{
  "schema_version": "1.0.0",
  "license": "CC-BY-4.0",
  "attribution": "CanIEmbody (https://caniembody.pages.dev)",
  "dataset_version": "1.0.0-seed",
  "generated": "2026-10-25",
  "data": [
    {
      "id": "a100-40gb",
      "name": "NVIDIA A100 40GB",
      "class": "datacenter",
      "memory_gb": 40,
      "arch": "Ampere",
      "ai_tops": "BFLOAT16 Tensor Core 312 TFLOPS | 624 TFLOPS* (*with sparsity)",
      "power_w": "250W (PCIe) / 400W (SXM)",
      "notes": "HBM2, PCIe or SXM4. NVIDIA's current A100 product page lists only the 80GB variants; the 40GB figures come from the A100 datasheet. Not offered as a Hugging Face Jobs flavor (Jobs' A100 is the 80 GB part). Many papers say just \"A100\" without the memory variant; such runs cannot be filed here.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-a100-datasheet-columns",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf",
          "verified_on": "2026-10-24",
          "summary": "A100 datasheet spec table column order.",
          "quote": "40GB PCIe 80GB PCIe 40GB SXM 80GB SXM"
        },
        {
          "id": "ev-nvidia-a100-datasheet-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf",
          "verified_on": "2026-10-24",
          "summary": "A100 datasheet GPU Memory row (40GB PCIe, 80GB PCIe, 40GB SXM, 80GB SXM).",
          "quote": "GPU Memory 40GB 80GB 40GB 80GB"
        },
        {
          "id": "ev-nvidia-a100-datasheet-tdp",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf",
          "verified_on": "2026-10-24",
          "summary": "A100 datasheet Max TDP row (40GB PCIe, 80GB PCIe, 40GB SXM, 80GB SXM).",
          "quote": "250W 300W 400W 400W"
        },
        {
          "id": "ev-nvidia-a100-datasheet-bf16",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf",
          "verified_on": "2026-10-24",
          "summary": "A100 datasheet BFLOAT16 Tensor Core figure (dense | sparse), same for all variants.",
          "quote": "312 TFLOPS | 624 TFLOPS*"
        }
      ]
    },
    {
      "id": "a100-80gb",
      "name": "NVIDIA A100 80GB",
      "class": "datacenter",
      "memory_gb": 80,
      "arch": "Ampere",
      "ai_tops": "BFLOAT16 Tensor Core 312 TFLOPS | 624 TFLOPS* (*with sparsity)",
      "power_w": "300W (PCIe) / 400W (SXM)",
      "notes": "HBM2e, PCIe or SXM4. Available on Hugging Face Jobs as the \"a100-large\" flavor (1x, 4x, 8x).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-a100-80gb-page",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/a100/",
          "verified_on": "2026-10-24",
          "summary": "A100 product page spec table (80GB PCIe, 80GB SXM): memory, bandwidth, TDP.",
          "quote": "GPU Memory 80GB HBM2e 80GB HBM2e GPU Memory Bandwidth 1,935 GB/s 2,039 GB/s Max Thermal Design Power (TDP) 300W 400W ***"
        },
        {
          "id": "ev-nvidia-a100-80gb-bf16",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/a100/",
          "verified_on": "2026-10-24",
          "summary": "A100 product page: BFLOAT16 Tensor Core 312 TFLOPS dense, 624 with sparsity.",
          "quote": "BFLOAT16 Tensor Core 312 TFLOPS | 624 TFLOPS*"
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists an A100 flavor with 80 GB GPU memory.",
          "quote": "Nvidia A100 - large 12 vCPU 142 GB 80 GB 1000 GB $2.50"
        }
      ]
    },
    {
      "id": "a10g",
      "name": "NVIDIA A10G (AWS)",
      "class": "cloud",
      "memory_gb": 24,
      "arch": "Ampere",
      "notes": "Cloud-only AWS variant of the A10 (EC2 G5 instances); NVIDIA publishes no standalone A10G spec page, and its specs differ from the retail A10, so no TOPS figure is given here. Available on Hugging Face Jobs (a10g-small, a10g-large, 2x and 4x large).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-aws-g5-a10g",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://aws.amazon.com/ec2/instance-types/g5/",
          "verified_on": "2026-10-24",
          "summary": "AWS EC2 G5 page: A10G Tensor Core GPUs with 24 GB memory per GPU.",
          "quote": "Each instance features up to 8 A10G Tensor Core GPUs that come with 80 ray tracing cores and 24 GB of memory per GPU."
