NVIDIA H100 vs H200: Which GPU for AI Training and Inference?

Vishnu Subramanian

Founder @JarvisLabs.ai

The H200 has 141GB HBM3e memory and 4.8 TB/s bandwidth versus H100's 80GB HBM3 and 3.35 TB/s. Both run on the same Hopper architecture with identical compute specs. The H200's advantage is memory capacity and bandwidth, not raw compute. For compute-bound workloads that fit in 80GB, performance is often similar, making H100 the more cost-effective choice.

H100 vs H200: Specs Comparison

These specs are for SXM variants. H100 NVL (94GB, 3.9 TB/s, 350-400W) and H200 NVL (141GB, 4.8 TB/s, up to 600W) have different power profiles but the same memory/bandwidth characteristics for H200.

The H200 provides 76% more memory and 43% higher bandwidth than H100 SXM while sharing the same Hopper architecture and Tensor Core configuration.

NVIDIA H100 Overview

NVIDIA announced the H100 at GTC 2022 as the flagship Hopper architecture GPU. It represented a significant jump from the A100 in both compute and memory bandwidth.

The H100 SXM delivers 3.35 TB/s of HBM3 bandwidth. Compared to the A100 family, that's about 2.1x the bandwidth of A100 40GB (1,555 GB/s) and about 1.6x the bandwidth of A100 80GB (2,039 GB/s). The exact comparison depends on which A100 variant you're measuring against.

Key H100 features:

• Fourth-generation Tensor Cores
• Transformer Engine that dynamically manages FP8/FP16 precision
• Native FP8 support for efficient inference
• 80 billion transistors on TSMC 4N process

NVIDIA reports up to 4x faster GPT-3 (175B) training versus A100 in its published benchmarks, though real-world gains depend heavily on model architecture, batch size, and optimization level.

NVIDIA H200 Overview

NVIDIA announced the H200 at Supercomputing 2023. It's not a new architecture but an enhanced H100 with significantly more memory. The H200 uses HBM3e instead of HBM3, pushing capacity to 141GB and bandwidth to 4.8 TB/s.

Key H200 characteristics:

• Same Hopper architecture as H100
• Same Tensor Core configuration and compute specs
• 141GB HBM3e (76% more than H100)
• 4.8 TB/s bandwidth (43% higher than H100)
• Available in SXM and NVL form factors

NVIDIA's official benchmarks show the H200 achieving about 1.9x faster inference on Llama 2 70B compared to H100. The gains come from reduced memory bottlenecks when handling large models, not from additional compute.

Performance: Where H200 Pulls Ahead

The H100 and H200 have identical Tensor Core specs. Both deliver the same FP8, FP16, TF32, and FP64 TFLOPS. The performance difference shows up in memory-bound workloads.

H200 is faster when:

• Your workload is bottlenecked on memory bandwidth
• You're running models with large KV caches (long context inference)
• Batch sizes are large enough that memory transfer dominates
• Model weights plus activations push against H100's 80GB limit

Performance is similar when:

• Your workload is compute-bound (matrix multiplications dominate)
• Models fit comfortably in 80GB with room to spare
• Training is limited by compute rather than memory access

For models that fit within 80GB, expect roughly equivalent performance from both GPUs. The H200's advantage materializes when memory capacity or bandwidth becomes the constraint.

When H100 Makes Sense

H100 is the practical choice for most production AI workloads:

Model size fits in 80GB. With FP8/INT8 quantization, 70B-class weights often fit in 80GB, though real serving headroom depends on context length, KV cache, and batch size. If you're not hitting memory limits, H200's extra capacity doesn't help.

Cost matters. H100 costs less to rent and has broader availability. If your workload runs efficiently on H100, the savings add up.

You're doing compute-heavy training. For training jobs where compute dominates over memory access, H100 delivers the same Tensor Core performance as H200.

Latency-sensitive inference. H100 handles real-time inference for models that fit in memory without issues.

When H200 Makes Sense

H200 is worth the premium in specific scenarios:

Memory is genuinely your bottleneck. If you're constantly hitting 80GB limits, splitting models across GPUs, or constrained on batch size, H200's 141GB changes the equation.

Long context inference. KV cache memory scales with context length. For 100K+ token contexts, the extra memory matters.

Reducing multi-GPU complexity. A single H200 can handle workloads that would require two H100s, simplifying your infrastructure and eliminating communication overhead.

Large batch inference. More memory means more concurrent requests. If throughput is limited by memory, H200 helps.

Note on large models: A 70B parameter model in FP16 needs roughly 140GB just for weights (70B × 2 bytes). That's before runtime overhead and KV cache. So even H200's 141GB is tight for FP16 70B inference. In practice, quantization (FP8/INT8/4-bit) or shorter contexts are typically needed for comfortable single-GPU serving of models at this scale.

Power and Cooling

NVIDIA lists the same max TDP (up to 700W) for both H100 SXM and H200 SXM.

The NVL variants differ: H100 NVL runs at 350-400W while H200 NVL goes up to 600W. If you're planning self-hosted deployments, verify the specific SKU's power requirements.

For cloud deployments, power and cooling are handled by the provider.

Pricing and Availability

Check our pricing page for current rates on H100 and H200 instances. Pricing changes as availability shifts, so the pricing page has the most accurate numbers.

Both H100 and H200 are widely available across major cloud providers as of 2026. H200 typically commands a premium, but availability has improved significantly since its initial launch.

For teams evaluating both, starting with H100 makes sense for most workloads. The software ecosystem is identical since both use Hopper architecture, so migration to H200 is straightforward if you later need the additional memory.

What Comes After H200?

NVIDIA's Blackwell architecture (B100, B200, GB200) is the next generation after Hopper. Blackwell brings architectural improvements beyond just memory upgrades.

That said, H100 and H200 will remain solid choices for years given their mature software ecosystem and broad deployment base. The Hopper architecture is well-optimized across frameworks and continues to receive driver and library updates.

FAQ

What's the main difference between H100 and H200?

Memory. H200 has 141GB HBM3e versus H100's 80GB HBM3, with 43% higher bandwidth (4.8 TB/s vs 3.35 TB/s). Both share the same Hopper architecture and identical compute specs. Choose H200 when memory capacity or bandwidth is your bottleneck.

Is H200 faster than H100?

For memory-bound workloads, yes. NVIDIA reports about 1.9x faster inference on Llama 2 70B. For compute-bound workloads, performance is similar since both have identical Tensor Cores. The H200 is not a new architecture, just an H100 with more memory.

Can I run the same models on both GPUs?

Yes. Both support the same CUDA ecosystem, frameworks, and libraries. Code runs identically on both. The difference is memory capacity and bandwidth, not compatibility.

Which GPU is better for LLM inference?

Depends on model size. For models up to 70B with quantization, H100 handles inference well at lower cost. For larger models, full-precision serving, or very long contexts where KV cache is large, H200's extra memory helps. Most production deployments work fine on H100.

Does H200 use more power than H100?

No, for SXM variants. Both H100 SXM and H200 SXM have the same 700W power envelope. NVIDIA says H200 operates within the same power profile as H100. NVL variants differ (H100 NVL: 350-400W, H200 NVL: up to 600W).

Should I wait for H200 or use H100 now?

Use H100 now if it meets your needs. H100 handles the vast majority of AI workloads effectively and costs less. Move to H200 when you have specific memory requirements that H100 can't meet. Waiting for perfect hardware often delays projects unnecessarily.