AI基础设施趋势2026

GPU战争、推理即服务与边缘AI：支撑万亿参数模型的底层算力版图

引言

AI基础设施正在经历从"训练为王"到"推理为先"的结构性转型。随着大模型从研究走向大规模生产部署，推理成本已超过训练成本成为主要支出项。2026年的AI基础设施版图围绕三条主线展开：芯片架构的推理优化、云服务的Serverless化、以及边缘部署的爆发。

GPU竞争格局

NVIDIA主导与挑战者

AI加速器性能对比（2025-2026主力芯片）

                    FP16 TFLOPS    HBM容量    HBM带宽      TDP      定价(估)
                    ──────────    ────────   ────────    ─────    ────────
NVIDIA H100 SXM     989          80GB       3.35 TB/s   700W     ~$30K
NVIDIA H200 SXM     989          141GB      4.80 TB/s   700W     ~$35K
NVIDIA B200          4,500       192GB      8.00 TB/s   1000W    ~$40K
NVIDIA B300(预期)    ~5,000+     288GB      ~12 TB/s    1200W    TBD
AMD MI300X           1,307       192GB      5.30 TB/s   750W     ~$15K
AMD MI350(预期)      ~2,500      288GB      ~8.0 TB/s   TBD      TBD
Intel Gaudi 3        1,835       128GB      3.68 TB/s   900W     ~$15K
Groq LPU             750(INT8)   230MB SRAM  80 TB/s    300W     云服务
Cerebras CS-3        ~900K       44GB SRAM   ~20 PB/s   23kW     整机
华为昇腾910B         ~512        64GB       1.60 TB/s   400W     国内定价

关键趋势

1. HBM容量战：从80GB到192GB到288GB，显存容量决定单卡可承载的模型规模
2. 带宽为王：推理是memory-bound操作，HBM带宽直接决定推理吞吐量
3. 专用推理芯片崛起：Groq的确定性延迟和Cerebras的晶圆级集成代表新范式

# GPU selection decision framework
def recommend_gpu(
    model_params_b: float,
    batch_size: int,
    latency_target_ms: float,
    budget_per_gpu_usd: float,
    deployment_region: str = "global",
) -> dict:
    """Recommend GPU configuration for inference deployment."""

    # Memory requirement: ~2 bytes per parameter (FP16/BF16)
    model_memory_gb = model_params_b * 2

    gpus = {
        "H100_80GB": {"mem": 80, "bw_tbs": 3.35, "price": 30000, "avail": "global"},
        "H200_141GB": {"mem": 141, "bw_tbs": 4.80, "price": 35000, "avail": "global"},
        "B200_192GB": {"mem": 192, "bw_tbs": 8.00, "price": 40000, "avail": "limited"},
        "MI300X_192GB": {"mem": 192, "bw_tbs": 5.30, "price": 15000, "avail": "global"},
        "Ascend910B": {"mem": 64, "bw_tbs": 1.60, "price": 8000, "avail": "china"},
    }

    recommendations = []
    for name, spec in gpus.items():
        if deployment_region == "china" and spec["avail"] == "global":
            continue  # Export restrictions
        if deployment_region != "china" and spec["avail"] == "china":
            continue

        gpus_needed = max(1, int(model_memory_gb / (spec["mem"] * 0.85)) + 1)
        total_cost = gpus_needed * spec["price"]

        if total_cost / gpus_needed <= budget_per_gpu_usd:
            # Rough latency estimate (simplified)
            tokens_per_sec = spec["bw_tbs"] * 1e12 / (model_params_b * 1e9 * 2) * gpus_needed
            est_latency = 1000 / tokens_per_sec * 100  # ~100 tokens

            recommendations.append({
                "gpu": name,
                "count": gpus_needed,
                "total_cost": total_cost,
                "est_tokens_per_sec": round(tokens_per_sec),
                "est_latency_ms": round(est_latency),
            })

    recommendations.sort(key=lambda x: x["total_cost"])
    return {"model_memory_gb": model_memory_gb, "options": recommendations[:3]}

result = recommend_gpu(70, batch_size=8, latency_target_ms=500, budget_per_gpu_usd=40000)
for opt in result["options"]:
    print(f"{opt['gpu']}: {opt['count']} GPUs, ${opt['total_cost']:,d}, "
          f"~{opt['est_tokens_per_sec']} tok/s")