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists A10G small/large flavors (24 GB).",
          "quote": "Nvidia A10G - small 4 vCPU 15 GB 24 GB 110 GB $1.00 Nvidia A10G - large 12 vCPU 46 GB 24 GB 200 GB $1.50"
        }
      ]
    },
    {
      "id": "apple-m-16gb",
      "name": "Apple Silicon 16 GB",
      "class": "apple-silicon",
      "memory_gb": 16,
      "unified": true,
      "arch": "Apple M-series",
      "notes": "A memory class, not a single chip: any M-series Mac with 16 GB unified memory (e.g. M4 base configuration in the 2024 Mac mini). Chip generation (M1–M5) and variant (base/Pro/Max/Ultra, GPU core count) change inference speed substantially, so runs on this target must record the exact chip in platform.chip. PyTorch uses the MPS backend; unified memory is shared with macOS, so usable model memory is lower than 16 GB.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-apple-mac-mini-2024-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://support.apple.com/en-us/121555",
          "verified_on": "2026-10-24",
          "summary": "Apple tech specs for Mac mini (2024): M4 ships with 16GB (configurable to 24GB or 32GB); M4 Pro with 24GB (configurable to 48GB or 64GB) unified memory.",
          "quote": "Apple M4 chip 16GB unified memory Configurable to: 24GB or 32GB Apple M4 Pro chip 24GB unified memory Configurable to: 48GB or 64GB"
        },
        {
          "id": "ev-lerobot-mps",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/torch_accelerators.mdx",
          "date": "2026-08-03",
          "verified_on": "2026-10-24",
          "versions": {
            "lerobot": "0.6.1"
          },
          "summary": "LeRobot docs: Apple Silicon GPUs are used through the PyTorch MPS backend (--policy.device=mps).",
          "quote": "- **MPS**: acceleration with Apple Silicon GPUs"
        }
      ]
    },
    {
      "id": "apple-m-32gb",
      "name": "Apple Silicon 32 GB",
      "class": "apple-silicon",
      "memory_gb": 32,
      "unified": true,
      "arch": "Apple M-series",
      "notes": "A memory class, not a single chip: any M-series Mac with 32 GB unified memory (e.g. M4 configured to 32GB in the 2024 Mac mini). Chip generation (M1–M5) and variant (base/Pro/Max/Ultra, GPU core count) change inference speed substantially, so runs on this target must record the exact chip in platform.chip. PyTorch uses the MPS backend; unified memory is shared with macOS, so usable model memory is lower than 32 GB.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-apple-mac-mini-2024-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://support.apple.com/en-us/121555",
          "verified_on": "2026-10-24",
          "summary": "Apple tech specs for Mac mini (2024): M4 ships with 16GB (configurable to 24GB or 32GB); M4 Pro with 24GB (configurable to 48GB or 64GB) unified memory.",
          "quote": "Apple M4 chip 16GB unified memory Configurable to: 24GB or 32GB Apple M4 Pro chip 24GB unified memory Configurable to: 48GB or 64GB"
        },
        {
          "id": "ev-lerobot-mps",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/torch_accelerators.mdx",
          "date": "2026-08-03",
          "verified_on": "2026-10-24",
          "versions": {
            "lerobot": "0.6.1"
          },
          "summary": "LeRobot docs: Apple Silicon GPUs are used through the PyTorch MPS backend (--policy.device=mps).",
          "quote": "- **MPS**: acceleration with Apple Silicon GPUs"
        }
      ]
    },
    {
      "id": "apple-m-64gb",
      "name": "Apple Silicon 64 GB",
      "class": "apple-silicon",
      "memory_gb": 64,
      "unified": true,
      "arch": "Apple M-series",
      "notes": "A memory class, not a single chip: any M-series Mac with 64 GB unified memory (e.g. M4 Pro configured to 64GB in the 2024 Mac mini). Chip generation (M1–M5) and variant (base/Pro/Max/Ultra, GPU core count) change inference speed substantially, so runs on this target must record the exact chip in platform.chip. PyTorch uses the MPS backend; unified memory is shared with macOS, so usable model memory is lower than 64 GB.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-apple-mac-mini-2024-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://support.apple.com/en-us/121555",
          "verified_on": "2026-10-24",
          "summary": "Apple tech specs for Mac mini (2024): M4 ships with 16GB (configurable to 24GB or 32GB); M4 Pro with 24GB (configurable to 48GB or 64GB) unified memory.",
