How NVIDIA Built Their 2026 H100 GPU Driver for LLM Training with PyTorch 2.5 and TensorFlow 2.18

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
import sys
import logging
from typing import Optional

# Configure logging for error tracking
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def check_h100_driver_compatibility() -> None:
    """Verify NVIDIA driver meets 2026 H100 requirements (>= 550.54.15) and CUDA 12.8+"""
    if not torch.cuda.is_available():
        raise RuntimeError("CUDA not available. H100 2026 driver requires CUDA 12.8+")
    try:
        driver_version = torch.cuda.get_driver_version()
        cuda_version = torch.version.cuda
        logger.info(f"Detected driver version: {driver_version}, CUDA version: {cuda_version}")
        # 2026 H100 driver minimum is 550.54.15, CUDA 12.8
        if driver_version < 5505415:  # Driver version encoded as integer: 550.54.15 -> 5505415
            raise RuntimeError(f"Driver version {driver_version} too old. Require >= 550.54.15")
        if float(cuda_version) < 12.8:
            raise RuntimeError(f"CUDA version {cuda_version} too old. Require >= 12.8")
    except Exception as e:
        logger.error(f"Compatibility check failed: {e}")
        sys.exit(1)

def train_llm_h100(
    model_name: str = "meta-llama/Llama-2-7b-hf",
    batch_size: int = 16,
    epochs: int = 3,
    lr: float = 2e-5,
    use_flash_attn: bool = True
) -> None:
    """Train a causal LLM on H100 with 2026 driver optimizations"""
    check_h100_driver_compatibility()

    # Initialize tokenizer and model with H100-optimized flash attention
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    if tokenizer.pad_token is None:
        tokenizer.add_special_tokens({"pad_token": "[PAD]"})

    try:
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.bfloat16,  # H100 native bfloat16 support
            attn_implementation="flash_attention_2" if use_flash_attn else "eager",
            device_map="auto"  # Leverages H100 multi-GPU driver async memory pool
        )
    except OSError as e:
        logger.error(f"Model loading failed: {e}")
        raise

    # Enable PyTorch 2.5 CUDA graph capture for H100 driver
    model = torch.compile(
        model,
        backend="inductor",
        options={"cuda_graph_capture": True, "h100_optimized": True}
    )

    # Dummy dataset for demonstration (replace with real LLM corpus)
    class DummyLLMDataset:
        def __init__(self, tokenizer, seq_len=2048, size=1000):
            self.tokenizer = tokenizer
            self.seq_len = seq_len
            self.size = size

        def __len__(self):
            return self.size

        def __getitem__(self, idx):
            # Generate random token sequences for demo
            input_ids = torch.randint(0, tokenizer.vocab_size, (self.seq_len,))
            return {"input_ids": input_ids, "labels": input_ids.clone()}

    dataset = DummyLLMDataset(tokenizer)
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

    # Optimizer with H100 driver-optimized fused kernels
    optimizer = optim.AdamW(
        model.parameters(),
        lr=lr,
        fused=True  # Requires CUDA 12.8+ and 2026 H100 driver
    )
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)

    # Training loop with error handling for OOM
    for epoch in range(epochs):
        model.train()
        total_loss = 0.0
        for batch_idx, batch in enumerate(dataloader):
            try:
                batch = {k: v.to("cuda") for k, v in batch.items()}
                outputs = model(**batch)
                loss = outputs.loss
                loss.backward()
                optimizer.step()
                optimizer.zero_grad()
                total_loss += loss.item()
                if batch_idx % 10 == 0:
                    logger.info(f"Epoch {epoch+1}, Batch {batch_idx}, Loss: {loss.item():.4f}")
            except torch.cuda.OutOfMemoryError as e:
                logger.error(f"OOM at batch {batch_idx}: {e}")
                # Clear cache using H100 driver async memory release
                torch.cuda.empty_cache()
                continue
        avg_loss = total_loss / len(dataloader)
        logger.info(f"Epoch {epoch+1} Average Loss: {avg_loss:.4f}")
        scheduler.step()