AI云服务格局

主流AI云提供商

提供商	GPU可用性	推理服务	定价模式	特色能力
AWS (Bedrock/SageMaker)	H100/Inf2/Trainium	Serverless+Provisioned	按token/按时	最广模型选择
Azure (AI Studio)	H100/A100/MI300X	Serverless+Managed	按token/按PTU	OpenAI独家
GCP (Vertex AI)	H100/TPU v5/A3	Serverless+Endpoints	按token/按节点	Gemini原生
阿里云 (百炼)	A100/昇腾910B	Serverless	按token	Qwen原生
火山引擎 (豆包)	A100/自研	Serverless	按token	豆包/字节生态
Lambda Labs	H100/A100	Bare metal	按时	性价比最高
Together AI	H100	Serverless	按token	开源模型推理
Groq Cloud	Groq LPU	Serverless	按token	超低延迟
Modal	H100/A100	Serverless	按秒+GPU	开发者体验
Replicate	A100/T4	Serverless	按秒	模型市场

推理即服务（Inference-as-a-Service）

推理服务架构演进

2023: 固定实例
  用户 → API Gateway → [预留GPU集群] → 响应
  缺点: 空闲浪费，扩缩容慢

2024: 弹性推理
  用户 → API Gateway → [Auto-scaling GPU Pool] → 响应
  改进: 按需扩缩，但冷启动延迟

2025-2026: Serverless推理
  用户 → API Gateway → [Serverless Inference Engine] → 响应
  ┌──────────────────────────────────────────┐
  │  Serverless Inference Engine              │
  │  ├── 模型缓存层（热模型常驻）              │
  │  ├── 请求路由器（延迟/成本/质量三维均衡）   │
  │  ├── KV Cache池化（跨请求共享前缀）        │
  │  ├── 动态批处理（毫秒级组batch）           │
  │  └── 多模型复用（同GPU多模型分时）          │
  └──────────────────────────────────────────┘
  优势: 零冷启动，按token计费，多模型复用

边缘AI芯片

端侧推理芯片格局

边缘AI芯片分类

手机SoC集成NPU:
├── Apple Neural Engine (A18 Pro): 35 TOPS, 模型: CoreML优化
├── Qualcomm Hexagon NPU (Gen 4): 75 TOPS, 模型: ONNX/QNN
├── MediaTek APU (Dimensity 9400): 46 TOPS, 模型: NeuroPilot
└── Google Tensor G5 TPU: ~30 TOPS

PC/笔记本NPU:
├── Intel Lunar Lake NPU: 48 TOPS
├── AMD XDNA 2 (Ryzen AI): 50 TOPS
├── Qualcomm Snapdragon X Elite: 45 TOPS
└── Apple M4 Neural Engine: 38 TOPS

嵌入式/IoT:
├── NVIDIA Jetson Orin NX: 100 TOPS
├── Rockchip RK3588 NPU: 6 TOPS
├── 海思Hi3559/昇腾310: 8-16 TOPS
└── 寒武纪MLU220: 16 TOPS

端侧模型部署

# Edge deployment sizing calculator
def edge_model_feasibility(
    model_params_b: float,
    quantization: str = "Q4_K_M",  # GGUF quantization
    device_ram_gb: float = 8.0,
    device_npu_tops: float = 35.0,
) -> dict:
    """Check if a model can run on an edge device."""