          "quote": "Apple M4 chip 16GB unified memory Configurable to: 24GB or 32GB Apple M4 Pro chip 24GB unified memory Configurable to: 48GB or 64GB"
        },
        {
          "id": "ev-lerobot-mps",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/torch_accelerators.mdx",
          "date": "2026-08-03",
          "verified_on": "2026-10-24",
          "versions": {
            "lerobot": "0.6.1"
          },
          "summary": "LeRobot docs: Apple Silicon GPUs are used through the PyTorch MPS backend (--policy.device=mps).",
          "quote": "- **MPS**: acceleration with Apple Silicon GPUs"
        }
      ]
    },
    {
      "id": "cpu-only",
      "name": "CPU only (no accelerator)",
      "class": "cpu-only",
      "memory_gb": 0,
      "notes": "memory_gb is 0 because there is no dedicated accelerator memory: models run from system RAM, which varies per machine. CPU model, core count and RAM change results by large factors, so runs on this target must record the CPU in platform.cpu (or platform.chip) and the RAM size.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-lerobot-cpu-backend",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://github.com/huggingface/lerobot/blob/v0.6.1/docs/source/torch_accelerators.mdx",
          "date": "2026-08-03",
          "verified_on": "2026-10-24",
          "versions": {
            "lerobot": "0.6.1"
          },
          "summary": "LeRobot docs list CPU as a backend where all computation runs on the CPU with no accelerator.",
          "quote": "- **CPU**: CPU executes all computations, no dedicated accelerator is used"
        }
      ]
    },
    {
      "id": "h100-80gb",
      "name": "NVIDIA H100 80GB",
      "class": "datacenter",
      "memory_gb": 80,
      "arch": "Hopper",
      "ai_tops": "FP8 Tensor Core* 3,958 teraFLOPS (H100 SXM; *with sparsity)",
      "power_w": "Up to 700W (configurable) (SXM)",
      "notes": "The SXM part is 80GB HBM3; the H100 NVL is a different 94GB part. Not listed as a Hugging Face Jobs flavor in the current pricing table.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-h100-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/h100/",
          "verified_on": "2026-10-24",
          "summary": "H100 product page spec table (columns: H100 SXM, H100 NVL): FP8 3,958 TFLOPS sparse; memory 80GB vs 94GB.",
          "quote": "FP8 Tensor Core * 3,958 teraFLOPS 3,341 teraFLOPS INT8 Tensor Core * 3,958 TOPS 3,341 TOPS GPU Memory 80GB 94GB"
        },
        {
          "id": "ev-nvidia-h100-tdp",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/h100/",
          "verified_on": "2026-10-24",
          "summary": "Same table: Max TDP up to 700W (SXM) and 350-400W (NVL).",
          "quote": "Max Thermal Design Power (TDP) Up to 700W (configurable) 350-400W (configurable)"
        }
      ]
    },
    {
      "id": "h200",
      "name": "NVIDIA H200",
      "class": "datacenter",
      "memory_gb": 141,
      "arch": "Hopper",
      "ai_tops": "FP8 Tensor Core² 3,958 TFLOPS (H200 SXM; ²with sparsity)",
      "power_w": "Up to 700W (configurable) (SXM)",
      "notes": "141 GB HBM3e in both the SXM and NVL form factors. Available on Hugging Face Jobs (1x, 2x, 4x, 8x).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-h200-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/h200/",
          "verified_on": "2026-10-24",
          "summary": "H200 product page spec table (columns: H200 SXM, H200 NVL): FP8 3,958 TFLOPS sparse; 141GB memory.",
          "quote": "FP8 Tensor Core² 3,958 TFLOPS 3,341 TFLOPS INT8 Tensor Core² 3,958 TFLOPS 3,341 TFLOPS GPU Memory 141GB 141GB"
        },
        {
          "id": "ev-nvidia-h200-tdp",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/h200/",
          "verified_on": "2026-10-24",
          "summary": "Same table: Max TDP up to 700W (SXM), up to 600W (NVL).",
          "quote": "Max Thermal Design Power (TDP) Up to 700W (configurable) Up to 600W (configurable)"
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists an H200 flavor (141 GB).",
          "quote": "Nvidia H200 23 vCPU 256 GB 141 GB 3000 GB $5.00"
        }
      ]
    },
    {
      "id": "jetson-agx-orin-64gb",
      "name": "NVIDIA Jetson AGX Orin 64GB",
      "aliases": [
        "Jetson AGX Orin Developer Kit"
      ],
      "class": "edge",
      "memory_gb": 64,
      "unified": true,
      "arch": "Ampere (2048 CUDA cores, 64 Tensor Cores)",
      "ai_tops": "275 TOPS",
