NVIDIA H100 vs A100: Specs, Performance and Which GPU Server to Rent

For most teams, the right GPU is not the fastest one on paper but the one that clears their workload at the lowest cost per useful result. When planning your infrastructure for such deployments, reliable AI Hosting plays a critical role. The NVIDIA H100 is unquestionably the more capable accelerator, yet the A100 remains a remarkably cost-efficient workhorse for inference, fine-tuning and classic HPC. This guide frames the H100 vs A100 decision the way infrastructure buyers actually face it: architecture, measured performance, power and rack constraints, total cost, and fit by workload. It closes with where the H200 and Blackwell generations change the picture.

Related ReadHow to Set Up a Machine Learning Server: The Complete Checklist

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H100 vs A100 at a Glance

On paper, the H100 SXM delivers roughly three times the dense BF16 throughput of the A100 SXM and adds native FP8, which the A100 lacks entirely. Figures below are NVIDIA’s published peak values (approximate; dense, without sparsity).

Specification	NVIDIA A100 (Ampere)	NVIDIA H100 (Hopper)
Launch	2020	2022
Process / transistors	TSMC 7N / 54.2B	TSMC 4N / 80B
Memory	40 GB HBM2 or 80 GB HBM2e	80 GB HBM3 (SXM) / 80 GB HBM2e (PCIe) / 94 GB (NVL)
Memory bandwidth	1.6 TB/s (40 GB) to 2.0 TB/s (80 GB)	2.0 TB/s (PCIe) to 3.35 TB/s (SXM)
BF16 / FP16 Tensor	~312 TFLOPS	~756 (PCIe) to ~989 TFLOPS (SXM)
FP8 Tensor	Not supported	~1,513 (PCIe) to ~1,979 TFLOPS (SXM)
TF32 Tensor	~156 TFLOPS	~378 to ~495 TFLOPS
FP64 Tensor	~19.5 TFLOPS	~51 to ~67 TFLOPS
GPU interconnect	NVLink 3, 600 GB/s	NVLink 4, 900 GB/s
Host interface	PCIe Gen4	PCIe Gen5
Max power (TDP)	250 to 400 W	350 W (PCIe) to 700 W (SXM)
Multi-Instance GPU	Up to 7 instances	Up to 7 instances (2nd gen, with confidential computing)

Form factor matters as much as the chip name. A PCIe H100 sits well below its SXM sibling, so always confirm the variant before comparing quotes.

Architecture Differences

Hopper is not a die-shrink of Ampere; it was designed around the transformer. That design intent explains most of the real-world gap.

Hopper vs Ampere

• Fourth-generation Tensor Cores: Hopper roughly doubles per-SM matrix throughput at equal clock versus Ampere’s third-generation cores.
• Thread Block Clusters and Distributed Shared Memory: groups of SMs can share data directly, cutting round trips to global memory in fused kernels.
• Tensor Memory Accelerator (TMA): offloads bulk data movement from threads, which is why FlashAttention-3 and similar kernels extract far more from Hopper.
• DPX instructions: accelerate dynamic programming workloads such as genomics alignment and route optimisation.
• Security: Hopper adds hardware-based confidential computing, relevant for fintech, iGaming and other regulated tenants processing sensitive data in shared environments.

Ampere’s advantage is maturity. Six years of driver, framework and kernel optimisation make the A100 a low-risk target for almost any CUDA codebase.

Transformer Engine and FP8

The Transformer Engine is Hopper’s defining feature. It dynamically chooses between FP8 and 16-bit precision per layer, applying scaling factors to preserve accuracy.

• FP8 halves memory per value versus BF16, so larger batches and longer contexts fit in the same HBM.
• Throughput roughly doubles again over Hopper’s own BF16 peak on supported operations.
• Software support is required: gains materialise only with frameworks that use it, such as NVIDIA Transformer Engine, Megatron-LM, TensorRT-LLM, vLLM or SGLang FP8 paths.

The A100 has no FP8 hardware. It relies on BF16, TF32 and INT8, which remain perfectly adequate for many inference and fine-tuning jobs.

Memory and Bandwidth

Large-model inference is usually memory-bandwidth bound, not compute bound. Token generation speed tracks how fast weights stream from HBM.

• A100 40 GB: about 1.6 TB/s, enough for 7B to 13B models in FP16, or larger ones with 4-bit or 8-bit quantisation.
• A100 80 GB: about 2.0 TB/s, the practical floor for 30B-class models without heavy quantisation.
• H100 SXM 80 GB: about 3.35 TB/s of HBM3, roughly 1.7 times the A100 80 GB, which translates almost directly into faster decoding.
• Interconnect: NVLink 4 at 900 GB/s and PCIe Gen5 reduce the penalty of tensor parallelism across GPUs.

