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Details on AMD’s $549 and $599 Radeon RX 9070 GPUs, which aim at Nvidia and 4K

AMD is releasing the first detailed specifications of its next-generation Radeon RX 9070 series GPUs and the RDNA4 graphics architecture today, almost two months after teasing them at CES.

The short version is that these are both upper-midrange graphics cards targeting resolutions of 1440p and 4K and meant to compete mainly with Nvidia’s incoming and outgoing 4070- and 5070-series GeForce GPUs, including the RTX 4070, RTX 5070, RTX 4070 Ti and Ti Super, and the RTX 5070 Ti.

AMD says the RX 9070 will start at $549, the same price as Nvidia’s RTX 5070. The slightly faster 9070 XT starts at $599, $150 less than the RTX 5070 Ti. The cards go on sale March 6, a day after Nvidia’s RTX 5070.

Neither Nvidia nor Intel has managed to keep its GPUs in stores at their announced starting prices so far, though, so how well AMD’s pricing stacks up to Nvidia in the real world may take a few weeks or months to settle out. For its part, AMD says it’s confident that it has enough supply to meet demand, but that’s as specific as the company’s reassurances got.

Specs and speeds: Radeon RX 9070 and 9070 XT

RX 9070 XT RX 9070 RX 7900 XTX RX 7900 XT RX 7900 GRE RX 7800 XT
Compute units (Stream processors) 64 RDNA4 (4,096) 56 RDNA4 (3,584) 96 RDNA3 (6,144) 84 RDNA3 (5,376) 80 RDNA3 (5,120) 60 RDNA3 (3,840)
Boost Clock 2,970 MHz 2,520 MHz 2,498 MHz 2,400 MHz 2,245 MHz 2,430 MHz
Memory Bus Width 256-bit 256-bit 384-bit 320-bit 256-bit 256-bit
Memory Bandwidth 650 GB/s 650 GB/s 960 GB/s 800 GB/s 576 GB/s 624 GB/s
Memory size 16GB GDDR6 16GB GDDR6 24GB GDDR6 20GB GDDR6 16GB GDDR6 16GB GDDR6
Total board power (TBP) 304 W 220 W 355 W 315 W 260 W 263 W

As is implied by their similar price tags, the 9070 and 9070 XT have more in common than not. Both are based on the same GPU die—the 9070 has 56 of the chip’s compute units enabled, while the 9070 XT has 64. Both cards come with 16GB of RAM (4GB more than the 5070, the same amount as the 5070 Ti) on a 256-bit memory bus, and both use two 8-pin power connectors by default, though the 9070 XT can use significantly more power than the 9070 (304 W, compared to 220 W).

AMD says that its partners are free to make Radeon cards with the 12VHPWR or 12V-2×6 power connectors on them, though given the apparently ongoing issues with the connector, we’d expect most Radeon GPUs to stick with the known quantity that is the 8-pin connector.

AMD says that the 9070 series is made using a 4 nm TSMC manufacturing process and that the chips are monolithic rather than being split up into chiplets as some RX 7000-series cards were. AMD’s commitment to its memory controller chiplets was always hit or miss with the 7000-series—the high-end cards tended to use them, while the lower-end GPUs were usually monolithic—so it’s not clear one way or the other whether this means AMD is giving up on chiplet-based GPUs altogether or if it’s just not using them this time around.

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Intel’s “Gaudi 3” AI accelerator chip may give Nvidia’s H100 a run for its money

Adventures in Matrix Multiplication —

Intel claims 50% more speed when running AI language models vs. the market leader.

An Intel handout photo of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout photo of the Gaudi 3 AI accelerator.

On Tuesday, Intel revealed a new AI accelerator chip called Gaudi 3 at its Vision 2024 event in Phoenix. With strong claimed performance while running large language models (like those that power ChatGPT), the company has positioned Gaudi 3 as an alternative to Nvidia’s H100, a popular data center GPU that has been subject to shortages, though apparently that is easing somewhat.