      "power_w": "15W - 60W (nvpmodel modes 15W / 30W / 50W / MAXN)",
      "notes": "AGX Orin 64GB module / developer kit. Distinct from AGX Orin 32GB (fewer cores, 40W max mode) and AGX Orin Industrial. Of the Orin modules in NVIDIA's power-mode tables, only the AGX Orin 64GB has a 50W nvpmodel mode. Record power mode with every run. 64 GB is unified memory shared with the OS.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-jetson-orin-tops",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/",
          "verified_on": "2026-10-24",
          "summary": "NVIDIA Jetson Orin spec table AI Performance row. Column order on the page: AGX Orin Developer Kit / AGX Orin 64GB (275), AGX Orin Industrial (248), AGX Orin 32GB (241), Orin NX 16GB (157), Orin NX 8GB (117), Orin Nano Super Developer Kit (67), Orin Nano 8GB (67), Orin Nano 4GB (34).",
          "quote": "AI Performance 275 TOPS 248 TOPs 241 TOPS 157 TOPS 117 TOPS 67 TOPS 67 TOPS 34 TOPS"
        },
        {
          "id": "ev-nvidia-jetson-agx-orin-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/",
          "verified_on": "2026-10-24",
          "summary": "Jetson Orin spec table Memory row starts with the AGX Orin 64GB column: 64GB 256-bit LPDDR5, 204.8 GB/s.",
          "quote": "Memory 64GB 256-bit LPDDR5 204.8GB/s"
        },
        {
          "id": "ev-nvidia-jetson-orin-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/",
          "verified_on": "2026-10-24",
          "summary": "Jetson Orin spec table Power row; first column (AGX Orin Developer Kit / 64GB) is 15W - 60W.",
          "quote": "Power 15W - 60W 15W-75W 15W - 60W 10W - 15W - 25W - 40W"
        },
        {
          "id": "ev-jetson-power-modes-agx64",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://docs.nvidia.com/jetson/archives/r36.4.3/DeveloperGuide/SD/PlatformPowerAndPerformance/JetsonOrinNanoSeriesJetsonOrinNxSeriesAndJetsonAgxOrinSeries.html",
          "verified_on": "2026-10-24",
          "versions": {
            "jetson_linux": "36.4.3"
          },
          "summary": "Jetson Linux r36.4.3 power-mode table for AGX Orin 64GB: MAXN, 15W, 30W (default), 50W.",
          "quote": "for Jetson AGX Orin 64GB Property Mode MAXN 15W 30W* 50W Power budget n/a 15W 30W 50W"
        },
        {
          "id": "ev-jetson-power-modes-agx32",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://docs.nvidia.com/jetson/archives/r36.4.3/DeveloperGuide/SD/PlatformPowerAndPerformance/JetsonOrinNanoSeriesJetsonOrinNxSeriesAndJetsonAgxOrinSeries.html",
          "verified_on": "2026-10-24",
          "versions": {
            "jetson_linux": "36.4.3"
          },
          "summary": "Same guide: AGX Orin 32GB modes are MAXN, 15W, 30W, 40W (no 50W mode).",
          "quote": "NVP Model Clock Configuration for Jetson AGX Orin 32GB Property Mode MAXN 15W 30W* 40W Power budget n/a 15W 30W 40W"
        }
      ]
    },
    {
      "id": "jetson-agx-thor",
      "name": "NVIDIA Jetson AGX Thor 128GB",
      "aliases": [
        "Jetson AGX Thor Developer Kit",
        "Jetson T5000"
      ],
      "class": "edge",
      "memory_gb": 128,
      "unified": true,
      "arch": "Blackwell (2560-core GPU, fifth-gen Tensor Cores)",
      "ai_tops": "2070 TFLOPS (FP4—Sparse)",
      "power_w": "40 W–130 W",
      "notes": "Jetson AGX Thor Developer Kit / Jetson T5000 module (128 GB LPDDR5X, 273 GB/s). The smaller Jetson T4000 (64 GB, 1200 FP4-sparse TFLOPS, 40–70 W) is a different target. Headline figure is FP4 with sparsity; BF16/FP16 throughput is far lower. Record power mode (e.g. MAXN) and jetson_clocks state with every run.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-thor-tops",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/",
          "verified_on": "2026-10-24",
          "summary": "Jetson Thor series spec table: AGX Thor Developer Kit / T5000 = 2070 TFLOPS (FP4, sparse); T4000 = 1200.",
          "quote": "Jetson AGX Thor Developer Kit Jetson T5000 Jetson T4000 AI Performance 2070 TFLOPS (FP4—Sparse) 1200 TFLOPS (FP4—Sparse)"
        },
        {
          "id": "ev-nvidia-thor-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/",
          "verified_on": "2026-10-24",
          "summary": "Spec table Memory row: 128 GB (Dev Kit / T5000) and 64 GB (T4000) LPDDR5X at 273 GB/s.",
          "quote": "Memory 128 GB 256-bit LPDDR5X 273 GB/s 64 GB 256-bit LPDDR5X 273 GB/s"
        },
        {
          "id": "ev-nvidia-thor-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/",
