| Q | A | |---|---| | Can Venx‑287‑RM‑JavHD handle mixed codecs in the same pipeline? | Yes. The RM scheduler can branch a single input into separate encoder instances (e.g., HEVC for broadcast, AV1 for streaming) without exceeding the latency budget. | | What is the maximum cable length for HDMI 2.1 inputs? | Up to with active cables; for longer runs use HDMI‑over‑SDI converters. | | Is there a way to disable the AI‑upscale for power‑saving? | The AI engine can be toggled via the API: POST /api/v1/pipelines/id/features "ai_upscale": false . This frees ~30 % of GPU resources. | | Does the appliance support TPM 2.0 for secure boot? | Yes, a hardware TPM is built‑in and enabled by default. | | Can we run the platform in a virtualized environment? | The current hardware design is bare‑metal only ; however, a future “Venx‑Cloud‑RM” SaaS offering is planned for Q4 2025. |
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| Scenario | Configuration Highlights | Benefits | |----------|--------------------------|----------| | | 4K 60 fps HDMI/SDI ingest, AI‑up‑scale for legacy feeds, dual‑stream redundant output (HEVC + RTMP). | Sub‑10 ms delay, automatic fail‑over, minimal bandwidth penalty. | | Remote Surgery Tele‑medicine | 1080p 120 fps NDI input, low‑latency denoise, encrypted SRT output. | ≤ 7 ms round‑trip, ensures visual fidelity under variable network conditions. | | VR/AR Content Production | 8K 30 fps capture, on‑the‑fly AV1 encoding, GPU‑accelerated stitching. | Future‑proof codec, reduces storage costs while preserving visual quality. | | Edge Computing for Smart Cities | 4× 1080p 30 fps RTSP cameras, AI‑up‑scale for facial‑recognition pipelines. | Centralized processing reduces bandwidth to cloud, meets privacy regulations. | | Q | A | |---|---| | Can
Based on its format, the identifier appears to be broken down into key components: | | What is the maximum cable length for HDMI 2