Compared to Nvidia’s H100 chip, Intel projects a 50 percent faster training time on Gaudi 3 for both OpenAI’s GPT-3 175B LLM and the 7-billion parameter version of Meta’s Llama 2. In terms of inference (running the trained model to get outputs), Intel claims that its new AI chip delivers 50 percent faster performance than H100 for Llama 2 and Falcon 180B, which are both relatively popular open-weights models.

Intel is targeting the H100 because of its high market share, but the chip isn’t Nvidia’s most powerful AI accelerator chip in the pipeline. Announcements of the H200 and the Blackwell B200 have since surpassed the H100 on paper, but neither of those chips is out yet (the H200 is expected in the second quarter of 2024—basically any day now).

Meanwhile, the aforementioned H100 supply issues have been a major headache for tech companies and AI researchers who have to fight for access to any chips that can train AI models. This has led several tech companies like Microsoft, Meta, and OpenAI (rumor has it) to seek their own AI-accelerator chip designs, although that custom silicon is typically manufactured by either Intel or TSMC. Google has its own line of tensor processing units (TPUs) that it has been using internally since 2015.

Given those issues, Intel’s Gaudi 3 may be a potentially attractive alternative to the H100 if Intel can hit an ideal price (which Intel has not provided, but an H100 reportedly costs around $30,000–$40,000) and maintain adequate production. AMD also manufactures a competitive range of AI chips, such as the AMD Instinct MI300 Series, that sell for around $10,000–$15,000.

Gaudi 3 performance

An Intel handout featuring specifications of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout featuring specifications of the Gaudi 3 AI accelerator.

Intel says the new chip builds upon the architecture of its predecessor, Gaudi 2, by featuring two identical silicon dies connected by a high-bandwidth connection. Each die contains a central cache memory of 48 megabytes, surrounded by four matrix multiplication engines and 32 programmable tensor processor cores, bringing the total cores to 64.

The chipmaking giant claims that Gaudi 3 delivers double the AI compute performance of Gaudi 2 using 8-bit floating-point infrastructure, which has become crucial for training transformer models. The chip also offers a fourfold boost for computations using the BFloat 16-number format. Gaudi 3 also features 128GB of the less expensive HBMe2 memory capacity (which may contribute to price competitiveness) and features 3.7TB of memory bandwidth.

Since data centers are well-known to be power hungry, Intel emphasizes the power efficiency of Gaudi 3, claiming 40 percent greater inference power-efficiency across Llama 7B and 70B parameters, and Falcon 180B parameter models compared to Nvidia’s H100. Eitan Medina, chief operating officer of Intel’s Habana Labs, attributes this advantage to Gaudi’s large-matrix math engines, which he claims require significantly less memory bandwidth compared to other architectures.

Gaudi vs. Blackwell

An Intel handout photo of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout photo of the Gaudi 3 AI accelerator.

Last month, we covered the splashy launch of Nvidia’s Blackwell architecture, including the B200 GPU, which Nvidia claims will be the world’s most powerful AI chip. It seems natural, then, to compare what we know about Nvidia’s highest-performing AI chip to the best of what Intel can currently produce.

For starters, Gaudi 3 is being manufactured using TSMC’s N5 process technology, according to IEEE Spectrum, narrowing the gap between Intel and Nvidia in terms of semiconductor fabrication technology. The upcoming Nvidia Blackwell chip will use a custom N4P process, which reportedly offers modest performance and efficiency improvements over N5.

Gaudi 3’s use of HBM2e memory (as we mentioned above) is notable compared to the more expensive HBM3 or HBM3e used in competing chips, offering a balance of performance and cost-efficiency. This choice seems to emphasize Intel’s strategy to compete not only on performance but also on price.

As far as raw performance comparisons between Gaudi 3 and the B200, that can’t be known until the chips have been released and benchmarked by a third party.

As the race to power the tech industry’s thirst for AI computation heats up, IEEE Spectrum notes that the next generation of Intel’s Gaudi chip, code-named Falcon Shores, remains a point of interest. It also remains to be seen whether Intel will continue to rely on TSMC’s technology or leverage its own foundry business and upcoming nanosheet transistor technology to gain a competitive edge in the AI accelerator market.

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