    # Save model with H100 driver-optimized serialization
    model.save_pretrained("./h100_trained_llama2_7b")
    tokenizer.save_pretrained("./h100_trained_llama2_7b")
    logger.info("Training complete. Model saved.")

if __name__ == "__main__":
    try:
        train_llm_h100()
    except Exception as e:
        logger.error(f"Training failed: {e}")
        sys.exit(1)

import tensorflow as tf
import numpy as np
import logging
import sys
from typing import Optional

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def check_tf_h100_compatibility() -> None:
    """Verify TensorFlow 2.18 and H100 2026 driver compatibility"""
    if not tf.config.list_physical_devices("GPU"):
        raise RuntimeError("No GPU detected. H100 2026 driver required for TF 2.18 LLM training")
    try:
        # Get GPU details
        gpus = tf.config.list_physical_devices("GPU")
        for gpu in gpus:
            details = tf.config.experimental.get_device_details(gpu)
            logger.info(f"GPU: {details.get('device_name', 'Unknown')}, Compute Capability: {details.get('compute_capability', 'Unknown')}")
        # Check TF version
        tf_version = tf.__version__
        if tuple(map(int, tf_version.split("."))) < (2, 18):
            raise RuntimeError(f"TensorFlow version {tf_version} too old. Require 2.18+")
        # Check CUDA and driver version via TF
        cuda_version = tf.sysconfig.get_build_info()["cuda_version"]
        if float(cuda_version) < 12.8:
            raise RuntimeError(f"CUDA version {cuda_version} too old. Require 12.8+ for H100 2026 driver")
    except Exception as e:
        logger.error(f"Compatibility check failed: {e}")
        sys.exit(1)

def train_llm_tf_h100(
    vocab_size: int = 32000,
    seq_len: int = 2048,
    batch_size: int = 8,
    epochs: int = 3,
    lr: float = 2e-5
) -> None:
    """Train a simple LLM using TensorFlow 2.18 on H100 with 2026 driver"""
    check_tf_h100_compatibility()

    # Enable mixed precision (H100 native bfloat16)
    tf.keras.mixed_precision.set_global_policy("mixed_bfloat16")

    # Enable XLA compilation with H100 2026 driver optimizations
    tf.config.optimizer.set_jit(True)
    tf.config.optimizer.set_experimental_options({
        "h100_xla_optimizations": True,
        "async_memory_pool": True  # Leverages H100 2026 driver async memory
    })

    # Define a simple causal LLM model for demonstration
    class SimpleLLM(tf.keras.Model):
        def __init__(self, vocab_size: int, seq_len: int, embed_dim: int = 4096, num_heads: int = 32):
            super().__init__()
            self.embed = tf.keras.layers.Embedding(vocab_size, embed_dim)
            self.pos_embed = tf.keras.layers.Embedding(seq_len, embed_dim)
            # Use H100-optimized flash attention via TF 2.18
            self.attn = tf.keras.layers.MultiHeadAttention(
                num_heads=num_heads,
                key_dim=embed_dim // num_heads,
                flash_attention=True  # Requires TF 2.18 and H100 2026 driver
            )
            self.dense = tf.keras.layers.Dense(vocab_size)

        def call(self, inputs):
            seq_len = tf.shape(inputs)[1]
            positions = tf.range(seq_len)
            x = self.embed(inputs) + self.pos_embed(positions)
            x = self.attn(x, x)  # Self-attention
            x = self.dense(x)
            return x

    # Initialize model
    model = SimpleLLM(vocab_size=vocab_size, seq_len=seq_len)

    # Compile model with H100 driver-optimized fused kernels
    model.compile(
        optimizer=tf.keras.optimizers.AdamW(learning_rate=lr, fused=True),  # Fused optimizer requires CUDA 12.8+
        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=["accuracy"]
    )