Performance Compared

In practice, the H100 SXM typically runs transformer workloads 2 to 3 times faster than the A100, and more when FP8 is fully exploited. NVIDIA’s headline claims (up to 9x training and up to 30x inference) refer to large multi-node clusters and specific models, so treat them as ceilings rather than planning figures.

LLM Training

When evaluating H100 vs A100 for LLM training, the gap widens with model size and cluster scale.

• Single GPU, BF16: expect roughly 2 to 2.5 times the A100’s throughput on dense transformer pre-training.
• FP8 with Transformer Engine: a further 1.3 to 1.6 times on top, depending on how much of the model runs in FP8.
• Multi-node: NVLink 4 and faster InfiniBand or Ethernet fabrics keep scaling efficiency higher, which compounds over long runs.
• Fine-tuning: for LoRA or QLoRA on 7B to 13B models, a single A100 finishes most jobs in hours, so the H100’s advantage rarely justifies its premium.

The practical rule: pre-training and full fine-tuning of 30B+ models favour Hopper; parameter-efficient fine-tuning and experimentation sit comfortably on Ampere.

Inference Throughput

Inference performance depends on batch size, sequence length and the serving stack as much as on silicon.

• Latency-sensitive, small batch: decoding is bandwidth-bound, so the H100 SXM is typically 1.5 to 2 times faster per token.
• High-concurrency serving: with FP8 weights and KV cache in TensorRT-LLM or vLLM, throughput gains of 3 to 4 times are commonly reported.
• Small and mid-size models: an A100 40 GB serves 7B to 13B models at production-grade latency, often at a lower cost per million tokens.
• Non-LLM inference: computer vision, recommendation and speech models rarely saturate either GPU, which strengthens the case for the A100.

Benchmark your own model and serving framework before committing. Published numbers vary widely with configuration.

Power, Cooling and Rack Requirements

An 8-GPU H100 SXM node draws up to about 10.2 kW, versus roughly 6.5 kW for an 8-GPU A100 system. That difference often decides where, and whether, you can host the hardware yourself.

Configuration	Typical max draw	Facility implication
1x A100 PCIe server	~0.8 to 1.2 kW	Fits standard air-cooled racks
1x H100 PCIe server	~1.0 to 1.5 kW	Standard racks, higher airflow needed
8x A100 SXM (HGX/DGX)	~6.5 kW	15 to 20 kW racks, strong air cooling
8x H100 SXM (HGX/DGX)	~10.2 kW	20 to 40+ kW racks, rear-door or liquid cooling

Operational points to plan for:

• Rack density: two HGX H100 nodes can exceed the power budget of many legacy colocation racks.
• Cooling: sustained 700 W per GPU pushes facilities toward rear-door heat exchangers or direct-to-chip liquid cooling.
• Power redundancy: use dual feeds (A+B) and correctly rated PDUs; GPU nodes spike under load.
• Monitoring: track GPU temperature, power and throttling through NVIDIA DCGM, exported to Prometheus and Grafana, with alerting on thermal events.

A single-GPU A100 server avoids almost all of these constraints, which is a significant part of its operational appeal.

Cost: Rent vs Buy vs Colocate

The A100 vs H100 price gap is larger than the performance gap for many workloads. Market figures move quickly, so treat the ranges below as approximate and request current quotes.

Model	Upfront cost	Ongoing cost	Best suited to
Buy and host on-premises	High: an H100 GPU alone has typically cost several times an A100	Power, cooling, staff, spares, refresh cycle	Steady, multi-year utilisation above 70%
Buy and colocate	High hardware capex	Rack space, power (per kW), remote hands	Owned hardware without building a data centre
Rent a dedicated GPU server	None	Fixed monthly fee	Predictable production workloads, fast start
Hourly cloud instances	None	Per-hour billing, egress, storage	Short bursts and experiments

For most B2B teams, renting dedicated hardware hits the best balance: no capital outlay, no facility risk, and costs that are simple to forecast. An H100 GPU server makes sense when FP8 throughput directly shortens time to market; otherwise, an A100 dedicated server usually delivers a lower cost per completed job.

Featured: HostingB2B NVIDIA A100 GPU Server

€1,354.00 per month, equivalent to roughly €1.85 per hour of continuous, single-tenant use.

