16337
42db2b0ca9
- 根据 RTX 3050 8GB 实测结果更新 GPU 需求建议 - 添加 bitsandbytes 兼容性问题记录 - 生成包含实测数据的综合测试报告 REPORT.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
"""GPU 算力需求分析"""
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import json
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import os
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# Qwen3.5-9B 不同精度下的显存需求估算
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GPU_REQUIREMENTS = {
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"model": "Qwen3.5-9B",
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"parameters": "9B",
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"precision_requirements": {
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"FP32": {
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"model_size_gb": 36,
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"min_vram_gb": 40,
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"recommended_gpus": ["A100 80GB", "H100 80GB"],
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"note": "不推荐，显存占用过大",
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},
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"FP16/BF16": {
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"model_size_gb": 18,
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"min_vram_gb": 22,
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"recommended_gpus": ["A100 40GB", "RTX 4090 24GB", "RTX A6000 48GB", "V100 32GB"],
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"note": "标准推理精度，推荐用于生产环境",
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},
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"INT8": {
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"model_size_gb": 9,
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"min_vram_gb": 12,
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"recommended_gpus": ["RTX 4070 Ti 16GB", "RTX 3090 24GB", "T4 16GB", "RTX 4080 16GB"],
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"note": "轻微精度损失，性价比高",
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},
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"INT4 (NF4)": {
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"model_size_gb": 5,
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"min_vram_gb": 8,
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"recommended_gpus": ["RTX 4060 8GB", "RTX 3060 12GB", "RTX 3070 8GB"],
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"note": "理论可行但 bitsandbytes 在 Windows 上兼容性差，不推荐",
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},
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},
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"actual_test_results": {
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"gpu": "NVIDIA GeForce RTX 3050 OEM 8GB",
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"method": "FP16 + CPU offload (accelerate device_map=auto)",
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"gpu_vram_used_gb": 3.91,
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"ram_used_gb": 13.60,
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"inference_speed_tokens_per_sec": 0.4,
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"output_quality": "极差（乱码/重复输出）",
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"conclusion": "RTX 3050 8GB 无法有效运行 Qwen3.5-9B，显存不足导致大量层 offload 到 CPU，推理极慢且输出质量不可用",
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"issues": [
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"bitsandbytes 4-bit 量化不支持 CPU offload，8GB 显存装不下完整 4-bit 模型",
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"bitsandbytes INT8 与 accelerate 版本不兼容（Windows）",
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"FP16 + CPU offload 虽可加载但速度仅 0.4 tokens/s，输出为乱码",
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],
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},
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"deployment_recommendations": {
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"开发测试": {
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"gpu": "RTX 3060 12GB / RTX 4060 Ti 16GB",
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"precision": "INT8 或 INT4",
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"concurrent": 1,
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"cost_estimate": "~2500-4000 RMB (显卡)",
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},
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"小规模部署": {
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"gpu": "RTX 4090 (24GB)",
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"precision": "FP16",
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"concurrent": "2-4",
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"cost_estimate": "~12000-15000 RMB (显卡)",
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},
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"生产环境": {
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"gpu": "A100 40GB / H100 80GB",
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"precision": "FP16/BF16",
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"concurrent": "8-32 (vLLM)",
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"cost_estimate": "~60000-200000 RMB (显卡) 或云服务按需",
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},
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},
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}
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def analyze_gpu_requirements():
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"""输出 GPU 需求分析"""
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print("=" * 60)
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print("Qwen3.5-9B GPU 算力需求分析")
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print("=" * 60)
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for precision, info in GPU_REQUIREMENTS["precision_requirements"].items():
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print(f"\n{precision}:")
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print(f" 模型大小: ~{info['model_size_gb']} GB")
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print(f" 最低显存: {info['min_vram_gb']} GB")
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print(f" 推荐显卡: {', '.join(info['recommended_gpus'])}")
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print(f" 备注: {info['note']}")
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print(f"\n{'='*60}")
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print("部署方案推荐")
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print(f"{'='*60}")
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for scenario, info in GPU_REQUIREMENTS["deployment_recommendations"].items():
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print(f"\n{scenario}:")
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for k, v in info.items():
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print(f" {k}: {v}")
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# 保存
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output_dir = "vsp/qwen3.5-9b/results"
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os.makedirs(output_dir, exist_ok=True)
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path = os.path.join(output_dir, "gpu_requirements.json")
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with open(path, "w", encoding="utf-8") as f:
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json.dump(GPU_REQUIREMENTS, f, ensure_ascii=False, indent=2)
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print(f"\n结果已保存到 {path}")
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if __name__ == "__main__":
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analyze_gpu_requirements()
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