          "verified_on": "2026-10-24",
          "summary": "Spec table Power row: 40 W–130 W (Dev Kit, T5000), 40 W–70 W (T4000).",
          "quote": "Power 40 W–130 W 40 W–130 W 40 W–70 W"
        }
      ]
    },
    {
      "id": "jetson-orin-nano-8gb",
      "name": "NVIDIA Jetson Orin Nano 8GB (Super)",
      "aliases": [
        "Jetson Orin Nano Super",
        "Orin Nano Super Developer Kit",
        "Jetson Orin Nano 8GB"
      ],
      "class": "edge",
      "memory_gb": 8,
      "unified": true,
      "arch": "Ampere (1024 CUDA cores, 32 Tensor Cores)",
      "ai_tops": "67 INT8 TOPS (Super Developer Kit; up from 40 TOPS before the JetPack 6.2 Super update)",
      "power_w": "7W–25W (MAXN_SUPER mode has no fixed power budget)",
      "notes": "One entry for the Orin Nano 8GB module and its developer kit. The 'Super' developer kit is the same hardware: JetPack 6.2+ adds the 25W and MAXN_SUPER power modes (MAXN_SUPER only when flashed with the jetson-orin-nano-devkit-super configuration), raising the headline from 40 to 67 TOPS and memory bandwidth from 68 to 102 GB/s. Power mode and clock pinning (jetson_clocks) change inference latency materially, so runs on this target must record platform.power_mode and the JetPack version. 8 GB is unified memory shared with the OS and CPU.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-orin-nano-super-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/",
          "verified_on": "2026-10-24",
          "summary": "NVIDIA Jetson Orin Nano Super Developer Kit spec table: 67 INT8 TOPS, Ampere GPU with 1024 CUDA cores and 32 Tensor Cores.",
          "quote": "Specifications Jetson Orin Nano Super Developer Kit AI Performance 67 INT8 TOPS GPU NVIDIA Ampere architecture with 1024 CUDA cores and 32 tensor cores"
        },
        {
          "id": "ev-nvidia-orin-nano-super-mem-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/",
          "verified_on": "2026-10-24",
          "summary": "Same spec table: 8 GB 128-bit LPDDR5 at 102 GB/s; 7W–25W.",
          "quote": "Memory 8GB 128-bit LPDDR5 102 GB/s Storage Supports SD card slot and external NVMe Power 7W–25W"
        },
        {
          "id": "ev-nvidia-orin-nano-super-update",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/",
          "verified_on": "2026-10-24",
          "summary": "The Super uplift is a software update to the existing Orin Nano developer kit (40 to 67 TOPS, 68 to 102 GB/s).",
          "quote": "NVIDIA's latest software update enhances Jetson Orin Nano's AI performance from 40 to 67 AI TOPs and memory bandwidth from 68 to 102 GB/s"
        },
        {
          "id": "ev-jetpack-6-2-super",
          "tier": "upstream-documented",
          "type": "release-notes",
          "url": "https://developer.nvidia.com/embedded/jetpack-sdk-62",
          "verified_on": "2026-10-24",
          "summary": "NVIDIA JetPack 6.2 page: JetPack 6.2 (Jetson Linux 36.4.3) introduces the high-power Super modes for Orin Nano and Orin NX production modules.",
          "quote": "JetPack 6.2 supports new high-power Super Mode for NVIDIA Jetson Orin Nano and Jetson Orin NX production modules."
        },
        {
          "id": "ev-jetson-power-modes-nano",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://docs.nvidia.com/jetson/archives/r36.4.3/DeveloperGuide/SD/PlatformPowerAndPerformance/JetsonOrinNanoSeriesJetsonOrinNxSeriesAndJetsonAgxOrinSeries.html",
          "verified_on": "2026-10-24",
          "versions": {
            "jetson_linux": "36.4.3"
          },
          "summary": "Jetson Linux r36.4.3 power-mode table for Orin Nano 8GB / 8GB Super: 15W, 7W (non-Super); 15W, 25W, MAXN_SUPER (Super).",
          "quote": "Jetson Orin Nano 8GB Jetson Orin Nano 8GB Super Property Mode Mode 15W* 7W 15W 25W* MAXN_SUPER** Power budget 15W 7W 15W 25W n/a"
        },
        {
          "id": "ev-jetson-maxn-super-flash",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://docs.nvidia.com/jetson/archives/r36.4.3/DeveloperGuide/SD/PlatformPowerAndPerformance/JetsonOrinNanoSeriesJetsonOrinNxSeriesAndJetsonAgxOrinSeries.html",
          "verified_on": "2026-10-24",
          "versions": {
            "jetson_linux": "36.4.3"
          },
          "summary": "MAXN_SUPER requires flashing with the devkit-super configuration.",
          "quote": "MAXN_SUPER mode is available only when flashing with the jetson-orin-nano-devkit-super configurations."
