[TOP 5]

The Top 5 lists — ranked and benchmarked.

Curated rankings across training, inference, and cloud capacity. Updated quarterly from the Servers.Computer index.

Training

Top 5 GPUs for AI training (2026)

  1. 1
    NVIDIA B20020 PFLOPS FP4 · 192GB HBM3e

    Reference for new 70B+ training runs.

  2. 2
    NVIDIA H200989 TFLOPS FP16 · 141GB HBM3e

    Best price/perf for 7B–70B fine-tuning.

  3. 3
    NVIDIA H100989 TFLOPS FP16 · 80GB HBM3

    Workhorse of the 2024–2025 era.

  4. 4
    AMD MI300X1.3 PFLOPS FP16 · 192GB HBM3

    Largest memory per GPU for big batches.

  5. 5
    Google TPU v5p459 TFLOPS BF16 · 95GB HBM2e

    Strong if you live in JAX/XLA.

Clouds

Top 5 clouds for H100/H200 capacity

  1. 1
    CoreWeaveDense H100/H200/B200 · InfiniBand

    Largest neocloud fleet, fastest fabric.

  2. 2
    Lambda1-Click clusters · on-demand H100

    Best DX for ML teams.

  3. 3
    AWS (P5/P5e)H100 / H200 in EC2 capacity blocks

    Pair with S3 + SageMaker stack.

  4. 4
    CrusoeH100/H200 on flared-gas power

    Low-cost energy, growing fleet.

  5. 5
    RunPodCommunity & Secure cloud GPUs

    Cheapest on-demand H100 hours.

Inference

Top 5 GPUs for LLM inference

  1. 1
    NVIDIA H200141GB HBM3e · 4.8 TB/s

    Memory bandwidth is the inference bottleneck.

  2. 2
    NVIDIA B200192GB HBM3e · 8 TB/s

    Best tokens/sec/$ on FP4 quantized models.

  3. 3
    AMD MI300X192GB HBM3 · 5.3 TB/s

    Single-GPU 70B inference without sharding.

  4. 4
    NVIDIA L40S48GB GDDR6 · cost-efficient

    Best for 7B–13B serving at scale.

  5. 5
    Groq LPUDeterministic low-latency

    Highest tokens/sec for chat use cases.

Methodology

Rankings combine raw throughput, memory bandwidth, $/TFLOP, and real-world availability from the Servers.Computer index.

→ Benchmarks→ Compare GPUs