Hardware & GPU support

NVIDIA GPU compatibility, NVENC session limits, VRAM requirements by workload, and driver versions for orchestrators.

Livepeer orchestrators use NVIDIA GPUs for video transcoding (NVENC/NVDEC) and AI inference (CUDA / Tensor cores). AMD and Intel GPUs are not supported.

Supported GPUs

GPU family	Transcoding	AI inference	Notes
GeForce RTX 40xx (Ada)	Yes	Yes	Best consumer option; AV1 encode
GeForce RTX 30xx (Ampere)	Yes	Yes	Widely used; good price/performance
GeForce RTX 20xx (Turing)	Yes	Yes	Supported but older
GeForce GTX 16xx (Turing)	Yes	Limited	No Tensor cores — AI slower/unsupported for some pipelines
GeForce GTX 10xx (Pascal)	Yes	Limited	Legacy; NVENC Gen 6; no Tensor cores
Tesla T4	Yes	Yes	Data center, 16 GB, common in cloud
Tesla V100	Yes	Yes	Data center, 16/32 GB
A100	Yes	Yes	Data center, 40/80 GB, highest throughput
A10 / A10G	Yes	Yes	Cloud-optimized (AWS G5), 24 GB
L4	Yes	Yes	Ada data center, 24 GB, good for AI
L40 / L40S	Yes	Yes	48 GB, high-end AI + transcoding
H100	Overkill	Yes	80 GB, primarily LLM / large-model inference

NVENC session limits

Consumer GPUs cap concurrent NVENC encode sessions, which limits simultaneous transcode streams per GPU.

GPU class	Default NVENC sessions
GeForce GTX 10xx	2	Can be patched
GeForce GTX 16xx	3	Can be patched
GeForce RTX 20xx	3	Can be patched
GeForce RTX 30xx	3–5 (by model)	Can be patched
GeForce RTX 40xx	3–8 (by model)	Can be patched
Tesla / Quadro / A-series	Unlimited	No session limit

The community nvidia-patch removes the limit on consumer GPUs and is widely used by orchestrators.

Patching modifies a system binary, is unsupported by NVIDIA, must be re-applied after driver updates, and may be disallowed on some managed cloud GPU instances.

VRAM by workload

Workload	Minimum VRAM	Recommended	Notes
Video transcoding only	4 GB	8 GB	NVENC/NVDEC uses minimal VRAM
Batch AI (single warm model)	8 GB	16 GB	SDXL needs ~7 GB
Batch AI (multiple warm models)	16 GB	24 GB+	Each warm model consumes VRAM simultaneously
LLM inference (quantized)	8 GB	16 GB	Via Ollama runner, quantized weights
LLM inference (full precision)	24 GB+	48 GB+	Large models at full precision
Real-time AI (ComfyStream)	12 GB	16 GB+	Latency-sensitive; headroom improves stability

Driver & toolkit versions

Component	Minimum	Notes
NVIDIA driver	525+
CUDA toolkit	12.0+
NVIDIA Container Toolkit	Latest	Required for Docker (AI runner, containerized orchestrator)

nvidia-smi          # driver version
nvcc --version      # CUDA version
docker run --gpus all nvidia/cuda:12.0-base nvidia-smi   # Docker GPU access

GPU selection guidance

Goal	Pick
Transcoding only, budget	GTX 1660 Super (6 GB); patch the NVENC limit for more sessions
Transcoding + AI	RTX 4070 Ti Super (16 GB) or RTX 3090 (24 GB) — 24 GB runs 2–3 warm models + transcoding
AI-heavy / LLM	RTX 4090 (24 GB), or A100 / L40S in a data center

Related

Configure your orchestrator

GPU selection and session-limit flags.

Add AI inference

Match a pipeline to your available VRAM.