大模型与 AI 算力架构

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadLatentAttention(nn.Module):
    def __init__(self, dim=7168, num_heads=128, head_dim=128, kv_lora_rank=512, qk_rope_dim=64):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.kv_lora_rank = kv_lora_rank
        self.qk_rope_dim = qk_rope_dim
        
        

import asyncio
from concurrent.futures import ThreadPoolExecutor
from fastapi import FastAPI
import uvicorn

app = FastAPI(title="Neuronix No-GIL High-Throughput Engine")
executor = ThreadPoolExecutor(max_workers=32)

def heavy_cpu_matrix_math(n: int) -> float:
    # 在 Python 3.13 (No-GIL) 下，32 个线程可以真正同时跑满 32 个 CPU 核心！
    total = sum(i * i for i in range(n))
    return float(total)

@app.get("/co