cuframes

gx/cuframes

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Author	SHA1	Message	Date
gx	52fb2ad722	benchmarks: actual measured VRAM + network bandwidth (tcpdump-based) VRAM breakdown (nvidia-smi pmon): - 4 publishers = 4.4 GB (FHD + 2688x1520 ring buffers + NVDEC) - cctv-backend = 1.0 GB - frigate embeddings_manager = 1.6 GB - frigate detector:onnx = 0.6 GB - Total cuframes-stack = ~7.7 GB Network (10-sec tcpdump capture от camera subnet к R9): - Measured: 31.5 Mbps (всё включая go2rtc on-demand, ONVIF) - cuframes core: ~16 Mbps (4 publishers × main HEVC) - ONVIF/RTSP keepalives: ~1-2 Mbps - Без cuframes setup тех же 4 cam × 3 consumer был бы ~45-50 Mbps Source: production deploy 2026-05-19 measurement.	2026-05-19 19:22:53 +01:00
gx	3779175737	docs(benchmarks): production v0.2 deploy metrics (4 cam × 3 consumer) Real-world numbers с production deploy 2026-05-19: - RTSP к камерам: 12 → 4 (−67%) - NVDEC sessions: 8 → 4 (−50%) - Camera bandwidth: 34 → 16 Mbps (−54%) - PCIe D2H copies: 346 MB/s → ~0 (−100% через zero-copy CUDA IPC) - Frigate прямые RTSP: 8 → 0 (−100%) Plus live nvidia-smi metrics, что сохранилось vs не сэкономлено, projection table для других setup'ов (8/16 cam × 2/3/4 consumer). Для promotional material — public-facing claims на основе measured deploy.	2026-05-19 19:07:16 +01:00
gx	a3ba3a95b2	docs: ROADMAP + CHANGELOG v0.1.0 + BENCHMARKS - ROADMAP.md: structured v0.1✅ / v0.2📋 (encoded packet sharing + FFmpeg upstream PR + scale-cuda alt) / v0.3 (Python bindings, Jetson, multi-GPU) / v1.0 (stable ABI) - CHANGELOG.md: full v0.1.0 release notes — features, tested config, production deployment, known limitations - BENCHMARKS.md: measurements (stress 1×pub×4×sub, E2E real camera, prod multi-consumer 24h, VRAM cost per resolution, cuframes vs N×NVDEC) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>	2026-05-18 21:11:37 +01:00

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52fb2ad722

benchmarks: actual measured VRAM + network bandwidth (tcpdump-based)

VRAM breakdown (nvidia-smi pmon):
- 4 publishers = 4.4 GB (FHD + 2688x1520 ring buffers + NVDEC)
- cctv-backend = 1.0 GB
- frigate embeddings_manager = 1.6 GB
- frigate detector:onnx = 0.6 GB
- Total cuframes-stack = ~7.7 GB

Network (10-sec tcpdump capture от camera subnet к R9):
- Measured: 31.5 Mbps (всё включая go2rtc on-demand, ONVIF)
- cuframes core: ~16 Mbps (4 publishers × main HEVC)
- ONVIF/RTSP keepalives: ~1-2 Mbps
- Без cuframes setup тех же 4 cam × 3 consumer был бы ~45-50 Mbps

Source: production deploy 2026-05-19 measurement.

2026-05-19 19:22:53 +01:00

3779175737

docs(benchmarks): production v0.2 deploy metrics (4 cam × 3 consumer)

Real-world numbers с production deploy 2026-05-19:
- RTSP к камерам: 12 → 4 (−67%)
- NVDEC sessions: 8 → 4 (−50%)
- Camera bandwidth: 34 → 16 Mbps (−54%)
- PCIe D2H copies: 346 MB/s → ~0 (−100% через zero-copy CUDA IPC)
- Frigate прямые RTSP: 8 → 0 (−100%)

Plus live nvidia-smi metrics, что сохранилось vs не сэкономлено,
projection table для других setup'ов (8/16 cam × 2/3/4 consumer).

Для promotional material — public-facing claims на основе measured deploy.

2026-05-19 19:07:16 +01:00

a3ba3a95b2

docs: ROADMAP + CHANGELOG v0.1.0 + BENCHMARKS

- ROADMAP.md: structured v0.1✅ / v0.2📋 (encoded packet sharing + FFmpeg
  upstream PR + scale-cuda alt) / v0.3 (Python bindings, Jetson, multi-GPU)
  / v1.0 (stable ABI)
- CHANGELOG.md: full v0.1.0 release notes — features, tested config,
  production deployment, known limitations
- BENCHMARKS.md: measurements (stress 1×pub×4×sub, E2E real camera,
  prod multi-consumer 24h, VRAM cost per resolution, cuframes vs N×NVDEC)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

2026-05-18 21:11:37 +01:00

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