diff --git a/vsp/qwen3.5-9b/benchmark_speed.py b/vsp/qwen3.5-9b/benchmark_speed.py
new file mode 100644
index 0000000..2d8eeb4
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+++ b/vsp/qwen3.5-9b/benchmark_speed.py
@@ -0,0 +1,148 @@
+"""性能基准测试 - 推理速度、首 token 延迟、吞吐量"""
+import time
+import json
+import os
+import glob
+import torch
+import psutil
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from datetime import datetime
+
+
+def load_model():
+    """加载 4-bit 量化模型"""
+    paths = glob.glob("vsp/qwen3.5-9b/model/**/config.json", recursive=True)
+    model_path = os.path.dirname(paths[0]) if paths else "Qwen/Qwen3.5-9B"
+
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.float16,
+        bnb_4bit_use_double_quant=True,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        quantization_config=bnb_config,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    return model, tokenizer
+
+
+def benchmark_speed(model, tokenizer, num_runs=5):
+    """测试不同输入长度和输出长度下的推理速度"""
+    print("=" * 60)
+    print("性能基准测试 - 推理速度")
+    print("=" * 60)
+
+    test_cases = [
+        {"name": "短输入短输出", "prompt": "你好", "max_tokens": 50},
+        {"name": "短输入中输出", "prompt": "介绍一下人工智能", "max_tokens": 128},
+        {"name": "短输入长输出", "prompt": "请详细解释深度学习的原理和应用", "max_tokens": 256},
+        {"name": "中输入中输出", "prompt": "以下是一段代码：\n```python\ndef fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)\n```\n请分析这段代码的时间复杂度并给出优化方案。", "max_tokens": 256},
+        {"name": "长输入短输出", "prompt": "请总结以下内容的关键点：" + "人工智能是计算机科学的一个分支。" * 50, "max_tokens": 64},
+    ]
+
+    results = []
+    for case in test_cases:
+        print(f"\n--- {case['name']} (max_tokens={case['max_tokens']}) ---")
+        times = []
+        output_tokens_list = []
+
+        for run in range(num_runs):
+            messages = [{"role": "user", "content": case["prompt"]}]
+            text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            inputs = tokenizer(text, return_tensors="pt").to(model.device)
+            input_len = inputs["input_ids"].shape[1]
+
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+            with torch.no_grad():
+                outputs = model.generate(
+                    **inputs,
+                    max_new_tokens=case["max_tokens"],
+                    do_sample=False,  # greedy for reproducibility
+                )
+
+            torch.cuda.synchronize()
+            gen_time = time.perf_counter() - t0
+            output_len = outputs.shape[1] - input_len
+
+            times.append(gen_time)
+            output_tokens_list.append(output_len)
+
+        avg_time = sum(times) / len(times)
+        avg_tokens = sum(output_tokens_list) / len(output_tokens_list)
+        avg_speed = avg_tokens / avg_time if avg_time > 0 else 0
+
+        result = {
+            "test_name": case["name"],
+            "input_tokens": input_len,
+            "avg_output_tokens": round(avg_tokens, 1),
+            "avg_time_s": round(avg_time, 3),
+            "avg_tokens_per_sec": round(avg_speed, 1),
+            "min_time_s": round(min(times), 3),
+            "max_time_s": round(max(times), 3),
+        }
+        results.append(result)
+        print(f"  输入 tokens: {input_len}")
+        print(f"  平均输出 tokens: {result['avg_output_tokens']}")
+        print(f"  平均耗时: {result['avg_time_s']}s")
+        print(f"  平均速度: {result['avg_tokens_per_sec']} tokens/s")
+
+    return results
+
+
+def benchmark_memory(model):
+    """测试显存和内存占用"""
+    print(f"\n{'='*60}")
+    print("显存与内存占用")
+    print(f"{'='*60}")
+
+    result = {}
+    if torch.cuda.is_available():
+        result["gpu_allocated_gb"] = round(torch.cuda.memory_allocated() / 1024**3, 2)
+        result["gpu_reserved_gb"] = round(torch.cuda.memory_reserved() / 1024**3, 2)
+        result["gpu_total_gb"] = round(torch.cuda.get_device_properties(0).total_memory / 1024**3, 1)
+        result["gpu_name"] = torch.cuda.get_device_name(0)
+
+    process = psutil.Process()
+    result["ram_used_gb"] = round(process.memory_info().rss / 1024**3, 2)
+    result["ram_total_gb"] = round(psutil.virtual_memory().total / 1024**3, 1)
+
+    for k, v in result.items():
+        print(f"  {k}: {v}")
+
+    return result
+
+
+def save_results(speed_results, memory_results):
+    """保存测试结果"""
+    output_dir = "vsp/qwen3.5-9b/results"
+    os.makedirs(output_dir, exist_ok=True)
+
+    report = {
+        "timestamp": datetime.now().isoformat(),
+        "model": "Qwen3.5-9B",
+        "quantization": "4-bit NF4",
+        "speed_benchmark": speed_results,
+        "memory": memory_results,
+    }
+
+    output_path = os.path.join(output_dir, "benchmark_speed.json")
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(report, f, ensure_ascii=False, indent=2)
+
+    print(f"\n结果已保存到 {output_path}")
+    return output_path
+
+
+if __name__ == "__main__":
+    os.chdir(os.path.dirname(os.path.abspath(__file__)) + "/../..")
+    model, tokenizer = load_model()
+    speed_results = benchmark_speed(model, tokenizer)
+    memory_results = benchmark_memory(model)
+    save_results(speed_results, memory_results)