        }
      ]
    },
    {
      "id": "jetson-orin-nx-16gb",
      "name": "NVIDIA Jetson Orin NX 16GB",
      "class": "edge",
      "memory_gb": 16,
      "unified": true,
      "arch": "Ampere (1024 CUDA cores, 32 Tensor Cores)",
      "ai_tops": "157 TOPS (with JetPack 6.2 Super mode)",
      "power_w": "10W - 15W - 25W - 40W",
      "notes": "Module (needs a carrier board). 157 TOPS and the 40W mode require the JetPack 6.2+ Super modes (MAXN_SUPER, flashed with the jetson-orin-nano-devkit-super configuration); older JetPack releases cap at 25W/MAXN. Record power mode and JetPack version with every run. 16 GB is unified memory shared with the OS.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-jetson-orin-tops",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/",
          "verified_on": "2026-10-24",
          "summary": "NVIDIA Jetson Orin spec table AI Performance row. Column order on the page: AGX Orin Developer Kit / AGX Orin 64GB (275), AGX Orin Industrial (248), AGX Orin 32GB (241), Orin NX 16GB (157), Orin NX 8GB (117), Orin Nano Super Developer Kit (67), Orin Nano 8GB (67), Orin Nano 4GB (34).",
          "quote": "AI Performance 275 TOPS 248 TOPs 241 TOPS 157 TOPS 117 TOPS 67 TOPS 67 TOPS 34 TOPS"
        },
        {
          "id": "ev-nvidia-jetson-orin-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/",
          "verified_on": "2026-10-24",
          "summary": "Jetson Orin spec table Memory row (AGX Orin 64GB / Industrial / 32GB, then Orin NX 16GB at 16GB 128-bit LPDDR5).",
          "quote": "64GB 256-bit LPDDR5 (+ ECC) 204.8GB/s 32GB 256-bit LPDDR5 204.8GB/s 16GB 128-bit LPDDR5 102.4GB/s 8GB 128-bit LPDDR5 102.4GB/s"
        },
        {
          "id": "ev-jetson-power-modes-nx16",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://docs.nvidia.com/jetson/archives/r36.4.3/DeveloperGuide/SD/PlatformPowerAndPerformance/JetsonOrinNanoSeriesJetsonOrinNxSeriesAndJetsonAgxOrinSeries.html",
          "verified_on": "2026-10-24",
          "versions": {
            "jetson_linux": "36.4.3"
          },
          "summary": "Jetson Linux r36.4.3 power-mode table for Orin NX 16GB and 16GB Super (10W, 15W, 25W, 40W, MAXN/MAXN_SUPER).",
          "quote": "Jetson Orin NX 16GB Jetson Orin NX 16GB Super Property Mode Mode MAXN 10W 15W* 25W MAXN_SUPER 10W 15W 25W 40W*"
        },
        {
          "id": "ev-jetpack-6-2-super",
          "tier": "upstream-documented",
          "type": "release-notes",
          "url": "https://developer.nvidia.com/embedded/jetpack-sdk-62",
          "verified_on": "2026-10-24",
          "summary": "NVIDIA JetPack 6.2 page: JetPack 6.2 (Jetson Linux 36.4.3) introduces the high-power Super modes for Orin Nano and Orin NX production modules.",
          "quote": "JetPack 6.2 supports new high-power Super Mode for NVIDIA Jetson Orin Nano and Jetson Orin NX production modules."
        }
      ]
    },
    {
      "id": "l4",
      "name": "NVIDIA L4",
      "class": "datacenter",
      "memory_gb": 24,
      "arch": "Ada Lovelace",
      "ai_tops": "FP8 Tensor Core 485 teraFLOPs* (*shown with sparsity)",
      "power_w": "72W",
      "notes": "Low-profile 72 W inference card. Available on Hugging Face Jobs (1x and 4x L4).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-l4-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l4/",
          "verified_on": "2026-10-24",
          "summary": "L4 spec table: FP8 485 TFLOPS with sparsity, 24GB, 72W TDP.",
          "quote": "FP8 Tensor Core 485 teraFLOPs* INT8 Tensor Core 485 TOPs* GPU memory 24GB GPU memory bandwidth 300GB/s NVENC | NVDEC | JPEG decoders 2 | 4 | 4 Max thermal design power (TDP) 72W"
        },
        {
          "id": "ev-nvidia-l4-arch",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l4/",
          "verified_on": "2026-10-24",
          "summary": "L4 page: powered by the Ada Lovelace architecture.",
          "quote": "The NVIDIA L4 Tensor Core GPU powered by the NVIDIA Ada Lovelace architecture"
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists L4 flavors (24 GB).",
          "quote": "1x Nvidia L4 8 vCPU 30 GB 24 GB 400 GB $0.80"
        }
      ]
    },
    {
      "id": "l40",
      "name": "NVIDIA L40",
      "class": "datacenter",
      "memory_gb": 48,
      "arch": "Ada Lovelace",
      "power_w": "300W",
      "notes": "Not the L40S (different SKU, lower power). NVIDIA's L40 page does not show a Tensor TFLOPS headline in its spec table. Not listed as a Hugging Face Jobs flavor.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-l40-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l40/",