    # Dummy dataset for demonstration
    def dummy_dataset(seq_len: int, vocab_size: int, size: int = 1000):
        for _ in range(size):
            inputs = np.random.randint(0, vocab_size, (seq_len,))
            yield inputs, inputs  # Labels are same as inputs for causal LM

    # Create TF dataset
    dataset = tf.data.Dataset.from_generator(
        lambda: dummy_dataset(seq_len, vocab_size),
        output_signature=(
            tf.TensorSpec(shape=(seq_len,), dtype=tf.int32),
            tf.TensorSpec(shape=(seq_len,), dtype=tf.int32)
        )
    ).batch(batch_size).prefetch(tf.data.AUTOTUNE)

    # Training loop with error handling
    try:
        logger.info("Starting training...")
        history = model.fit(
            dataset,
            epochs=epochs,
            callbacks=[
                tf.keras.callbacks.EarlyStopping(patience=2),
                tf.keras.callbacks.TensorBoard(log_dir="./logs/h100_tf")
            ]
        )
        logger.info(f"Training complete. Final loss: {history.history['loss'][-1]:.4f}")
    except tf.errors.ResourceExhaustedError as e:
        logger.error(f"OOM error during training: {e}")
        # Clear GPU memory using H100 driver async release
        tf.config.experimental.reset_memory_stats("GPU:0")
        raise
    except Exception as e:
        logger.error(f"Training failed: {e}")
        raise

    # Save model with H100-optimized serialization
    model.save("./h100_trained_tf_llm", save_format="tf")
    logger.info("Model saved to ./h100_trained_tf_llm")

if __name__ == "__main__":
    try:
        train_llm_tf_h100()
    except Exception as e:
        logger.error(f"Main failed: {e}")
        sys.exit(1)

import subprocess
import json
import logging
import sys
import os
from typing import Dict, List, Optional

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class H100DriverTuner:
    """Tune NVIDIA 2026 H100 driver for optimal LLM training performance"""
    # 2026 H100 driver minimum version
    MIN_DRIVER_VERSION = "550.54.15"
    # Optimal settings for LLM training
    OPTIMAL_SETTINGS = {
        "compute_mode": "EXCLUSIVE_PROCESS",
        "persistence_mode": "ENABLED",
        "power_limit": "400W",  # H100 max power for sustained training
        "memory_fraction": "0.95",  # Reserve 5% for driver overhead
        "async_memory_pool": "ENABLED",
        "kernel_launch_overhead_reduction": "ENABLED"
    }

    def __init__(self, gpu_ids: Optional[List[int]] = None):
        self.gpu_ids = gpu_ids or self._get_all_h100_gpus()
        self._verify_driver_version()

    def _get_all_h100_gpus(self) -> List[int]:
        """Get list of H100 GPU IDs using nvidia-smi"""
        try:
            result = subprocess.run(
                ["nvidia-smi", "--query-gpu=index,name,driver_version", "--format=csv,noheader,json"],
                capture_output=True,
                text=True,
                check=True
            )
            gpus = json.loads(result.stdout)
            h100_gpus = []
            for gpu in gpus:
                if "H100" in gpu["name"]:
                    h100_gpus.append(int(gpu["index"]))
            if not h100_gpus:
                raise RuntimeError("No H100 GPUs detected")
            logger.info(f"Detected H100 GPUs: {h100_gpus}")
            return h100_gpus
        except subprocess.CalledProcessError as e:
            logger.error(f"Failed to query GPUs: {e.stderr}")
            sys.exit(1)
        except json.JSONDecodeError as e:
            logger.error(f"Failed to parse nvidia-smi output: {e}")
            sys.exit(1)

    def _verify_driver_version(self) -> None:
        """Verify driver version meets 2026 H100 requirements"""
        try:
            result = subprocess.run(
                ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"],
                capture_output=True,
                text=True,
                check=True
            )
            driver_version = result.stdout.strip().split("\n")[0]
            logger.info(f"Detected driver version: {driver_version}")
            # Compare version strings (simple split for demo)
            min_ver = tuple(map(int, self.MIN_DRIVER_VERSION.split(".")))
            curr_ver = tuple(map(int, driver_version.split(".")))
            if curr_ver < min_ver:
                raise RuntimeError(f"Driver version {driver_version} too old. Require {self.MIN_DRIVER_VERSION}+")
        except Exception as e:
            logger.error(f"Driver version check failed: {e}")
            sys.exit(1)

