The AMD Ryzen 9 9950X and Ryzen 9 9900X Review: Flagship Zen 5 Soars - and Stalls
Earlier this month, AMD launched the first two desktop CPUs using their latest Zen 5 microarchitecture: the Ryzen 7 9700X and the Ryzen 5 9600X. As part of the new Ryzen 9000 family, it gave us their latest Zen 5 cores to the desktop market, as AMD actually launched Zen 5 through their mobile platform last month, the Ryzen AI 300 series (which we reviewed).
Today, AMD is launching the remaining two Ryzen 9000 SKUs first announced at Computex 2024, completing the current Ryzen 9000 product stack. Both chips hail from the premium Ryzen 9 series, which includes the flagship Ryzen 9 9950X, which has 16 Zen 5 cores and can boost as high as 5.7 GHz, while the Ryzen 9 9900X has 12 Zen 5 cores and offers boost clock speeds of up to 5.6 GHz.
Although they took slightly longer than expected to launch, as there was a delay from the initial launch date of July 31st, the full quartet of Ryzen 9000 X series processors armed with the latest Zen 5 cores are available. All of the Ryzen 9000 series processors use the same AM5 socket as the previous Ryzen 7000 (Zen 4) series, which means users can use current X670E and X670 motherboards with the new chips. Unfortunately, as we highlighted in our Ryzen 7 9700X and Ryzen 5 9600X review, the X870E/X870 motherboards, which were meant to launch alongside the Ryzen 9000 series, won't be available until sometime in September.
We've seen how the entry-level Ryzen 5 9600X and the mid-range Ryzen 7 9700X perform against the competition, but it's time to see how far and fast the flagship Ryzen 9 pairing competes. The Ryzen 9 9950X (16C/32T) and the Ryzen 9 9900X (12C/24T) both have a higher TDP (170 W/120 W respectively) than the Ryzen 7 and Ryzen 5 (65 W), but there are more cores, and Ryzen 9 is clocked faster at both base and turbo frequencies. With this in mind, it's time to see how AMD's Zen 5 flagship Ryzen 9 series for desktops performs with more firepower, with our review of the Ryzen 9 9950X and Ryzen 9 9900 processors.
CPUs
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Intel and Sandia National Labs Roll Out 1.15B Neuron “Hala Point” Neuromorphic Research System 
While neuromorphic computing remains under research for the time being, efforts into the field have continued to grow over the years, as have the capabilities of the specialty chips that have been developed for this research. Following those lines, this morning Intel and Sandia National Laboratories are celebrating the deployment of the Hala Point neuromorphic system, which the two believe is the highest capacity system in the world. With 1.15 billion neurons overall, Hala Point is the largest deployment yet for Intel’s Loihi 2 neuromorphic chip, which was first announced at the tail-end of 2021.
The Hala Point system incorporates 1152 Loihi 2 processors, each of which is capable of simulating a million neurons. As noted back at the time of Loihi 2’s launch, these chips are actually rather small – just 31 mm2 per chip with 2.3 billion transistors each, as they’re built on the Intel 4 process (one of the only other Intel chips to do so, besides Meteor Lake). As a result, the complete system is similarly petite, taking up just 6 rack units of space (or as Sandia likes to compare it to, about the size of a microwave), with a power consumption of 2.6 kW. Now that it’s online, Hala Point has dethroned the SpiNNaker system as the largest disclosed neuromorphic system, offering admittedly just a slightly larger number of neurons at less than 3% of the power consumption of the 100 kW British system.
A Single Loihi 2 Chip (31 mm2)
Hala Point will be replacing an older Intel neuromorphic system at Sandia, Pohoiki Springs, which is based on Intel’s first-generation Loihi chips. By comparison, Hala Point offers ten-times as many neurons, and upwards of 12x the performance overall,
Both neuromorphic systems have been procured by Sandia in order to advance the national lab’s research into neuromorphic computing, a computing paradigm that behaves like a brain. The central thought (if you’ll excuse the pun) is that by mimicking the wetware writing this article, neuromorphic chips can be used to solve problems that conventional processors cannot solve today, and that they can do so more efficiently as well.
