diff --git a/v2/orchestrators/resources/gpu-support.mdx b/v2/orchestrators/resources/gpu-support.mdx
index 2f9240416..178ff658d 100644
--- a/v2/orchestrators/resources/gpu-support.mdx
+++ b/v2/orchestrators/resources/gpu-support.mdx
@@ -13,6 +13,8 @@ keywords:
- session limits
- RTX
- transcoding
+ - HEVC
+ - H.265
- AI inference
'og:image': /snippets/assets/site/og-image/en/orchestrators.png
'og:image:alt': Livepeer Docs social preview image for Orchestrators
@@ -23,7 +25,7 @@ pageType: reference
audience: orchestrator
purpose: reference
status: review
-lastVerified: 2026-03-13
+lastVerified: 2026-04-07
---
{/* TODO:
Terminology Validation:
@@ -64,6 +66,7 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
GPU Family
Transcoding
+ HEVC Encode
AI Inference
Notes
@@ -71,29 +74,34 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
**GeForce RTX 40xx** (Ada Lovelace)
Yes
Yes
- Best consumer option. AV1 encode support.
+ Yes
+ Best consumer option. AV1 and HEVC 10-bit encode support.
**GeForce RTX 30xx** (Ampere)
Yes
Yes
+ Yes
Widely used by orchestrators. Good price-performance.
**GeForce RTX 20xx** (Turing)
Yes
Yes
- Supported but older.
+ Yes
+ Supported but older. HEVC B-frames supported.
**GeForce GTX 16xx** (Turing)
Yes
+ Yes
Limited
- No Tensor cores — AI inference slower or unsupported for some pipelines.
+ No Tensor cores - AI inference slower or unsupported for some pipelines.
**GeForce GTX 10xx** (Pascal)
Yes
+ Yes
Limited
Legacy. NVENC Gen 6. No Tensor cores.
@@ -101,42 +109,49 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
**Tesla T4**
Yes
Yes
+ Yes
Data centre card. 16 GB VRAM. Common in cloud.
**Tesla V100**
Yes
Yes
+ Yes
Data centre. 16/32 GB VRAM.
**A100**
Yes
Yes
+ Yes
Data centre. 40/80 GB VRAM. Highest throughput.
**A10 / A10G**
Yes
Yes
+ Yes
Cloud-optimised (AWS G5, etc.). 24 GB VRAM.
**L4**
Yes
Yes
+ Yes
Ada Lovelace data centre. 24 GB VRAM. Good for AI.
**L40 / L40S**
Yes
Yes
+ Yes
48 GB VRAM. High-end AI and transcoding.
**H100**
Transcoding works but overkill
Yes
+ Yes
80 GB VRAM. Primarily for LLM and large model inference.
@@ -314,6 +329,96 @@ For detailed per-pipeline VRAM planning, see the [Model and Demand Reference](/v
+## Popular GPUs for AI Workloads
+
+The following GPUs are well-suited for AI inference on the Livepeer network. VRAM is the primary constraint - larger models require more VRAM, and running multiple warm models simultaneously multiplies the requirement.
+
+
+
+ GPU Model
+ VRAM
+ HEVC Encode
+ Best For
+ Notes
+
+
+ **RTX 4090**
+ 24 GB
+ Yes
+ Image/video AI, quantised LLMs
+ Top consumer GPU. High throughput for diffusion models.
+
+
+ **RTX 3090 / 3090 Ti**
+ 24 GB
+ Yes
+ Image/video AI, quantised LLMs
+ Best value 24 GB option. Widely used in the Livepeer network.
+
+
+ **RTX 4070 Ti Super**
+ 16 GB
+ Yes
+ Single warm AI model + transcoding
+ Good balance of price, power, and VRAM.
+
+
+ **RTX 4080 Super**
+ 16 GB
+ Yes
+ Single warm AI model + transcoding
+ Higher CUDA core count than 4070 Ti Super.
+
+
+ **Tesla T4**
+ 16 GB
+ Yes
+ Cloud AI inference
+ Efficient data centre card. Common in AWS/GCP/Azure.
+
+
+ **A10G**
+ 24 GB
+ Yes
+ Cloud AI inference + transcoding
+ AWS G5 instance GPU. Strong diffusion model performance.
+
+
+ **L4**
+ 24 GB
+ Yes
+ Cloud AI inference + transcoding
+ Ada Lovelace. Efficient power draw. Common in GCP.
+
+
+ **L40S**
+ 48 GB
+ Yes
+ Multi-model AI, large LLMs
+ 48 GB VRAM allows multiple warm models simultaneously.
+
+
+ **A100 SXM/PCIe**
+ 40/80 GB
+ Yes
+ Large LLMs, high-throughput inference
+ Data centre. Highest AI throughput available at scale.
+
+
+ **H100 SXM/PCIe**
+ 80 GB
+ Yes
+ Very large LLMs, maximum throughput
+ Data centre. ~3x faster than A100 for LLM inference.
+
+
+
+
+ For orchestrators running both AI pipelines and video transcoding, prioritise VRAM when selecting a GPU. A 24 GB card such as the RTX 3090, A10G, or L4 provides headroom for one or two warm AI models alongside active transcoding sessions.
