Quadro gpu5/17/2023 Similarly, the ex-Tesla V100 makes a great part for provisioning a virtual Quadro instance, even though it’s not a Quadro part.Īs a result, NVIDIA has opted to go the route of essentially merging their compute and ProViz hardware lineups in an effort to simplify their offerings. If you need an actively-cooled desktop card for running neural network prototyping, for example, what card do you buy? Previously it was the Quadro card, despite the fact that it was a ProViz part. One of the consequences of which has been that NVIDIA’s own messaging on what cards can do what tasks has become unfocused, never mind potentially confusing customers. And compute cards, though almost exclusively server-mounted, can be provisioned as a virtual ProViz card as well. As NVIDIA already charges “full” price for both their compute and ProViz cards, there are little-if-any feature differences between the two: desktop ProViz cards have the same access to compute features as compute cards. At the time we suspected that this has to do with the increasing overlap in NVIDIA’s product lines between professional visualization cards and compute cards, and the company has since confirmed that our hunch was correct.Īs NVIDIA has continued to expand into the compute market, their professional visualization (ProViz) and compute products have increasingly overlapped in terms of features and pricing. The more interesting aspect to this change is why: why would NVIDIA retire one of its oldest video card brands after so long? After all, the market for pro cards isn’t going away, and it remains a tidy, profitable business for NVIDIA. Going forward, all of these cards will be given brand-less names, such as the “NVIDIA RTX A6000” and “NVIDIA A40”. Similar to the Tesla brand a couple of years back, the brand is set to be slowly retired from the market, as new professional visualization cards are released without the Quadro branding. At the time it wasn’t clear just what this meant for the Quadro brand as a whole, but now that GTC has wrapped we’ve been given some better insights into what’s going on.įirst and foremost, NVIDIA has confirmed that the Quadro brand is being retired, or “streamlined” as the company calls it. However the card was notably excluded from the Quadro family in something of a last-minute change. Based on the new GA102 GPU, the card ticks all the boxes for a high-end, pro-grade video card and under normal circumstances, it would be part of NVIDIA’s Quadro family of products. In short, NVIDIA has confirmed that the Quadro brand is going away for sure, and as we suspected, it’s largely due to the overlap between graphics and compute.Īs a quick refresher, last week NVIDIA launched their new professional visualization-focused video card, the RTX A6000. If your Jellyfin server does not support hardware acceleration, but you have another machine that does, you can leverage rffmpeg to delegate the transcoding to another machine.Now that NVIDIA’s second GTC event of the year has wrapped up, we’ve finally gotten a chance to follow up with NVIDIA on last week’s announcement of their RTX A6000 video card, and what that means for the Quadro brand. The hardware acceleration is available immediately for media playback. Supported codecs need to be indicated by checking the boxes in Enable hardware decoding for and Hardware encoding options. Select a valid hardware acceleration method from the drop-down menu and a device if applicable. Hardware acceleration options can be found in the Admin Dashboard under the Transcoding section of the Playback tab. The current state of hardware acceleration support in FFmpeg can be checked on the rpi-ffmpeg repository. Jellyfin will fallback to software de/encoding for those usecases. This decision was made because Raspberry Pi is currently migrating to a V4L2 based hardware acceleration, which is already available in Jellyfin but does not support all features other hardware acceleration methods provide due to lacking support in FFmpeg. Video Scaling & Format conversion (optional)Īs of Jellyfin 10.8 hardware acceleration on Raspberry Pi via OpenMAX OMX was dropped and is no longer available. The transcoding pipeline usually has multiple stages, which can be simplified to: Raspberry Pi Video4Linux2 (V4L2, Linux only) Intel/AMD Video Acceleration API (VA-API, Linux only) The supported and validated video hardware acceleration (HWA) methods are: It enables the Jellyfin server to access the fixed-function video codecs, video processors and GPGPU computing interfaces provided by vendor of the installed GPU and the operating system. The Jellyfin server uses a modified version of FFmpeg as its transcoder, namely jellyfin-ffmpeg. The Jellyfin server can offload on the fly video transcoding by utilizing an integrated or discrete graphics card ( GPU) suitable to accelerate this workloads very efficiently without straining your CPU.
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