Cloud KVM GPUs

import bpy

# Configure GPU rendering in Blender
bpy.context.scene.render.engine = 'CYCLES'
bpy.context.preferences.addons['cycles'].preferences.compute_device_type = 'OPTIX'

for device in bpy.context.preferences.addons['cycles'].preferences.devices:
    if device.type == 'OPTIX':
        device.use = True

bpy.context.scene.cycles.device = 'GPU'
bpy.context.scene.render.resolution_x = 3840  # 4K resolution
bpy.context.scene.render.resolution_y = 2160
bpy.context.scene.cycles.samples = 1024
bpy.context.scene.cycles.denoiser = 'OPENIMAGEDENOISE'

bpy.ops.render.render(write_still=True)

import numpy as np
from numba import cuda

@cuda.jit
def scientific_simulation_gpu(data, result, params):
    i, j = cuda.grid(2)

    if i < data.shape[0] and j < data.shape[1]:
        x = data[i, j]
        a, b, c = params
        result[i, j] = a * np.sin(x) + b * np.cos(x) + c * x**2

data = np.random.random((10000, 10000)).astype(np.float32)
result = np.zeros_like(data)
params = (2.5, 1.3, 0.7)

threads_per_block = (16, 16)
blocks_per_grid_x = (data.shape[0] + threads_per_block[0] - 1) // threads_per_block[0]
blocks_per_grid_y = (data.shape[1] + threads_per_block[1] - 1) // threads_per_block[1]
blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y)

scientific_simulation_gpu[blocks_per_grid, threads_per_block](data, result, params)

// Example game server configuration
const config = {
  network: {
    tickRate: 60,
    interpolation: true,
    prediction: true,
    lagCompensation: true,
    maxPlayers: 100
  },
  graphics: {
    renderer: 'WebGL2',
    antialiasing: 4,
    shadows: { enabled: true, quality: 'high', resolution: 2048 },
    textures: { compression: 'bc7', anisotropy: 16 },
    physics: { enabled: true, solverIterations: 10, broadphase: 'GPU' }
  },
  scaling: {
    minInstances: 1,
    maxInstances: 10,
    playersPerInstance: 100,
    gpuScaling: {
      enableDynamicGPUAllocation: true,
      minGPUsPerInstance: 1,
      maxGPUsPerInstance: 4
    }
  }
};