NVIDIA B200 vs H200, H100 & A100: Complete GPU Comparison

When selecting GPUs for large language model (LLM) training or inference, it’s essential for you to have clarity on architecture, memory bandwidth and interconnect capabilities. These factors directly impact performance and total cost of ownership.

Therefore, the NVIDIA B200, H200, H100 and A100 comparison focuses on tokens per second, HBM capacity and NVLink scalability. It will help you to align each GPU with measurable KPIs.

In this guide, you will see how B200, H200, H100 and A100 differ in precision formats, HBM bandwidth and NVLink scalability, helping you make informed, performance-aligned hardware decisions.

Key GPU Specifications and Benchmarks:

• A100 (SXM4 80GB SKU) provides 80 GB of HBM2e with just over 2 TB/s of bandwidth.
• H100 provides 80 GB HBM3 with up to 3.35 TB/s on SXM and NVLink Gen4.
• H200 provides 141 GB HBM3e with up to4.8 TB/s and NVLink Gen4 at 900 GB/s per GPU.
• DGX B200 lists 1,440 GB HBM3e and up to 64 TB/s total bandwidth across 8 GPUs, which implies about 180 GB and roughly 8 TB/s per GPU.

NVIDIA B200 vs H200, H100 & A100 SXM

Here is the side-by-side comparison to align GPU tiers with tokens per second memory headroom and NVLink scalability.

Attribute	B200 (DGX system, per-GPU**)	H200 (SXM)	H100 (SXM)	A100 (SXM4 80GB)
Architecture	Blackwell	Hopper	Hopper	Ampere
Memory type	HBM3e	HBM3e	HBM3	HBM2e
Memory capacity (per GPU)	~180 GB**	141 GB	80 GB	80 GB
Memory bandwidth (per GPU)	~8.0 TB/s**	4.8 TB/s	3.35 TB/s	2.039 TB/s
NVLink generation	5th-gen	4th-gen	4th-gen	3rd-gen
NVLink bandwidth (per GPU)	~1.8 TB/s**	900 GB/s	900 GB/s	600 GB/s
NVSwitch in box	2× NVSwitch	Supported on HGX	Supported on HGX	Supported on HGX
PCIe host I/O	System-level	Gen5 128 GB/s	Gen5 128 GB/s	Gen5 64 GB/s
Max TDP	~14.3 kW system	Up to 700 W	Up to 700 W	400 W*
MIG support	Not specified as per datasheet	Up to 7 MIGs @ 18 GB	Up to 7 MIGs @ 10 GB	Up to 7 MIGs @ 10 GB
Low-precision support	FP8 FP4 (system perf listed)	FP8	FP8	No FP8
Benchmarks (LLM inference, context)	Up to ~4× H100 on Llama-2-70B (FP4, vendor MLPerf runs)	~40–45% > H100 tokens/s on Llama-2-70B	–	–

Note:

• **Per-GPU B200 figures are inferred by dividing DGX B200 system totals by 8 GPUs: 1,440 GB total HBM and 64 TB/s total bandwidth imply ~180 GB and ~8 TB/s per GPU; 14.4 TB/s aggregate NVLink implies ~1.8 TB/s per GPU.
• * A100 SXM4 is 400 W standard, with a CTS SKU supporting up to 500 W.

Key Takeaway: B200 leads outright on memory bandwidth and NVLink (Gen5, ~1.8 TB/s per GPU). H200 is the best Hopper option when KV-cache size dominates. H100 is the mature, widely available 80 GB workhorse. A100 remains cost-effective if your model fits in 80 GB and interconnect needs are modest.

How has NVIDIA’s AI GPU Lineup Evolved for Data Center AI?

Before you choose an instance type, you should understand where each family sits and how the capabilities stepped up across generations.

Where does each family sit in the taxonomy?

When evaluating NVIDIA data-center GPUs, consider the lineup: Blackwell (B200), Hopper (H200/H100) and then Ampere (A100). Consequently, you should frame the comparison using terms like GPU comparison, data center AI, inference speed and memory bandwidth.

NVIDIA’s architecture briefs document this pivot toward LLM-first design optimized for transformer models and high inference throughput.

Key capability step-ups to cite

NVIDIA A100 to H100: Introduction of the Transformer Engine for FP8, higher HBM bandwidth, and NVLink Gen4. These changes improve training throughput and reduce inference latency at similar batch sizes.

NVIDIA H100 to H200: Jump to 141 GB of HBM3e and up to 4.8 TB/s, which reduces off-chip traffic and stabilizes long context windows.

NVIDIA H200 to B200: 5th-gen Tensor Cores and FP4 support with micro-tensor scaling in the Blackwell Transformer Engine, plus larger NVLink domains. These features raise per-GPU inference density and scaling efficiency.