Sandia, for its part, has said that it will be using the system to look at large-scale neuromorphic computing, with work operating on a scale well beyond Pohoiki Springs. With Hala Point offering a simulated neuron count very roughly on the level of complexity of an owl brain, the lab believes that a larger-scale system will finally enable them to properly exploit the properties of neuromorphic computing to solve real problems in fields such as device physics, computer architecture, computer science and informatics, moving well beyond the simple demonstrations initially achieved at a smaller scale.
One new focus from the lab, which in turn has caught Intel’s attention, is the applicability of neuromorphic computing towards AI inference. Because the neural networks themselves behind the current wave of AI systems are attempting to emulate the human brain, in a sense, there is an obvious degree of synergy with the brain-mimicking neuromorphic chips, even if the algorithms differ in some key respects. Still, with energy efficiency being one of the major benefits of neuromorphic computing, it’s pushed Intel to look into the matter further – and even build a second, Hala Point-sized system of their own.
According to Intel, in their research on Hala Point, the system has reached efficiencies as high as 15 TOPS-per-Watt at 8-bit precision, albeit while using 10:1 sparsity, making it more than competitive with current-generation commercial chips. As an added bonus to that efficiency, the neuromorphic systems don’t require extensive data processing and batching in advance, which is normally necessary to make efficient use of the high density ALU arrays in GPUs and GPU-like processors.
Perhaps the most interesting use case of all, however, is the potent... CPUs
A Single Loihi 2 Chip (31 mm2)
Intel and Sandia National Labs Roll Out 1.15B Neuron “Hala Point” Neuromorphic Research System
While neuromorphic computing remains under research for the time being, efforts into the field have continued to grow over the years, as have the capabilities of the specialty chips that have been developed for this research. Following those lines, this morning Intel and Sandia National Laboratories are celebrating the deployment of the Hala Point neuromorphic system, which the two believe is the highest capacity system in the world. With 1.15 billion neurons overall, Hala Point is the largest deployment yet for Intel’s Loihi 2 neuromorphic chip, which was first announced at the tail-end of 2021.
The Hala Point system incorporates 1152 Loihi 2 processors, each of which is capable of simulating a million neurons. As noted back at the time of Loihi 2’s launch, these chips are actually rather small – just 31 mm2 per chip with 2.3 billion transistors each, as they’re built on the Intel 4 process (one of the only other Intel chips to do so, besides Meteor Lake). As a result, the complete system is similarly petite, taking up just 6 rack units of space (or as Sandia likes to compare it to, about the size of a microwave), with a power consumption of 2.6 kW. Now that it’s online, Hala Point has dethroned the SpiNNaker system as the largest disclosed neuromorphic system, offering admittedly just a slightly larger number of neurons at less than 3% of the power consumption of the 100 kW British system.
A Single Loihi 2 Chip (31 mm2)
Hala Point will be replacing an older Intel neuromorphic system at Sandia, Pohoiki Springs, which is based on Intel’s first-generation Loihi chips. By comparison, Hala Point offers ten-times as many neurons, and upwards of 12x the performance overall,
Both neuromorphic systems have been procured by Sandia in order to advance the national lab’s research into neuromorphic computing, a computing paradigm that behaves like a brain. The central thought (if you’ll excuse the pun) is that by mimicking the wetware writing this article, neuromorphic chips can be used to solve problems that conventional processors cannot solve today, and that they can do so more efficiently as well.
Sandia, for its part, has said that it will be using the system to look at large-scale neuromorphic computing, with work operating on a scale well beyond Pohoiki Springs. With Hala Point offering a simulated neuron count very roughly on the level of complexity of an owl brain, the lab believes that a larger-scale system will finally enable them to properly exploit the properties of neuromorphic computing to solve real problems in fields such as device physics, computer architecture, computer science and informatics, moving well beyond the simple demonstrations initially achieved at a smaller scale.
One new focus from the lab, which in turn has caught Intel’s attention, is the applicability of neuromorphic computing towards AI inference. Because the neural networks themselves behind the current wave of AI systems are attempting to emulate the human brain, in a sense, there is an obvious degree of synergy with the brain-mimicking neuromorphic chips, even if the algorithms differ in some key respects. Still, with energy efficiency being one of the major benefits of neuromorphic computing, it’s pushed Intel to look into the matter further – and even build a second, Hala Point-sized system of their own.
