Leverage Blackwell's capabilities for maximum performance in training and inference workloads.

Training Optimization

Mixed-Precision Training

import torch
from torch.cuda.amp import autocast, GradScaler

# Automatic Mixed Precision on Blackwell
scaler = GradScaler()

for data, target in dataloader:
    optimizer.zero_grad()
    
    # FP8 forward pass (Blackwell Transformer Engine)
    with autocast(dtype=torch.float8_e4m3fn):
        output = model(data)
        loss = criterion(output, target)
    
    # Scaled backward pass
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

Distributed Training

import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# Initialize distributed training across Blackwell GPUs
dist.init_process_group(backend='nccl')
local_rank = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)

model = YourModel().to(local_rank)
model = DDP(model, device_ids=[local_rank])

# NVLink-optimized gradient synchronization
for data, target in dataloader:
    output = model(data)
    loss = criterion(output, target)
    loss.backward()  # Gradients synced via NVLink
    optimizer.step()

Inference Optimization

FP4 Quantization

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model with FP4 quantization (Blackwell-optimized)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-70b",
    torch_dtype=torch.float8_e4m3fn,  # FP4/FP8 mode
    device_map="auto",
    load_in_4bit=True
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3-70b")

# 4x faster inference on Blackwell
inputs = tokenizer("Generate a summary:", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=500)

TensorRT-LLM Deployment

import tensorrt_llm
from tensorrt_llm.runtime import ModelRunner

# Compile model for Blackwell optimizations
engine = tensorrt_llm.build(
    model_path="llama-70b",
    dtype="fp8",  # Blackwell FP8 precision
    max_batch_size=32,
    max_input_len=8192,  # Extended context on Blackwell
    max_output_len=2048,
    use_gpt_attention_plugin=True,  # Transformer Engine
    use_gemm_plugin=True
)

runner = ModelRunner.from_engine(engine)
outputs = runner.generate(input_tokens, max_new_tokens=100)

Memory Optimization

Gradient Checkpointing

from torch.utils.checkpoint import checkpoint

# Reduce memory usage for large models
class OptimizedTransformer(nn.Module):
    def forward(self, x):
        # Checkpoint layers to trade compute for memory
        for layer in self.layers:
            x = checkpoint(layer, x)
        return x

Flash Attention 3

from flash_attn import flash_attn_func

# Blackwell-optimized attention (built into Transformer Engine)
attn_output = flash_attn_func(
    q, k, v,
    dropout_p=0.1,
    softmax_scale=None,
    causal=True,
    window_size=(-1, -1),  # Full attention
    alibi_slopes=None
)