          "verified_on": "2026-10-24",
          "summary": "L40 spec table: Ada Lovelace, 48 GB GDDR6 with ECC, 300W max power.",
          "quote": "GPU Architecture NVIDIA Ada Lovelace Architecture GPU Memory Bandwidth 864GB/s GPU Memory 48 GB GDDR6 with ECC Display Connectors 4 x DP 1.4a Max Power Consumption 300W"
        }
      ]
    },
    {
      "id": "l40s",
      "name": "NVIDIA L40S",
      "class": "datacenter",
      "memory_gb": 48,
      "arch": "Ada Lovelace",
      "ai_tops": "FP8 Tensor Core 733 I 1,466* (*with sparsity)",
      "power_w": "350W",
      "notes": "Not the same part as the L40 (see compute \"l40\"). Available on Hugging Face Jobs (1x, 4x, 8x).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-l40s-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l40s/",
          "verified_on": "2026-10-24",
          "summary": "L40S spec table: Ada Lovelace, 48GB GDDR6 with ECC.",
          "quote": "GPU Architecture NVIDIA Ada Lovelace architecture GPU Memory 48GB GDDR6 with ECC"
        },
        {
          "id": "ev-nvidia-l40s-fp8",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l40s/",
          "verified_on": "2026-10-24",
          "summary": "L40S spec table: FP8 Tensor Core 733 dense / 1,466 with sparsity TFLOPS.",
          "quote": "FP8 Tensor Core 733 I 1,466*"
        },
        {
          "id": "ev-nvidia-l40s-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/l40s/",
          "verified_on": "2026-10-24",
          "summary": "L40S spec table: max power consumption 350W.",
          "quote": "Max Power Consumption 350W"
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists L40S flavors (48 GB).",
          "quote": "1x Nvidia L40S 8 vCPU 62 GB 48 GB 380 GB $1.80"
        }
      ]
    },
    {
      "id": "rtx-3060-12gb",
      "name": "NVIDIA GeForce RTX 3060 12GB",
      "class": "consumer-gpu",
      "memory_gb": 12,
      "arch": "Ampere",
      "power_w": "170 W (graphics card power)",
      "notes": "The RTX 3060 also exists in an 8 GB variant; this entry is the 12 GB GDDR6 card only. NVIDIA's page does not list an AI TOPS figure for the RTX 3060.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-rtx3060-memory",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/",
          "verified_on": "2026-10-24",
          "summary": "RTX 3060 family spec table (columns: RTX 3060 Ti, RTX 3060): RTX 3060 is 12 GB GDDR6 or 8 GB GDDR6.",
          "quote": "Memory Specs: Standard Memory Config 8 GB GDDR6 / 8 GB GDDR6X 12 GB GDDR6 / 8 GB GDDR6"
        },
        {
          "id": "ev-nvidia-rtx3060-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/",
          "verified_on": "2026-10-24",
          "summary": "Same table: graphics card power 200 W (3060 Ti) and 170 W (3060).",
          "quote": "Graphics Card Power (W) 200 170"
        },
        {
          "id": "ev-nvidia-rtx3060-arch",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/",
          "verified_on": "2026-10-24",
          "summary": "Same table: NVIDIA architecture Ampere for both columns.",
          "quote": "NVIDIA Architecture Ampere Ampere"
        }
      ]
    },
    {
      "id": "rtx-4090",
      "name": "NVIDIA GeForce RTX 4090",
      "class": "consumer-gpu",
      "memory_gb": 24,
      "arch": "Ada Lovelace",
      "ai_tops": "1321 AI TOPS",
      "power_w": "450 W (total graphics power)",
      "notes": "NVIDIA's AI TOPS headline is a low-precision, sparsity-assisted figure, not FP16/BF16 throughput. The China-market RTX 4090 D is a different, cut-down part and should not be logged against this target.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-rtx4090-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 4090 full specs: 1321 AI TOPS (4th-gen Tensor Cores), 24 GB GDDR6X.",
          "quote": "Tensor Cores (AI) 4th Generation 1321 AI TOPS Boost Clock (GHz) 2.52 Base Clock (GHz) 2.23 Memory Specs: Standard Memory Config 24 GB GDDR6X"
        },
        {
          "id": "ev-nvidia-rtx4090-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 4090 full specs: total graphics power 450 W.",
          "quote": "Total Graphics Power (W) 450"
        },
        {
          "id": "ev-nvidia-rtx4090-arch",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 4090 full specs: NVIDIA architecture Ada Lovelace.",
          "quote": "NVIDIA Architecture Ada Lovelace"
        }
      ]