• Location: United Kingdom data centre
• GPU: 1x NVIDIA A100 40 GB
• Processors: 2x Intel Xeon 5318Y (24C/48T each, up to 3.40 GHz)
• Memory: 256 GB system RAM
• Storage: 2x 480 GB M.2 (OS) + 2x 3.84 TB SAS SSD (data)
• RAID: RAID 1 + RAID 1 (software)
• OS: Ubuntu, Proxmox or custom image

Why this configuration works well in production:

• No noisy neighbours: the full GPU, CPUs and SSD storage are dedicated to you.
• Balanced host: 48 physical cores and 256 GB RAM keep data loading, tokenisation and pre-processing from starving the GPU.
• Data resilience: mirrored OS and data volumes protect against single-drive failure; pair them with off-server backups for full protection.
• Flexible stack: run bare-metal Ubuntu with CUDA and Docker, or Proxmox with GPU passthrough to isolate environments.
• UK hosting: helpful for teams that prefer UK data residency as part of their GDPR approach (general guidance, not legal advice).

Recommended practice: combine the server with scheduled backups, DCGM-based monitoring, key-only SSH with MFA on management access, and a documented incident response runbook.

Which GPU to Choose by Workload

The simplest way to settle the H100 vs A100 question is to start from the workload, not the spec sheet.

Workload	Recommended GPU	Rationale
Pre-training LLMs (30B+ parameters)	H100 (SXM, multi-GPU)	FP8, NVLink 4 and bandwidth shorten runs materially
Full fine-tuning of 30B to 70B models	H100	Memory bandwidth and FP8 reduce wall-clock time
LoRA / QLoRA fine-tuning up to 13B	A100	Ample capacity at a far lower monthly cost
LLM inference, 7B to 13B models	A100	Production latency with better cost per token
High-concurrency inference, 70B models	H100 or H200	Bandwidth and FP8 KV cache drive throughput
RAG pipelines and embeddings	A100	Mixed CPU/GPU load suits a balanced host
Computer vision, speech, recommendation	A100	Rarely saturates either GPU
Scientific HPC (FP64)	H100 for peak, A100 for value	Hopper roughly triples FP64 Tensor throughput
Dev, test and CI for ML teams	A100	Stable, mature and easy to partition with MIG

A practical pattern for growing teams:

1. Prototype, fine-tune and serve on an A100 dedicated server.
2. Measure GPU utilisation, tokens per second and cost per job as KPIs.
3. Move only the bottlenecked stages, typically large-scale training, to an H100 GPU server or cluster.
4. Automate the pipeline with containers, infrastructure as code (Terraform, Ansible) and CI/CD so workloads remain portable between GPU generations.

What About H200 and Blackwell?

Newer generations raise the ceiling, but they do not make the A100 or H100 obsolete for most production workloads.

H100 vs H200: the H200 uses the same Hopper compute die but upgrades memory to 141 GB of HBM3e at about 4.8 TB/s, versus 80 GB at 3.35 TB/s on the H100 SXM. Compute peaks are essentially identical, so the gain shows up in memory-bound inference: larger models per GPU, longer context windows and higher batch sizes.

Blackwell (B200, B300, GB200): NVIDIA’s next architecture adds second-generation Transformer Engine support for FP4 and FP6, up to roughly 180 to 288 GB of HBM3e per GPU depending on model, and fifth-generation NVLink. Rack-scale systems such as GB200 NVL72 are typically liquid cooled and draw well over 100 kW per rack.

What this means for buyers:

• Availability and lead times for Blackwell remain tighter, and facility requirements are far more demanding.
• Prices of earlier generations soften as new ones ship, improving the economics of A100 and H100 rentals.
• Software portability is high across generations, so starting on an A100 does not lock you in.

FAQ

Conclusion: Rent the GPU Your Workload Actually Needs

The H100 vs A100 choice comes down to where your bottleneck lies. If FP8 training at scale defines your roadmap, Hopper earns its premium. If you need dependable inference, fine-tuning and ML operations at a predictable monthly cost, Ampere remains one of the most efficient options available.

HostingB2B’s A100 dedicated server gives you a single-tenant NVIDIA A100 40 GB, 48 Xeon cores, 256 GB RAM and mirrored SSD storage in the UK for €1,354.00 per month, with no hardware capex or facility risk.

Next step: contact the HostingB2B team to discuss your workload, request a configuration review, or deploy an A100 GPU server today.

Specifications are based on NVIDIA’s published figures and are approximate. Real-world performance depends on model, framework and configuration; validate with your own benchmarks.