    def _run_nvidia_smi(self, args: List[str]) -> str:
        """Run nvidia-smi with given arguments, return output"""
        try:
            result = subprocess.run(
                ["nvidia-smi"] + args,
                capture_output=True,
                text=True,
                check=True
            )
            return result.stdout
        except subprocess.CalledProcessError as e:
            logger.error(f"nvidia-smi failed: {e.stderr}")
            raise

    def apply_optimal_settings(self) -> None:
        """Apply optimal H100 2026 driver settings for LLM training"""
        for gpu_id in self.gpu_ids:
            logger.info(f"Tuning GPU {gpu_id}...")
            # Set compute mode
            self._run_nvidia_smi(["-i", f"{gpu_id}", "-c", "EXCLUSIVE_PROCESS"])
            # Enable persistence mode
            self._run_nvidia_smi(["-i", f"{gpu_id}", "-pm", "ENABLED"])
            # Set power limit
            self._run_nvidia_smi(["-i", f"{gpu_id}", "-pl", "400"])
            # Set memory fraction (using CUDA_VISIBLE_DEVICES and driver settings)
            # Note: This requires H100 2026 driver's async memory pool
            os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, self.gpu_ids))
            os.environ["NVIDIA_DRIVER_ALLOW_UNSUPPORTED_MEM_FRACTION"] = "1"
            # Enable async memory pool via driver environment variable
            os.environ["H100_ASYNC_MEM_POOL"] = "ENABLED"
            logger.info(f"GPU {gpu_id} tuning complete.")

    def verify_settings(self) -> Dict[int, Dict[str, str]]:
        """Verify applied settings match optimal values"""
        results = {}
        for gpu_id in self.gpu_ids:
            logger.info(f"Verifying GPU {gpu_id} settings...")
            # Query current settings
            result = subprocess.run(
                ["nvidia-smi", "-i", f"{gpu_id}", "-q"],
                capture_output=True,
                text=True,
                check=True
            )
            # Parse output (simplified for demo)
            settings = {
                "compute_mode": "EXCLUSIVE_PROCESS" if "EXCLUSIVE_PROCESS" in result.stdout else "UNKNOWN",
                "persistence_mode": "ENABLED" if "Enabled" in result.stdout else "DISABLED",
                "power_limit": "400W" if "400 W" in result.stdout else "UNKNOWN"
            }
            results[gpu_id] = settings
            logger.info(f"GPU {gpu_id} settings: {settings}")
        return results

if __name__ == "__main__":
    try:
        tuner = H100DriverTuner(gpu_ids=[0,1])  # Tune GPUs 0 and 1
        tuner.apply_optimal_settings()
        tuner.verify_settings()
        logger.info("H100 driver tuning complete. Ready for LLM training.")
    except Exception as e:
        logger.error(f"Tuning failed: {e}")
        sys.exit(1)

# Enable CUDA graph capture in PyTorch 2.5 for H100
model = torch.compile(
    model,
    backend="inductor",
    options={
        "cuda_graph_capture": True,
        "h100_optimized": True,
        "static_graph": True  # Required for full graph capture
    }
)

# Enable XLA H100 optimizations in TF 2.18
tf.config.optimizer.set_jit(True)
tf.config.optimizer.set_experimental_options({
    "h100_xla_optimizations": True,
    "async_memory_pool": True
})

# Tune H100 driver for multi-GPU LLM training
export NVIDIA_DRIVER_ALLOW_UNSUPPORTED_MEM_FRACTION=1
export H100_ASYNC_MEM_POOL=ENABLED
export NCCL_BUFFSIZE=2097152
nvidia-smi -c EXCLUSIVE_PROCESS -pm ENABLED