{
  "schemaVersion": 1,
  "lessonId": "tpu-03",
  "contentHash": "e6dd2b245a8dfadb2626f5ecca9e89c0a274b8b8c32b51ec9ce69dffaefa00f9",
  "executedAt": "2026-10-20T10:58:55.101094+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "TPU ridge points (FLOP/byte): {'v5e': 240.5, 'v5p': 166.0, 'v6e': 573.8}\n7B memory budget (GiB): {'fp32_train_state_gib': 104.308, 'mixed_bf16_train_state_gib': 117.347, 'kv_fp32_gib': 2.0, 'kv_bf16_gib': 1.0, 'kv_int8_gib': 0.5}\n1.5B memory budget (GiB): {'fp32_train_state_gib': 22.352, 'mixed_bf16_train_state_gib': 25.146, 'kv_fp32_gib': 1.0, 'kv_bf16_gib': 0.5, 'kv_int8_gib': 0.25}\nTPU ridge points (FLOP/byte): {'v5e': 240.5, 'v5p': 166.0, 'v6e': 573.8}\n7B memory budget (GiB): {'fp32_train_state_gib': 104.308, 'mixed_bf16_train_state_gib': 117.347, 'kv_fp32_gib': 2.0, 'kv_bf16_gib': 1.0, 'kv_int8_gib': 0.5}\n1.5B memory budget (GiB): {'fp32_train_state_gib': 22.352, 'mixed_bf16_train_state_gib': 25.146, 'kv_fp32_gib': 1.0, 'kv_bf16_gib': 0.5, 'kv_int8_gib': 0.25}\nKV cache GiB (FP32 / BF16 / INT8): 2.0 1.0 0.5\n7B mixed train state GiB: 117.347 Minimum chips at 80% usable HBM: 10\n1.5B state plus BF16 KV cache (GiB): 25.646\nArithmetic intensity: 341.3333333333333 FLOPs/byte\nv6e-4 80% usable GiB: 102.4 Fits 7B state estimate: False\nPASS: tpu-03\n",
  "notebookCells": [
    {
      "sourceHash": "580b8698af0122b826f7121fcc04afee143aeea162e91e922d34201735efc088",
      "outputs": []
    },
    {
      "sourceHash": "eb4ccdcf7aef6cc6439f2b9e8de72adf00164110cfc685a0a48d37c72b066d61",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TPU ridge points (FLOP/byte): {'v5e': 240.5, 'v5p': 166.0, 'v6e': 573.8}\n",
            "7B memory budget (GiB): {'fp32_train_state_gib': 104.308, 'mixed_bf16_train_state_gib': 117.347, 'kv_fp32_gib': 2.0, 'kv_bf16_gib': 1.0, 'kv_int8_gib': 0.5}\n",
            "1.5B memory budget (GiB): {'fp32_train_state_gib': 22.352, 'mixed_bf16_train_state_gib': 25.146, 'kv_fp32_gib': 1.0, 'kv_bf16_gib': 0.5, 'kv_int8_gib': 0.25}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "ecbda9172940b94cb442b17e120b2bc89510c5a20135dd553d74f2195d256da1",
      "outputs": []
    },
    {
