> ## Documentation Index
> Fetch the complete documentation index at: https://na-36-mintlify-daydream-changelog-2026-09-28.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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](https://github.com/keylase/nvidia-patch) removes the limit on consumer
GPUs and is widely used by orchestrators.

<Warning>
  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.
</Warning>

## 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) |

```bash theme={null}
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

<CardGroup cols={2}>
  <Card title="Configure your orchestrator" icon="sliders" href="/network/guides/orchestrator-configure">
    GPU selection and session-limit flags.
  </Card>

  <Card title="Add AI inference" icon="microchip" href="/network/guides/orchestrator-add-ai">
    Match a pipeline to your available VRAM.
  </Card>
</CardGroup>