    },
    {
      "id": "rtx-5090",
      "name": "NVIDIA GeForce RTX 5090",
      "class": "consumer-gpu",
      "memory_gb": 32,
      "arch": "Blackwell",
      "ai_tops": "3352 AI TOPS",
      "power_w": "575 W (total graphics power)",
      "notes": "NVIDIA's AI TOPS headline is an FP4, sparsity-assisted figure, not FP16/BF16 throughput.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-rtx5090-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 5090 full specs: 3352 AI TOPS (5th-gen Tensor Cores), 32 GB GDDR7.",
          "quote": "Tensor Cores (AI) 5th Generation 3352 AI TOPS Ray Tracing Cores 4th Generation 318 TFLOPS Boost Clock (GHz) 2.41 Base Clock (GHz) 2.01 Memory Specs: Standard Memory Config 32 GB GDDR7"
        },
        {
          "id": "ev-nvidia-rtx5090-power",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 5090 full specs: total graphics power 575 W.",
          "quote": "Total Graphics Power (W) 575"
        },
        {
          "id": "ev-nvidia-rtx5090-arch",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/",
          "verified_on": "2026-10-24",
          "summary": "RTX 5090 full specs: NVIDIA architecture Blackwell.",
          "quote": "NVIDIA Architecture Blackwell"
        }
      ]
    },
    {
      "id": "rtx-pro-6000-blackwell-server",
      "name": "NVIDIA RTX PRO 6000 Blackwell Server",
      "class": "datacenter",
      "memory_gb": 96,
      "arch": "Blackwell",
      "ai_tops": "FP4 Tensor Core 4 PFLOPS",
      "power_w": "Up to 600W (configurable)",
      "notes": "Server (passively cooled) edition with 96 GB GDDR7. The RTX PRO 6000 Blackwell Workstation and Max-Q editions are different SKUs. Hugging Face Jobs lists an \"Nvidia RTX PRO 6000\" flavor (96 GB); its edition is not stated there.",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-rtxpro6000s-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/rtx-pro-6000-blackwell-server-edition/",
          "verified_on": "2026-10-24",
          "summary": "RTX PRO 6000 Blackwell Server Edition spec table: Blackwell, FP4 4 PFLOPS, FP16/BF16 1 PFLOP.",
          "quote": "GPU Architecture NVIDIA Blackwell Architecture CUDA Parallel Processing Cores 24,064 NVIDIA RT Cores 188 (4th Gen) FP4 Tensor Core 4 PFLOPS FP8 Tensor Core 2 PFLOPS FP16 | BF16 Tensor Core 1 PFLOP"
        },
        {
          "id": "ev-nvidia-rtxpro6000s-quick",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/rtx-pro-6000-blackwell-server-edition/",
          "verified_on": "2026-10-24",
          "summary": "Quick specs: 96GB GDDR7 with ECC, max power up to 600W.",
          "quote": "GPU Memory 96GB GDDR7 with ECC Display Ports 4x DisplayPort 2.1 Max Power Consumption Up to 600W (configurable)"
        }
      ]
    },
    {
      "id": "t4",
      "name": "NVIDIA T4",
      "class": "datacenter",
      "memory_gb": 16,
      "arch": "Turing",
      "ai_tops": "Mixed Precision (FP16/FP32) 65 FP16 TFLOPS; INT8 Precision 130 INT8 TOPS",
      "power_w": "70 watts",
      "notes": "Turing has no BF16 support, so BF16 checkpoints must run in FP16 or FP32. Available on Hugging Face Jobs (t4-small, t4-medium).",
      "verified_on": "2026-10-24",
      "sources": [
        {
          "id": "ev-nvidia-t4-specs",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/tesla-t4/",
          "verified_on": "2026-10-24",
          "summary": "T4 spec table: 65 FP16 TFLOPS, 130 INT8 TOPS, 16 GB GDDR6, 70 W.",
          "quote": "Mixed Precision (FP16/FP32) 65 FP16 TFLOPS INT8 Precision 130 INT8 TOPS INT4 Precision 260 INT4 TOPS Interconnect Gen3 x16 PCIe Memory Capacity 16 GB GDDR6 Bandwidth 320+ GB/s Power 70 watts"
        },
        {
          "id": "ev-nvidia-t4-arch",
          "tier": "upstream-documented",
          "type": "vendor-docs",
          "url": "https://www.nvidia.com/en-us/data-center/tesla-t4/",
          "verified_on": "2026-10-24",
          "summary": "T4 page: based on the Turing architecture.",
          "quote": "Based on the new NVIDIA Turing ™ architecture"
        },
        {
          "id": "ev-hf-jobs-pricing",
          "tier": "upstream-documented",
          "type": "official-docs",
          "url": "https://huggingface.co/docs/hub/jobs-pricing",
          "verified_on": "2026-10-24",
          "summary": "Hugging Face Jobs pricing lists T4 small and medium flavors (16 GB).",
          "quote": "Nvidia T4 - small 4 vCPU 15 GB 16 GB 50 GB $0.40 Nvidia T4 - medium 8 vCPU 30 GB 16 GB 100 GB $0.60"
        }
      ]
    }
  ]
}