According to Intel, in their research on Hala Point, the system has reached efficiencies as high as 15 TOPS-per-Watt at 8-bit precision, albeit while using 10:1 sparsity, making it more than competitive with current-generation commercial chips. As an added bonus to that efficiency, the neuromorphic systems don’t require extensive data processing and batching in advance, which is normally necessary to make efficient use of the high density ALU arrays in GPUs and GPU-like processors.
Perhaps the most interesting use case of all, however, is the potent... CPUs
A Single Loihi 2 Chip (31 mm2)
G.Skill Intros Low Latency DDR5 Memory Modules: CL30 at 6400 MT/s 
G.Skill on Tuesday introduced its ultra-low-latency DDR5-6400 memory modules that feature a CAS latency of 30 clocks, which appears to be the industry's most aggressive timings yet for DDR5-6400 sticks. The modules will be available for both AMD and Intel CPU-based systems.
With every new generation of DDR memory comes an increase in data transfer rates and an extension of relative latencies. While for the vast majority of applications, the increased bandwidth offsets the performance impact of higher timings, there are applications that favor low latencies. However, shrinking latencies is sometimes harder than increasing data transfer rates, which is why low-latency modules are rare.
Nonetheless, G.Skill has apparently managed to cherry-pick enough DDR5 memory chips and build appropriate printed circuit boards to produce DDR5-6400 modules with CL30 timings, which are substantially lower than the CL46 timings recommended by JEDEC for this speed bin. This means that while JEDEC-standard modules have an absolute latency of 14.375 ns, G.Skill's modules can boast a latency of just 9.375 ns – an approximately 35% decrease.
G.Skill's DDR5-6400 CL30 39-39-102 modules have a capacity of 16 GB and will be available in 32 GB dual-channel kits, though the company does not disclose voltages, which are likely considerably higher than those standardized by JEDEC.
The company plans to make its DDR5-6400 modules available both for AMD systems with EXPO profiles (Trident Z5 Neo RGB and Trident Z5 Royal Neo) and for Intel-powered PCs with XMP 3.0 profiles (Trident Z5 RGB and Trident Z5 Royal). For AMD AM5 systems that have a practical limitation of 6000 MT/s – 6400 MT/s for DDR5 memory (as this is roughly as fast as AMD's Infinity Fabric can operate at with a 1:1 ratio), the new modules will be particularly beneficial for AMD's Ryzen 7000 and Ryzen 9000-series processors.
G.Skill notes that since its modules are non-standard, they will not work with all systems but will operate on high-end motherboards with properly cooled CPUs.
The new ultra-low-latency memory kits will be available worldwide from G.Skill's partners starting in late August 2024. The company did not disclose the pricing of these modules, but since we are talking about premium products that boast unique specifications, they are likely to be priced accordingly.
Memory
NVIDIA's AD102 GPU Pops Up in MSI GeForce RTX 4070 Ti Super Cards 
As GPU families enter the later part of their lifecycles, we often see chip manufacturers start to offload stockpiles of salvaged chips that, for one reason or another, didn't make the grade for the tier of cards they normally are used in. These recovered chips are fairly unremarkable overall, but they are unsold silicon that still works and has economic value, leading to them being used in lower-tier cards so that they can be sold. And, judging by the appearance of a new video card design from MSI, it looks like NVIDIA's Ada Lovelace generation of chips has reached that stage, as the Taiwanese video card maker has put out a new GeForce RTX 4070 Ti Super card based on a salvaged AD102 GPU.
Typically based on NVIDIA's AD103 GPU, NVIDIA's GeForce RTX 4070 Ti Super series sits a step below the company's flagship RTX 4080/4090 cards, both of which are based on the bigger and badder AD102 chip. But with some number of AD102 chips inevitably failing to live up to RTX 4080 specifications, rather than being thrown out, these chips can instead be used to make RTX 4070 cards. Which is exactly what MSI has done with their new GeForce RTX 4070 Ti Super Ventus 3X Black OC graphics card.
