Intel Arc A580 Review: New Budget Contender

Sparkle Arc A580 Orc OC Card

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There's no Intel reference Arc A580, but we received the Sparkle Arc A580 Orc OC for this review. There are at least two other A580 cards coming out today, an ASRock model and one from Gunnir, but we haven't tested either of those. We suspect performance will largely be the same as this Sparkle card, though noise levels, temperatures, and lighting options are going to be different.

This is also the first ever review we've had a of a Sparkle graphics card. The company entered the GPU market earlier this year, and so far it's only selling Intel Arc-based cards. It covers nearly the whole suite of offerings, with the A770 8GB being the only omission. Besides graphics cards, Sparkle also dabbles in motherboards, FPGAs, and Thunderbolt/USB docks. Sparkle Power Inc. has also been selling PSUs for decades.

The packaging is pretty typical for a graphics card. There's plenty of foam padding, and the card comes in an anti-static bag. Bonus points (from me) for not putting plastic cling wrap all over the card. Besides the card, there's an Intel free game activation code included... or at least there was in our review sample. Except, the card says it's only valid until July 10, 2023. Hopefully that date gets extended for anyone else who wants Ghostbusters: Spirits Unleashed or Gotham Knights. (Neither game fared very well among critics, if you're wondering.)

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The Sparkle Arc A580 Orc OC measures 223x113x39.5 mm, and weighs 738g. It's relatively compact, in other words, though not exactly small. It's a pretty boxy design as well, eschewing fancy curves and any RGB lighting in favor of functionality.

The card has two fans, both the same moderately large 89mm model with an integrated rim. That's good to see, as often less expensive graphics cards opt for cheaper and less effective fans that need to spin at higher RPMs to keep the GPU cool. Except the weight of the card suggests maybe the fans might need to work harder to keep the GPU cool — just under 740g for a card that can pull around 200W suggests the heatsink and heatpipes aren't as robust as we've seen on cards from other manufacturers.

Somewhat curiously, the Sparkle A580 comes with dual 8-pin power connectors. We suspect that's because it reuses the existing A750 Orc design, though even there an 8-pin plus 6-pin configuration should have been more than sufficient. At the same time, 8-pin connectors are pretty ubiquitous these days, usually via 6-pin plus 2-pin connectors. Perhaps Sparkle just felt it was easier to only use 8-pin connectors.

The only lighting on the card is the Sparkle logo on top, which lights up when powered on. In an interesting twist, the color of the lighting changes based on GPU temperature, ranging from white (coolest) to blue, then on to green, yellow, orange, and red. During testing, it mostly stayed blue in our experience, though at first power on it was white (coolest).

Intel Arc A580 Test Setup

We updated our GPU test PC at the end of last year with a Core i9-13900K, though we continue to also test reference GPUs on our 2022 system that includes a Core i9-12900K for our GPU benchmarks hierarchy. (We'll be updating that with the A580 numbers in the next few days; we haven't yet run those tests due to time constraints.) For this review, we use the 13900K results for gaming tests, which ensures, as much as possible, that we're not CPU limited. We also use the 2022 PC for AI tests and professional workloads.

We're using preview drivers from Intel, version 4830. The latest public Intel drivers are 4885, but those don't support the A580. Results from other cards are from the past few months, and we've retested a few specific games where there were apparent anomalies in performance, including retesting the Arc A750 using the same 4830 drivers.

We're including current generation and previous generation AMD and Nvidia GPUs in our charts, nominally priced in the $180–$300 range. Obviously, the Arc A580 falls at the bottom of that price range and thus we shouldn't expect it to top the performance charts. What we really want to know is how it stacks up against the similarly priced RX 6600, and the more expensive RX 6650 XT and RTX 3050/3060.

Our current test suite consists of 15 games. Of these, nine support DirectX Raytracing (DXR), but we only enable the DXR features in six games. The remaining nine games are tested in pure rasterization mode. While many of the games in our test suite support upscaling — twelve support DLSS 2, five support DLSS 3, five support FSR 2, and four support XeSS — we're primarily interested in native resolution performance. GPUs generally show similar scaling if you enable DLSS/FSR2/XeSS.

We tested the Arc A580 at 1080p (medium and ultra), 1440p ultra, and 4K ultra in a few games — ultra being the highest supported preset if there is one, and in some cases maxing out all the other settings for good measure (except for MSAA or super sampling). This is primarily a 1080p gaming solution, however, so that will be our focus.

Our PC is hooked up to a Samsung Odyssey Neo G8 32, one of the best gaming monitors around, allowing us to fully experience some of the higher frame rates that might be available. G-Sync and FreeSync were enabled, as appropriate. As you can imagine, getting anywhere close to the 240 Hz limit of the monitor proved difficult, as we don't have any esports games in our test suite.

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We installed all the then-latest Windows 11 updates when we assembled the new test PC. We're running Windows 11 22H2, but we've used InControl to lock our test PC to that major release for the foreseeable future (though critical security updates still get installed monthly).

Our new test PC includes Nvidia's PCAT v2 (Power Capture and Analysis Tool) hardware, which means we can grab real power use, GPU clocks, and more during all of our gaming benchmarks. We'll cover those results on our page on power use.

Finally, because GPUs aren't purely for gaming these days, we've run some professional application tests, and we also ran some Stable Diffusion benchmarks to see how AI workloads scale on the various GPUs.

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