      "sourceHash": "ee7e8aead0eedf3f340f5ad5496da7e11480b9ceba80a4f992a089ac81854a68",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TPU ridge points (FLOP/byte): {'v5e': 240.5, 'v5p': 166.0, 'v6e': 573.8}\n",
            "7B memory budget (GiB): {'fp32_train_state_gib': 104.308, 'mixed_bf16_train_state_gib': 117.347, 'kv_fp32_gib': 2.0, 'kv_bf16_gib': 1.0, 'kv_int8_gib': 0.5}\n",
            "1.5B memory budget (GiB): {'fp32_train_state_gib': 22.352, 'mixed_bf16_train_state_gib': 25.146, 'kv_fp32_gib': 1.0, 'kv_bf16_gib': 0.5, 'kv_int8_gib': 0.25}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "39d7ab104c899ada9c83e091895c46db5614f7515281a57496368e7f43acbd76",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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WWTvq35oKwowc9q18N26kyXucPfO3/HH4L51RpO6hXicV5Jr/8xKZPnmuDnapbDNTgS4AQOZBDRgAAKxMTQUyKFOulE1e322bd+oP6cpX330qnbq2MX74zJ0nl65x4te0vv56yoTZuhaNtajMm4XzluljlWHz1uABOvii+GT1lrfefVWfVxkCzyqVFaKyRBS1+lR6mDVtgYSGhknWrD4ybc6PiTI41Jimzfkh2YyohfOW6+CLCrpMnf2D1Hm+hg6+KB6eHvrD/E/TvtUBiyW/rJL79x+avE9EeKRM/Xms+DVtYMySKVq8sIybMlpPoVMZMru2Pwq2JQweqQCMCtxMmvm9rl+kggqKyuKoUKmczuhQBY5vXr+lp+6You6t+r734Zs6+KKozKkCCaZaqZ/lDl1a6+lXhqlUapzq9VLZNaVKF9dBRzVNydpu37orC+Y++ll/uVcXPU4VfDG8xr0HdJf/G/oow0xlnKmpRaaofwv9X+8pg4e8bnyd1BSmN97pL43/DdJuXL/V6uMHADxbCMAAAGBlgQ8ffbBXsmZ7FJiwNsOHNfUB1/AB7nFvDX5V7x8+DJSA3Qes9thbNm3XxWvVh+/+A3uabKPql6gP788SVbNGZb2oGhxffva9PqeeQ7dXrJsdZAk1fWfzRn993KVbO2PwISH1Qf/FlzuYvceqZev0Xq2upZ6HKRUqldWFntV0l4P7jphs06T5CyZXgVKBAkNw6tyZ84muqWk/ampPnXo1pHzFMibvq4IkKitG2bNrv8k2KsDU77Ue8qRUsKfeC49q1Bw59JdYm/5Zj44RFxcXXUTbFPXzkzPXo+XTf1u3xWQb9f3pO+Blk9caNamn9+fOXLDauAEAzyamIAEAkAGdPH5G72vXq2G2jfpQnSt3Trlz+66uzaIyHKzhxPFHRWvLli+lM15MURkx5SqUkaNHjkl6MlV81cDT00O+/PYTKVS4wFMflwoEqelaippaY06NWlVlxuR5Sc6rjA/DFCo1TWn0Fz+avYeqg6IY6qg8ruJz5cz2VT8/Ce9hoKbTKGrqTNP65gNYkf9mXqmMGVPUa6+KIadk9859sm71Jv1zf/fufQkPC0/S5naCaUvWcvLEGePPejbfrCbbqIydmrWr6eCLuYLOxUsWMVsfxvAaBz32GgMAMh8CMAAAWFnWbP99UFOr2uTOY/3VTe7fezSdJO9jq788Lm++3DoAc//+A6s99oN/Hzt3nkcfHM3JnTeXpLeExVfV7BX1taqJo1al6dC5dYrPwZaFmh//AG5KbjPX1PfUwDCVKiVqqpEpaiqMOYaisDHRsY89/j29V4EQU8EQSx87e45H03GSy1oa/tHXenWmhGNSgT+VlWKYxqTHEZ7yOJ70Z10VbE5OnnyP/h2a+3fm6Wn+NVYrTikqqwwAkLkRgAEAwMrUlA+DM6fOpduHfMgTFV9VU6ciI6MkMjLlujmqEK6x37/1V1LNxBLRKYmN/W+58mVrZkvJ0sXlaYr79/FVQVlVF+VJOTomv+rP6hUbdPBFTVVSRXrVkuiFihRItFrQpPGzZOaUeRIf/8TDAADgqaAGDAAAVlarTjVj8dStm61fGDRh5kDC1WJMuXnj9qP22RPXGFFLViuR/y5/bEpIiOkiur7/PrYqUJocW0wJeRoMU01Sem0NU4EU9f32yWr5akoJa77c+fceJu+fINMloZw5H9UcUa5fMz21yJbUEs9P47E3/VvrqGOX1vL2ewN1ceDHl2pWS4nbiuFnPaWfhVtm/p0BAJAQARgAAKxMfbhu1rKRPt64botcvPCPxX3VlAtLlK9YVu/3Bxwy2+bC+UvGqSqqGGtChiWiDR8cTY3DUGfmcRX+fezTJ89JcNCjOiaPCw4O0UsMZ0SGorJHjxzXxXKT88e/NW5Kly1hXEHIEmqVH0P9nEMHjpptd+jAHybPqxWB1GpXyk7/vfK0qWWvlYP7jyTKArI2QwCxbIXSZn9OzRUXVhwdH2UXpfR9NCfhz3rgQ9M1WtSy26oWjql/ZwAAJEQABgAAG1BLMavaGmoqy5D/DTNbhNQgKipKxn4zSf44bFnR2pZtmui9CpLs2LbHZJtJ42bpfbZsWaVu/VqJrpUuW1Lvt23ZZfLD6dpVG83+1b9J84Y6g0aNefaMRSbbzJ6+UD/3jKh5q8bGorUb1v5Xe+Rxhw/+KUf//X4Z+lhKLafcrMWjIN2KJWtNLhGtarssX7zG7D06v9RW79es2ijH/jyZ7OOZCx48KbUstPoZUIWEJ/44I9m2KkhiLlCXEkOg8Ozpv01eX7pwtbEYsSle3o9qrwQ94ePrn3UXZ72K1Myp882OwZCF0+rfVZ8AADCFAAwAADagVnf5euxwXU/k4oXL0r3TAJkxZV6ibBgV+Lh04bLMm7VY2jfvoZdHjrcwA6ZxswbGJYI/ef9L+XXFBh0QMUwN+vzj0bLt3+lPbwzuL1keq09iCOBcPP+PfDn8e2Mh15CQUD2Or0b8YHaFI1XTptsrnfXxnJkLZcqEn3XGi6I+aKuv1XnDh+eMRmUvGV7bkZ9+J7NnLDz3ENsAAATWSURBVEw0zUUFHVRg5N03P9HfQ1WT5KWXO6b6cfoPfEUXCFbLhA/q916iIMrxv07JoP7v6eyK5OrblCpTQgcHVH/1fUtY3DcsNEwO7DsiX40YK22bmV4C+UkVLJRfBr7VVx8vnLdcPvy/EXL65FljME8FXc6fuyhzZy2SLm36yM7tAU/0OM83eBQ4XLVsvSxdtNr4M65+Xqf+NFu+//onHWA0x1AbZ/eOfcZsmtRQP+s9+7yoj9XrO37MVOO/FfX6zp+9RH74dpL+unkrv2RXlAIAgCK8AADYSIOGdWTWgp/ks4++1oGOyeNn6U1lDqhVUUJDQxOtfFKjVhUpXLSQRfdWdTDGTPhS3nrtAzl35ryM+PRbGTVirP5AHxwUYvwg3GdAd5PBgdp1q0ubDs1l/a+/6w+3alMBk9CQMP3huVuPTnp1mbWrN5p8/HeGDNTBpL27Dsj0yXN1doDKNggJDtX9VSaAWp7XXP9nmXptf5g4Soa8/Zn8+cdxmTB2mt5URpODo4N+jobXt1iJIvLjpK/NLjGcHDWN6Osxn8kHgz/X38Pe3d7QqzQpalUf9f347IsPZOi7n+tzrm6uifqrtpNnfq/7q+W+VQaV2ry8HwW+QkP+G6eh5o81vfZGb/29nj5prvz+m7/eVMDR3d1d1w9K+LOtMn6ehCryu/X3nXLh70syeuSP8u2X4/XPmeFnvGHjejoIpYrwmqKK9s7/eYlcvXJdWjd+SXyzZxM3t0fByG9+GG4MtCXnzcGv6mwwtcz0nJmLZO6sxcZ/K4YAmar7NPzLoU/0HAEA9oMADAAANqRqQixfO0e2b92tpwr9dfSk3Lt3XwdfVBCmQKF8UqVaJZ2RUqly+VTdO1fuHLJg2VQ9hUV9+D3/90UdNFEFUlWNDpWlUr1mZbP9vxj9sZSvUEbWrPpN/rl4ReLj4qVy1Qq6X4vWjWX4R6PN9nV1dZUJU7+RFUvWyOoVv+lgjFoZRz3fjl3bSKeubeTzj7+RjEq9hrMWTNBZRBvXb5OTJ87Iw/sPJS4uXnLmyiFlypUUv6YNpG2H5vq1eFIqgLBo1UyZNXW+zlZR2TU5cvpKyzaN5dXXeyWaOmMqo0iNRY1TBSk2rt8iJ46dlgf3A41LJxcvUVRq1K5qrElkba+/1VdnfqhpOKoOys3rt3Q2lApWqcwglRGinqMKUDwJb28vmbNoksyYPE/8t+ySmzdvi4M4SJVqFaVtx5b652zaxDnJZqLNnD9efp7+i/639/DBQ2NgKMrCKXKqto8KlDVv3VhWLV0rx4+dlqDAIP0c1c9Bm/bNpW3HFsbC2wAAmOMQ/6RVyQAAAGBTKsA16vOxOpCw5veFvNoAAGRghOoBAACeQWp1oV/mLtPHzz9WRBkAAGQ8TEECAABIJ4sXrNSFj5s2bygFCuXX011iYmL06krjx07TBZxVAeUefbryPQIAIIMjAAMAAJBOrl+7qVfSmTRupq4homqehIaFSUx0jL6u6st88c0nUrhIQb5HAABkcARgAAAA0olaiUqtEHTk0J96pR21xLGri4sOuNSsXVV69O5K8AUAgEyCIrwAAAAAAAA2RhFeAAAAAAAAGyMAAwAAAAAAYGMEYAAAAAAAAGyMAAwAAAAAAAABGAAAAAAAAMnQ/h+bkGZGx0SplgAAAABJRU5ErkJggg==",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "01451127be3135864d8fe01f4380f8e4e040c9e42d253c003ae70b0a29b40f0d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KV cache GiB (FP32 / BF16 / INT8): 2.0 1.0 0.5\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "f2683fb54674761ed7d375ee654ece21917729cfcb189dd99a50af504ae7b117",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7B mixed train state GiB: 117.347 Minimum chips at 80% usable HBM: 10\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "dbd13e32d4806d1771b1034fcc9e23f1029ee9c1c8654c4c32b5cd7b8b11b2c6",
      "outputs": []
    },
    {
      "sourceHash": "cb90997d6845dcaffcfa1729f8a315c89fe62ac3ddbf8a8b0c4898b1fe3c1dcf",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5B state plus BF16 KV cache (GiB): 25.646\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "39ebe32c82a2f2c4a461d7b907a606b05a445287204297aa8c9baebd18725531",
      "outputs": []
    },
    {
      "sourceHash": "c6a41719b8746bd30d8d7e99ffae63e6e85faf53c4765e8e4ad59201745eae95",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Arithmetic intensity: 341.3333333333333 FLOPs/byte\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "bf39ffba938c0a18a789686efa56f1fb4a7784d557f8c7b8cf278b55aec56700",
      "outputs": []
    },
    {
      "sourceHash": "165a272a2c297161a5afc917352e9c528c80f1b177fac9a42d92b9b1378db918",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "v6e-4 80% usable GiB: 102.4 Fits 7B state estimate: False\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}