadra’s blue line is the best alternative at all tested bitrates, beating NVIDIA and x264 using the medium and very fast presets.
Figure 3. Quadra was tops in H.264 quality at all tested bitrates.
Kenneth next shared the low-frame scores (Figure 4), noting that while the NVIDIA L4’s score was marginally higher than the Quadra’s, the difference at the higher end was only 1%. Since no viewer would notice this differential, this indicates operational parity in this measure.
Figure 4. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.
The final H.264 quality finding displayed a 20-second rolling average of the VMAF score. As you can see in Figure 5, the Quadra, which is the blue line, is consistently higher than the NVIDIA L4 or medium or very fast. So, even though the Quadra had a lower single-frame VMAF score compared to NVIDIA, over the course of the entire file, the quality was predominantly superior.
Figure 5. 20-second rolling frame quality over file duration.
HEVC Average, Low-Frame, and Rolling Frame Quality
Kenneth then related the same results for HEVC. In terms of average quality (Figure 6), NVIDIA was slightly higher than the Quadra, but the delta was insignificant. Specifically, NVIDIA’s advantage starts at 0.2% and drops to 0.04% at the higher bit rates. So, again, a difference that no viewer would notice. Both NVIDIA and Quadra produced better quality than CPU-only transcoding with x265 and the medium and very fast presets.
Figure 6. Quadra was tops in H.264 quality at all tested bitrates.
In the low-frame measure (Figure 7), Quadra proved consistently superior, with NVIDIA significantly lower, again a predictor for transient quality issues. In this measure, Quadra also consistently outperformed x265 using medium and very fast presets, which is impressive.
Figure 7. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.
Finally, HEVC moving average scoring (Figure 8) again showed Quadra to be consistently better across all frames when compared to the other alternatives. You see NVIDIA’s downward spike around frame 3796, which could indicate a transient quality drop that could impact the viewer’s quality of experience.
Figure 8. 20-second rolling frame quality over file duration.
Cost Per Stream and Power Consumption Per Stream - H.264
To measure cost and power consumption per stream, Kenneth first calculated the cost for a single server for each transcoding technology and then measured throughput and power consumption for that server using each technology. Then, he compared the results, assuming that a video engineer had to source and run systems capable of transcoding 320 1080p30 streams.
You see the first step for H.264 in Figure 9. The baseline computer without add-in cards costs $7,100 but can only output fifteen 1080p30 streams using an average of the medium and veryfast presets, resulting in a cost per stream was $473. Kenneth installed two NVIDIA L4 cards in the same system, which boosted the price to $14,214, but more than tripled throughput to fifty streams, dropping cost per stream to $285. Kenneth installed ten Quadra T1U VPUs in the system, which increased the price to $21,000, but skyrocketed throughput to 320 1080p30 streams, and a $65 cost per stream.
This analysis reveals why computing and focusing on the cost per stream is so important; though the Quadra system costs roughly three times the CPU-only system, the ASIC-fueled output is over 21 times greater, producing a much lower cost per stream. You’ll see how that impacts CAPEX for our 320-stream required output in a few slides.
Figure 9. Computing system cost and cost per stream.
Figure 10 shows the power consumption per stream computation. Kenneth measured power consumption during processing and divided that by the number of output streams produced. This analysis again illustrates why normalizing power consumption on a per-stream basis is so necessary; though the CPU-only system draws the least power, making it appear to be the most efficient, on a per-stream basis, it’s almost 20x the power draw of the Quadra system.
Figure 10. Computing power per stream for H.264 transcoding.
Figure 11 summarizes CAPEX and OPEX for a 320-channel system. Note that Kenneth rounded down rather than up to compute the total number of servers for CPU-only and NVIDIA. That is, at a capacity of 15 streams for CPU-only transcoding, you would need 21.33 servers to produce 320 streams. Since you can’t buy a fractional server, you would need 22, not the 21 shown. Ditto for NVIDIA and the six servers, which, at 50 output streams each, should have been 6.4, or actually 7. So, the savings shown are underrepresented by about 4.5% for CPU-only and 15% for NVIDIA. Even without the corrections, the CAPEX and OPEX differences are quite substantial.
Figure 11. CAPEX and OPEX for 320 H.264 1080p30 streams.
Cost Per Stream and Power Consumption Per Stream - HEVC
Kenneth performed the same analysis for HEVC. All systems cost the same, but throughput of the CPU-only and NVIDIA-equipped systems both drop significantly, boosting their costs per stream. The ASIC-powered Quadra outputs the same stream count for HEVC as for H.264, producing an identical cost per stream.
Figure 12. Computing system cost and cost per stream.
The throughput drop for CPU-only and NVIDIA transcoding also boosted the power consumption per stream, while Quadra’s remained the same.
Figure 13. Computing power per stream for H.264 transcoding.
Figure 14 shows the total CAPEX and OPEX for the 320-channel system, and this time, all calculations are correct. While CPU-only systems are tenuous–at best– for H.264, they’re clearly economically untenable with more advanced codecs like HEVC. While the differential isn’t quite so stark with the NVIDIA products, Quadra’s superior quality and much lower CAPEX and OPEX are compelling reasons to adopt the ASIC-based solution.
Figure 14. CAPEX and OPEX for 320 1080p30 HEVC streams.
As Kenneth pointed out in his talk, even if you’re producing only H.264 today, if you’re considering HEVC in the future, it still makes sense to choose a Quadra-equipped system because you can switch over to HEVC with no extra hardware cost at any time. With a CPU-only system, you’ll have to more than double your CAPEX spending, while with NVIDIA, you’ll need to spend another 25% to meet capacity.
The Cost of Redundancy
Kenneth concluded his talk with a discussion of full hardware and geo-redundancy. He envisioned a setup where one location houses two servers (a primary and a backup) for full hardware redundancy. A similar setup would be replicated in a second location for geo-redundancy. Using the Quadra video server, four servers could provide both levels of redundancy, costing a total of $84,000. Obviously, this is much cheaper than any of the other transcoding alternatives.
NETINT’s Quadra VPU proved slightly superior in quality to the alternatives, vastly cheaper than CPU-only transcoding, and very meaningfully more affordable than GPU-based transcoders. While these conclusions may seem unsurprising – an employee at an encoding ASIC manufacturer concludes that his ASIC-based technology is best — you can check Ilya Mikhaelis’ independent analysis here and see that he reached the same result.