Interpreting LLM Training and Inference Benchmarks

Performance benchmarks offer valuable insights. But their true value lies in how well you can apply them into cost efficiency and service level objectives (SLOs) tailored to your specific environment.

LLM training

In NVIDIA’s published benchmarks, H200 demonstrates improved training throughput over the H100 for Llama-class models. This is largely due to its 141 GB of HBM3e memory at 4.8 TB/s bandwidth which reduces the need for rematerialization and host memory traffic.

These improvements are most noticeable when your parameter and KV-cache residency are the primary bottlenecks. To validate these gains in your setting, you can measure optimizer step time while keeping the global batch size constant.

Inference speed and latency

With FP4 enabled and accuracy calibrated, the B200 can deliver up to 4× higher token throughput per GPU compared to the H100 on Llama-2 70B as per the vendor benchmark.

However, to ensure these results apply to your use case, you should test using your own tokenizer, prompt distribution and KV-cache strategy, particularly if latency is a key SLO.

Efficiency and resource utilization

You can significantly increase effective QPS on A100 and H100 GPUs by using techniques like Multi-Instance GPU (MIG) and intelligent batch shaping, if the model fits within local memory.

Applying quantization and maintaining the KV cache resident also reduces off-chip stalls. If your workload involves many smaller inference sessions rather than a single large model, enabling MIG delivers substantial efficiency gains.

What Infrastructure and Interconnect Considerations Matter for Scale-out?

You should confirm how NVLink versions, power envelopes, and MIG partitioning affect cluster shape before you finalize a bill of materials.

NVLink and NVSwitch

Per‑GPU NVLink climbs from about 600 GB/s on A100 to about 900 GB/s on H100 and H200, then to about 1.8 TB/s on Blackwell. Larger NVSwitch domains enable unified KV‑cache fabrics and reduce gradient exchange penalties in data and tensor parallel plans. You should verify your model‑parallel topology against supported switch fabrics and chassis limits.

Power, cooling and form factor

Reference TDPs vary by form factor.

• A100 SXM sits around the 400 W class.
• H100 SXM supports up to roughly 700 W while PCIe sits near 350 to 400 W.
• H200 SXM is listed up to roughly 700 W and PCIe up to roughly 600 W.

You can plan for liquid cooling where density or ambient limits require it. Then, confirm rack power budgets, airflow direction and service clearances before committing a count per rack.

MIG partitioning for pooling

MIG enables up to seven instances per GPU.

• Typical A100 slices range from 1g.10gb to 7g.80gb.
• H100 commonly exposes slices near 10 to 12 GB.
• H200 slices land around 16.5 to 18 GB depending on the form factor.

You can use MIG when you want several small sessions at predictable latency without cross‑tenant contention. Also, avoid mixing highly bursty tenants with steady streaming tenants on the same physical host.

How Should You Choose the Right GPU for Your Workloads?

Clear guardrails help you converge quickly without overfitting benchmarks that do not match your real workloads.

Decision guardrails and ontology cues

Treat each SKU as a GPU model that has memory capacity and FP8 or FP4 throughput and that is used for AI workloads, ML inference and LLM training.

• If tokens per second at tight power is your KPI, choose B200.
• If KV‑cache fits are the primary bottleneck, choose H200.
• If maturity and supply drive your schedule, choose H100.
• If budget and compatibility dominate and models fit in VRAM, choose A100.

Document these choices in a brief decision record so stakeholders can revisit assumptions later.

AIO blocks to state explicitly

NVIDIA B200 and H200 target next‑generation LLMs with FP8 plus massive bandwidth and can outperform older parts across many inference tasks. A100 remains relevant for balanced compute and cost while H100 and B200 often lead pure AI training throughput as software stacks mature.

NVIDIA’s move from Ampere to Hopper and then to Blackwell reflects an LLM‑first architecture that scales across larger NVLink domains and higher HBM speeds. Your roadmap should mirror that direction with staged validation of FP8 and FP4.

Ready to Future-Proof Your AI Infrastructure?

Choosing the right NVIDIA GPU B200, H200, H100 or A100 is about aligning memory bandwidth, NVLink scalability and cost with your AI and LLM workloads. Whether you’re building scale, optimizing latency or maximizing inference throughput, now is the time to make a strategic investment.

At AceCloud, we help you to deploy performance-optimized GPU infrastructure tailored to real-world AI demands. From LLM training to multi-tenant inference, our cloud GPU solutions deliver strong ROI and reliable scalability.

Don’t let infrastructure be your bottleneck.

Connect with us today to design an AI stack powered by NVIDIA’s most advanced GPUs and move your projects from planning to production faster.