+
+
+
+
## See Also
diff --git a/v2/orchestrators/resources/reference/gpu-support.mdx b/v2/orchestrators/resources/reference/gpu-support.mdx
index 128169e79..e39c73448 100644
--- a/v2/orchestrators/resources/reference/gpu-support.mdx
+++ b/v2/orchestrators/resources/reference/gpu-support.mdx
@@ -13,6 +13,8 @@ keywords:
- session limits
- RTX
- transcoding
+ - HEVC
+ - H.265
- AI inference
'og:image': /snippets/assets/media/og-images/en/orchestrators.png
'og:image:alt': Livepeer Docs social preview image for Orchestrators
@@ -23,7 +25,7 @@ pageType: reference
audience: orchestrator
purpose: reference
status: review
-lastVerified: 2026-03-13
+lastVerified: 2026-04-07
---
{/* TODO:
Terminology Validation:
@@ -64,6 +66,7 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
GPU Family
Transcoding
+ HEVC Encode
AI Inference
Notes
@@ -71,29 +74,34 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
**GeForce RTX 40xx** (Ada Lovelace)
Yes
Yes
- Best consumer option. AV1 encode support.
+ Yes
+ Best consumer option. AV1 and HEVC 10-bit encode support.
**GeForce RTX 30xx** (Ampere)
Yes
Yes
+ Yes
Widely used by orchestrators. Good price-performance.
**GeForce RTX 20xx** (Turing)
Yes
Yes
- Supported but older.
+ Yes
+ Supported but older. HEVC B-frames supported.
**GeForce GTX 16xx** (Turing)
Yes
+ Yes
Limited
- No Tensor cores — AI inference slower or unsupported for some pipelines.
+ No Tensor cores - AI inference slower or unsupported for some pipelines.
**GeForce GTX 10xx** (Pascal)
Yes
+ Yes
Limited
Legacy. NVENC Gen 6. No Tensor cores.
@@ -101,42 +109,49 @@ go-livepeer requires NVIDIA GPUs with NVENC and NVDEC support. AMD and Intel GPU
**Tesla T4**
Yes
Yes
+ Yes
Data centre card. 16 GB VRAM. Common in cloud.
**Tesla V100**
Yes
Yes
+ Yes
Data centre. 16/32 GB VRAM.
**A100**
Yes
Yes
+ Yes
Data centre. 40/80 GB VRAM. Highest throughput.
**A10 / A10G**
Yes
Yes
+ Yes
Cloud-optimised (AWS G5, etc.). 24 GB VRAM.
**L4**
Yes
Yes
+ Yes
Ada Lovelace data centre. 24 GB VRAM. Good for AI.
**L40 / L40S**
Yes
Yes
+ Yes
48 GB VRAM. High-end AI and transcoding.
**H100**
Transcoding works but overkill
Yes
+ Yes
80 GB VRAM. Primarily for LLM and large model inference.
@@ -314,6 +329,96 @@ For detailed per-pipeline VRAM planning, see the [Model and Demand Reference](/v
+## Popular GPUs for AI Workloads
+
+The following GPUs are well-suited for AI inference on the Livepeer network. VRAM is the primary constraint - larger models require more VRAM, and running multiple warm models simultaneously multiplies the requirement.
+
+
+
+ GPU Model
+ VRAM
+ HEVC Encode
+ Best For
+ Notes
+
+
+ **RTX 4090**
+ 24 GB
+ Yes
+ Image/video AI, quantised LLMs
+ Top consumer GPU. High throughput for diffusion models.
+
+
+ **RTX 3090 / 3090 Ti**
+ 24 GB
+ Yes
+ Image/video AI, quantised LLMs
+ Best value 24 GB option. Widely used in the Livepeer network.
+
+
+ **RTX 4070 Ti Super**
+ 16 GB
+ Yes
+ Single warm AI model + transcoding
+ Good balance of price, power, and VRAM.
+
+
+ **RTX 4080 Super**
+ 16 GB
+ Yes
+ Single warm AI model + transcoding
+ Higher CUDA core count than 4070 Ti Super.
+
+
+ **Tesla T4**
+ 16 GB
+ Yes
+ Cloud AI inference
+ Efficient data centre card. Common in AWS/GCP/Azure.
+
+
+ **A10G**
+ 24 GB
+ Yes
+ Cloud AI inference + transcoding
+ AWS G5 instance GPU. Strong diffusion model performance.
+
+
+ **L4**
+ 24 GB
+ Yes
+ Cloud AI inference + transcoding
+ Ada Lovelace. Efficient power draw. Common in GCP.
+
+
+ **L40S**
+ 48 GB
+ Yes
+ Multi-model AI, large LLMs
+ 48 GB VRAM allows multiple warm models simultaneously.
+
+
+ **A100 SXM/PCIe**
+ 40/80 GB
+ Yes
+ Large LLMs, high-throughput inference
+ Data centre. Highest AI throughput available at scale.
+
+
+ **H100 SXM/PCIe**
+ 80 GB
+ Yes
+ Very large LLMs, maximum throughput
+ Data centre. ~3x faster than A100 for LLM inference.
+
+
+
+
+ For orchestrators running both AI pipelines and video transcoding, prioritise VRAM when selecting a GPU. A 24 GB card such as the RTX 3090, A10G, or L4 provides headroom for one or two warm AI models alongside active transcoding sessions.
+
+
+
+
## See Also