Browsing: HighPerformance

@tilelang.jit(out_idx=[-1]) def make_matmul(M: int, N: int, K: int, block_M: int = 128, block_N: int = 128, block_K: int = 32, num_stages: int = 3, threads: int…

The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But…

import subprocess, sys subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, “datashader”, “colorcet”, “numba”, “scipy”]) import numpy as np import pandas as pd import datashader as ds import datashader.transfer_functions…

The transition from a raw dataset to a fine-tuned Large Language Model (LLM) traditionally involves significant infrastructure overhead, including CUDA environment management and high VRAM requirements.…

angles = np.linspace(0.0, 2.0 * np.pi, n_particles, endpoint=False, dtype=np.float32) px0_np = 0.4 * np.cos(angles).astype(np.float32) py0_np = (0.7 + 0.15 * np.sin(angles)).astype(np.float32) vx0_np = (-0.8 * np.sin(angles)).astype(np.float32)…

Infiniti could launch a hotter QX80 variant with 600 horsepower.   It could be a ‘first step’ toward a model inspired by the 650-horsepower QX80 Track…

print(“5) Concurrency progress: thread_map / process_map”) def cpuish(n: int) -> int: x = 0 for i in range(50_000): x = (x + (n * i)) %…

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a…

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As the industry moves from simple Large Language Model (LLM) inference toward autonomous agentic systems, the challenge for devs have shifted. It is no longer just…