Dukungan CUDA native Windows on Arm Nvidia

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load Llama 4 70B langsung di unified memory N1X
model_id = "meta-llama/Llama-4-70B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,     # fp16 untuk efisiensi
    device_map="cuda",             # gunakan GPU N1X
    load_in_4bit=True,            # 4-bit quantization
)

# N1X: 128GB unified memory = model 70B muat penuh!
# RTX 5090 (24GB VRAM): model overflow ke RAM

inputs = tokenizer("Jelaskan transformer attention",
                   return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

from transformers import TrainingArguments
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer

lora_config = LoraConfig(
    r=16, lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

# N1X: 128GB unified memory memungkinkan
# fine-tune model 70B dengan QLoRA

training_args = TrainingArguments(
    output_dir="./fine-tuned-model",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    fp16=True,                   # CUDA fp16 native
    dataloader_num_workers=4,    # 20 ARM cores
)

import cupy as cp          # NumPy di GPU
import cudf                   # Pandas di GPU
import cuml                   # scikit-learn di GPU
from cuml import RandomForestClassifier

# Semua berjalan di GPU Blackwell N1X
df = cudf.read_csv("dataset.csv")
X = df.drop("target", axis=1)
y = df["target"]

model = RandomForestClassifier(n_estimators=1000)
model.fit(X, y)                # GPU-accelerated training

# CuPy: operasi matrix seperti NumPy tapi di GPU
arr = cp.random.rand(100_000, 100_000)
result = cp.linalg.svd(arr) # SVD di GPU Blackwell

# Install TensorRT untuk RTX (Blackwell-optimized)
pip install torch torch-tensorrt tensorrt \
  --extra-index-url \
  https://download.pytorch.org/whl/nightly/cu130

# Atau via Nvidia Python package index
pip install nvidia-tensorrt \
  --extra-index-url https://pypi.ngc.nvidia.com

import torch
import torch_tensorrt

# Model PyTorch biasa
model = MyModel().eval().cuda()

# Compile ke TensorRT Engine (Blackwell FP4)
trt_model = torch_tensorrt.compile(
    model,
    inputs=[torch_tensorrt.Input(
        min_shape=[1, 3, 224, 224],
        opt_shape=[8, 3, 224, 224],
        max_shape=[32, 3, 224, 224],
    )],
    enabled_precisions={torch.float4},  # FP4 Blackwell
    workspace_size=1 << 33,
)

# Simpan engine untuk production
torch.save(trt_model, "model_trt_fp4.pt")

from tensorrt_llm import LLM, SamplingParams

# TensorRT-LLM: 62 tok/s vs 35 tok/s PyTorch
# pada Llama 4 70B di N1X

llm = LLM(
    model="meta-llama/Llama-4-70B-Instruct",
    tensor_parallel_size=1,   # single N1X chip
    dtype="float4",             # FP4 Blackwell
    max_model_len=131072,      # 128K context
)

sampling = SamplingParams(
    temperature=0.7,
    max_tokens=512
)

outputs = llm.generate(
    ["Analyze this code and suggest improvements:"],
    sampling
)
print(outputs[0].outputs[0].text)