Lambda 获 10 亿美元债务融资 以扩大 NVIDIA GPU 算力基础设施

import os
from vllm import LLM, SamplingParams

# 配置 GPU 显卡环境变量与 NCCL 无损通信参数
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
os.environ["NCCL_DEBUG"] = "INFO"

print("正在初始化 vLLM 分布式推理引擎（张量并行度 Tensor Parallelism = 4）...")

# 初始化 vLLM 模型实例
llm = LLM(
    model="meta-llama/Llama-3.3-70B-Instruct",
    tensor_parallel_size=4,        # 跨 4 张 GPU 进行张量并行
    gpu_memory_utilization=0.90,   # GPU 显存利用率上限 90%
    max_model_len=8192,            # 上下文窗口长度
    trust_remote_code=True,
    enforce_eager=False,           # 启用 CUDA Graph 编译加速
    dtype="bfloat16"               # 使用 bfloat16 精度降低显存占用
)

# 设置采样参数
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.95,
    max_tokens=1024
)

# 提示词列表
prompts = [
    "请详细分析分布在多个节点上的 GPU 集群在处理大模型训练时的通信瓶颈。",
    "解释 RoCE v2 协议如何在无损以太网中减少数据包丢失与延迟。"
]

# 执行批量推理生成
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"输入提示词: {prompt}")
    print(f"模型输出结果:\n{generated_text}\n" + "-"*50)

import time
import requests
from typing import Dict, Any, Optional

class UnifiedAIClient:
    """基于 n1n.ai 统一 API 网关的企业级多模型调用客户端"""
    
    def __init__(self, api_key: str, base_url: str = "https://api.n1n.ai/v1"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def generate_completion(
        self, 
        prompt: str, 
        model: str = "deepseek-v3", 
        fallback_model: str = "claude-3-5-sonnet",
        max_tokens: int = 1024
    ) -> Optional[Dict[str, Any]]:
        """执行大模型生成任务，包含自动备用模型切换与延迟统计功能"""
        payload = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": max_tokens,
            "temperature": 0.7
        }
        
        start_time = time.time()
        try:
            # 发起主模型请求
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=self.headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            elapsed_ms = (time.time() - start_time) * 1000
            print(f"请求成功！使用模型: {model} | 响应延迟: {elapsed_ms:.2f}毫秒")
            return response.json()
            
        except requests.exceptions.RequestException as e:
            print(f"主模型 {model} 请求异常: {e}。正在自动切换至备用模型 {fallback_model}...")
            payload["model"] = fallback_model
            try:
                # 发起备用模型请求
                fallback_resp = requests.post(
                    f"{self.base_url}/chat/completions",
                    headers=self.headers,
                    json=payload,
                    timeout=30
                )
                fallback_resp.raise_for_status()
                return fallback_resp.json()
                
            except requests.exceptions.RequestException as fallback_err:
                print(f"备用模型请求亦失败: {fallback_err}")
                return None

# 调用示例
if __name__ == "__main__":
    # 配置 n1n.ai 提供的 API 密钥
    API_KEY = "YOUR_N1N_API_KEY"
    client = UnifiedAIClient(api_key=API_KEY)
    
    test_prompt = "请简要分析 Lambda 获得 10 亿美元债务融资对当前 AI 芯片供给格局的影响。"
    result = client.generate_completion(prompt=test_prompt, model="deepseek-v3")
    
    if result:
        print("\n生成回答结果:")
        print(result["choices"][0]["message"]["content"])