    # Memory per parameter by quantization
    bits_per_param = {
        "FP16": 16, "Q8_0": 8.5, "Q6_K": 6.6,
        "Q5_K_M": 5.7, "Q4_K_M": 4.8, "Q4_0": 4.5,
        "Q3_K_M": 3.9, "Q2_K": 2.7,
    }

    bits = bits_per_param.get(quantization, 4.8)
    model_size_gb = model_params_b * bits / 8
    # Leave headroom for KV cache and OS
    available_ram = device_ram_gb * 0.6

    feasible = model_size_gb < available_ram

    # Rough token/s estimate (very simplified)
    if feasible and device_npu_tops > 0:
        tokens_per_sec = device_npu_tops * 1e12 / (model_params_b * 1e9 * bits) * 0.1
    else:
        tokens_per_sec = 0

    return {
        "model_size_gb": round(model_size_gb, 1),
        "available_ram_gb": round(available_ram, 1),
        "feasible": feasible,
        "est_tokens_per_sec": round(tokens_per_sec, 1),
        "recommendation": (
            f"OK: {quantization} fits in {device_ram_gb}GB device"
            if feasible
            else f"Too large: need {model_size_gb:.1f}GB, only {available_ram:.1f}GB available"
        ),
    }

# Test various configurations
configs = [
    (1.5, "Q4_K_M", 4, 35),   # 1.5B on phone
    (3.0, "Q4_K_M", 8, 35),   # 3B on phone
    (7.0, "Q4_K_M", 8, 35),   # 7B on phone
    (7.0, "Q4_K_M", 16, 38),  # 7B on laptop
    (14.0, "Q4_K_M", 32, 38), # 14B on laptop
    (70.0, "Q4_K_M", 32, 38), # 70B on laptop
]

for params, quant, ram, tops in configs:
    r = edge_model_feasibility(params, quant, ram, tops)
    status = "OK" if r["feasible"] else "NO"
    print(f"{params:>5.1f}B {quant:>7s} on {ram:>2d}GB: [{status}] "
          f"{r['model_size_gb']:>5.1f}GB, ~{r['est_tokens_per_sec']:>5.1f} tok/s")

网络与互联

GPU集群互联技术

技术	带宽	延迟	适用规模	代表
NVLink (5th gen)	1.8 TB/s	<1us	节点内	NVIDIA DGX B200
NVSwitch	14.4 TB/s (fabric)	<1us	8-GPU节点	NVIDIA NVSwitch 4
InfiniBand NDR	400 Gb/s/port	~1us	集群	NVIDIA Quantum-2
InfiniBand XDR	800 Gb/s/port	~1us	集群	NVIDIA Quantum-X800
RoCE v2	400 Gb/s	~2us	通用集群	Broadcom/Mellanox
Ultra Ethernet	400-800 Gb/s	~2us	云数据中心	UEC联盟

存储与数据基础设施

AI工作负载的存储需求

AI数据流水线存储需求

训练数据准备:
  原始数据 → 清洗/过滤 → Token化 → 训练集
  容量: 10TB-1PB+
  性能: 高吞吐顺序读 (10+ GB/s)
  存储: 分布式文件系统 (Lustre/GPFS/WekaFS)

模型检查点:
  每N步保存完整模型状态
  70B模型: ~280GB/checkpoint (FP16+优化器状态)
  容量: 10-100TB (训练周期)
  性能: 突发写入 (10+ GB/s)

推理服务:
  模型权重加载 + KV Cache
  70B模型: ~140GB权重 + 动态KV Cache
  性能: 快速加载 (冷启动优化)
  存储: 本地NVMe SSD + 网络缓存

向量数据库:
  Embedding存储与检索
  容量: 100GB-10TB
  性能: 低延迟随机读 (<10ms)
  存储: SSD-backed向量DB (Pinecone/Milvus/Qdrant)

总结与展望

2026年AI基础设施的核心变化是"推理成本成为主战场"。NVIDIA的B系列GPU在推理性能上实现了代际突破，但AMD MI300X凭借更高的性价比正在赢得云厂商的青睐。与此同时，Groq和Cerebras等专用架构在特定场景下展现出数量级的性能优势。对于工程团队，关键决策不再是"用什么GPU"，而是"在哪个抽象层部署"——从裸金属到Serverless，不同的抽象层级意味着不同的成本结构、灵活性和工程复杂度。

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Maurice | maurice_wen@proton.me