The card itself is relatively unremarkable – using a binned AD102 chip doesn't come with any advantages, and it should perform just like regular AD103 cards – and for that reason, video card vendors rarely publicly note when they're doing a run of cards with a binned-down version of a bigger chip. However, these larger chips have a tell-tale PCB footprint that usually makes it obvious what's going on. Which, as first noticed by @wxnod, is exactly what's going on with MSI's card.
Ada Lovelace Lineup: MSI GeForce RTX 4070 TiS (AD103), RTX 4070 TiS (AD102), & RTX 4090 (AD102)
The tell, in this case, is the rear board shot provided by MSI. The larger AD102 GPU uses an equally larger mounting bracket, and is paired with a slightly more complex array of filtering capacitors on the back side of the board PCB. Ultimately, since these are visible in MSI's photos of their GeForce RTX 4070 Ti Super Ventus 3X Black OC, it's easy to compare it to other video cards and see that it has exactly the same capacitor layout as MSI's GeForce RTX 4090, thus confirming the use of an AD102 GPU.
Chip curiosities aside, all of NVIDIA GeForce RTX 4070 Ti Super graphics cards – no matter whether they are based on the AD102 or AD103 GPU – come with a GPU with 8,448 active CUDA cores and 16 GB of GDDR6X memory, so it doesn't (typically) matter which chip they carry. Otherwise, compared to a fully-enabled AD102 chip, the RTX 4070 Ti Super specifications are relatively modest, with fewer than half as many CUDA cores, underscoring how the AD102 chip being used in MSI's card is a pretty heavy salvage bin.
As for the rest of the card, MSI GeForce RTX 4070 Ti Super Ventus 3X Black OC is a relatively hefty card overall, with a cooling system to match. Being overclocked, the Ventus also has a slightly higher TDP than normal GeForce RTX 4070 Ti Super cards, weighing in at 295 Watts, or 10 Watts above baseline cards.
Meanwhile, MSI is apparently not the only video card manufacturer using salvaged AD102 chips for GeForce RTX 4070 Ti Super, either. @wxnod has also posted a screenshot obtained on an Inno3D GeForce RTX 4070 Ti Super based on an AD102 GPU.
GPUs
Ada Lovelace Lineup: MSI GeForce RTX 4070 TiS (AD103), RTX 4070 TiS (AD102), & RTX 4090 (AD102)
ASMedia Preps USB4 v2 Controller and PHY 
The USB Implementers Forum (USB-IF) introduced USB4 version 2.0 in fall 2022, and it expects systems and devices with the tech to emerge later this year and into next year. These upcoming products will largely rely on Intel's Barlow Ridge controller, a full-featured Thunderbolt 5 controller that goes above and beond the baseline USB4 v2 spec. And though extremely capable, Intel's Thunderbolt controllers are also quite expensive, and Barlow Ridge isn't expected to be any different. Fortunately, for system and device vendors that just need a basic USB4 v2 solution, ASMedia is also working on its own USB4 v2 controller.
At Computex 2024, ASMedia demonstrated a prototype of its upcoming USB4 v2 physical interface (PHY), which will support USB4 v2's new Gen 4 (160Gbps) data rates and the associated PAM-3 signal encoding. The prototype was implemented using an FPGA, as the company yet has to tape out the completed controller.
Ultimately, the purpose of showing off a FPGA-based PHY at Computex was to allow ASMedia to demonstrate their current PHY design. With the shift to PAM-3 encoding for USB4 v2, ASMedia (and the rest of the USB ecosystem) must develop significantly more complex controllers – and there's no part of that more critical than a solid and reliable PHY design.
As part of their demonstration, ASMedia had a classic eye diagram display. The eye diagram demoed has a clear opening in the center, which is indicative of good signal integrity, as the larger the eye opening, the less distortion and noise in the signal. The horizontal width of the eye opening represents the time window in which the signal can be sampled correctly, so the relatively narrow horizontal spread of the eye opening suggests that there is minimal jitter, meaning the signal transitions are consistent and predictable. Finally, the vertical height of the eye opening indicates the signal amplitude and the rather tall eye opening suggests a higher signal-to-noise ratio (SNR), meaning that the signal is strong compared to any noise present.
