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682063abf1
- 模型加载改为 bitsandbytes 4-bit NF4 量化，device_map={"":0} 纯 GPU
- 关闭 Qwen3.5 thinking 模式 (enable_thinking=False)
- 精度从 60% 提升到 90%，推理速度 1-2 tokens/s
- GPU 显存 7.13GB/8GB，输出质量正常
- 更新所有测试结果和综合报告
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
82 lines
2.3 KiB
JSON
82 lines
2.3 KiB
JSON
{
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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": [
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"A100 80GB",
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"H100 80GB"
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],
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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": [
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"A100 40GB",
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"RTX 4090 24GB",
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"RTX A6000 48GB",
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"V100 32GB"
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],
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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": [
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"RTX 4070 Ti 16GB",
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"RTX 3090 24GB",
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"T4 16GB",
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"RTX 4080 16GB"
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],
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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": [
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"RTX 4060 8GB",
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"RTX 3060 12GB",
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"RTX 3070 8GB"
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],
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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": "4-bit NF4 量化 (bitsandbytes)，纯 GPU 运行，关闭 thinking 模式",
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"gpu_vram_used_gb": 7.13,
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"ram_used_gb": 7.59,
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"inference_speed_tokens_per_sec": "1.0-1.8",
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"accuracy": "90% (9/10)",
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"output_quality": "正常，回答准确",
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"conclusion": "RTX 3050 8GB 可以运行 Qwen3.5-9B 4-bit 量化版本，显存占用 7.13GB，推理速度 1-2 tokens/s，适合开发测试",
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"issues": [
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"显存占用 7.13GB，接近 8GB 上限，长文本可能 OOM",
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"推理速度较慢（1-2 tokens/s），不适合生产环境",
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"需关闭 thinking 模式才能正常输出"
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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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} |