ASMedia itself is one of the major suppliers for discrete USB controllers, so the availability of ASMedia's USB4 v2 chip is crucial for adoption of the standard in general. While Intel will spearhead the industry with their Barlow Ridge Thunderbolt 5/USB4 v2 controller, ASMedia's controller is poised to end up in a far larger range of devices. So the importance of the company's USB4 v2 PHY demo is hard to overstate.
Demos aside, ASMedia is hoping to tape the chip out soon. If all goes well, the company expects their first USB4 v2 controllers to hit the market some time in the second half of 2025.
Peripherals
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Intel and Sandia National Labs Roll Out 1.15B Neuron “Hala Point” Neuromorphic Research System
While neuromorphic computing remains under research for the time being, efforts into the field have continued to grow over the years, as have the capabilities of the specialty chips that have been developed for this research. Following those lines, this morning Intel and Sandia National Laboratories are celebrating the deployment of the Hala Point neuromorphic system, which the two believe is the highest capacity system in the world. With 1.15 billion neurons overall, Hala Point is the largest deployment yet for Intel’s Loihi 2 neuromorphic chip, which was first announced at the tail-end of 2021.
The Hala Point system incorporates 1152 Loihi 2 processors, each of which is capable of simulating a million neurons. As noted back at the time of Loihi 2’s launch, these chips are actually rather small – just 31 mm2 per chip with 2.3 billion transistors each, as they’re built on the Intel 4 process (one of the only other Intel chips to do so, besides Meteor Lake). As a result, the complete system is similarly petite, taking up just 6 rack units of space (or as Sandia likes to compare it to, about the size of a microwave), with a power consumption of 2.6 kW. Now that it’s online, Hala Point has dethroned the SpiNNaker system as the largest disclosed neuromorphic system, offering admittedly just a slightly larger number of neurons at less than 3% of the power consumption of the 100 kW British system.
A Single Loihi 2 Chip (31 mm2)
Hala Point will be replacing an older Intel neuromorphic system at Sandia, Pohoiki Springs, which is based on Intel’s first-generation Loihi chips. By comparison, Hala Point offers ten-times as many neurons, and upwards of 12x the performance overall,
Both neuromorphic systems have been procured by Sandia in order to advance the national lab’s research into neuromorphic computing, a computing paradigm that behaves like a brain. The central thought (if you’ll excuse the pun) is that by mimicking the wetware writing this article, neuromorphic chips can be used to solve problems that conventional processors cannot solve today, and that they can do so more efficiently as well.
Sandia, for its part, has said that it will be using the system to look at large-scale neuromorphic computing, with work operating on a scale well beyond Pohoiki Springs. With Hala Point offering a simulated neuron count very roughly on the level of complexity of an owl brain, the lab believes that a larger-scale system will finally enable them to properly exploit the properties of neuromorphic computing to solve real problems in fields such as device physics, computer architecture, computer science and informatics, moving well beyond the simple demonstrations initially achieved at a smaller scale.
One new focus from the lab, which in turn has caught Intel’s attention, is the applicability of neuromorphic computing towards AI inference. Because the neural networks themselves behind the current wave of AI systems are attempting to emulate the human brain, in a sense, there is an obvious degree of synergy with the brain-mimicking neuromorphic chips, even if the algorithms differ in some key respects. Still, with energy efficiency being one of the major benefits of neuromorphic computing, it’s pushed Intel to look into the matter further – and even build a second, Hala Point-sized system of their own.
According to Intel, in their research on Hala Point, the system has reached efficiencies as high as 15 TOPS-per-Watt at 8-bit precision, albeit while using 10:1 sparsity, making it more than competitive with current-generation commercial chips. As an added bonus to that efficiency, the neuromorphic systems don’t require extensive data processing and batching in advance, which is normally necessary to make efficient use of the high density ALU arrays in GPUs and GPU-like processors.
Perhaps the most interesting use case of all, however, is the potent... CPUs
A Single Loihi 2 Chip (31 mm2)
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