NVIDIA H100 PCIe – xpu live

Provider / plan	Price / GPU-hour	Rental type	Terms & configuration	Last checked	Provider pricing link
RunpodSecure Cloud Pods	$2.89Lowest flexible rate	On-demandNo reservation	1 GPU minimumSee provider View details Secure Cloud Pods published GPU-hour rate. CPU/RAM allocation varies; storage/networking extra. Not live capacity. Published rate $2.89 / GPU-hour CPU / vCPU Confirm with provider System RAM Confirm with provider Storage Confirm with provider Network / egress Confirm with provider GPU allocation Confirm with provider GPU topology Confirm with provider	2026-10-03Auto-collected	View pricing ↗

H100 PCIe brings Hopper acceleration to compatible PCIe servers with 80 GB of HBM2e. It is a different configuration from H100 SXM, with a 350 W maximum board power and lower memory bandwidth. Choose it using the exact rental configuration, not the H100 name alone.

CHOOSING THIS GPU

Is it right for your workload?

80 GB HBM2e PCIe card, GH100-200 product brief. This is not the 94 GB H100 NVL. The cited brief contains differing NVLink descriptions; ask the provider for the installed link configuration.

Best suited to

Hopper-based inference in PCIe servers
Model fine-tuning within an 80 GB memory budget
Compute jobs that do not require an HGX NVSwitch fabric

Strengths

80 GB GPU memory
PCIe server form factor
350 W maximum board power in the reference brief

Things to consider

2 TB/s memory bandwidth, below the SXM edition
NVLink bridges may not be present in a rental
Do not apply SXM peak performance figures to this card

HARDWARE DETAILS

Inside the GPU

Manufacturer: NVIDIA
Architecture: Hopper
VRAM (GB): 80
Memory type: HBM2e
Form factor: PCIe
Memory bandwidth (GB/s): 2000
Maximum board power (W): 350
CUDA cores: 14592
Tensor cores: 456 / 4th generation
Ray tracing cores: Not verified for this edition
CUDA capability: 9.0
Memory ECC: Supported
Host interface: PCIe Gen5 x16
GPU interconnect: Two-card NVLink bridge support; confirm installed topology
Hardware partitioning (MIG): Up to 7 hardware instances; provider must enable it
Cooling: Passive; server airflow required

Hardware capabilities are not a guarantee of access in a cloud instance. Check the exact edition, GPU allocation and server topology in the rental offer.

Theoretical compute performance

Precision	Dense peak	Sparse peak
FP32	51 TFLOPS	Not applicable

FP32 peak is published in the linked NVIDIA technical article. Other precision-specific rates are not normalized here; do not substitute SXM specifications.

Unlisted rates are not verified here; that does not mean a precision is unsupported. TFLOPS measures floating-point operations, while TOPS measures integer operations in the INT8 rows.

MODEL MEMORY

Plan your model size

Estimate · not a benchmark

70B 8-bit weights alone are about 70 GB; a full runtime needs additional memory. Compare quantized single-GPU serving with supported multi-GPU execution. Full fine-tuning requires substantially more than inference weights.

Example model class	Weight format	Raw weights ≈	Minimum GPUs for weights only
Llama 3.1 8B ↗	4-bit	4 GB	1Runtime needs more memory
Llama 3.1 8B ↗	FP16	16 GB	1Runtime needs more memory
Qwen2.5 14B ↗	4-bit	7 GB	1Runtime needs more memory
Qwen2.5 32B ↗	4-bit	16 GB	1Runtime needs more memory
Llama 3.1 70B ↗	4-bit	35 GB	1Runtime needs more memory
Llama 3.1 70B ↗	8-bit	70 GB	1Runtime needs more memory
Llama 3.1 70B ↗	FP16	140 GB	2Runtime needs more memory

Arithmetic lower bound: rounded parameter count × bits per weight ÷ 8, in decimal GB. Excludes quantization metadata, KV cache, activations, CUDA workspaces and training states. Actual model sizes differ from their rounded names. No context length, batch size or concurrency is guaranteed. GPU counts assume supported model sharding and can be higher in practice. Quantized checkpoints and compatible kernels are required for 4-bit/8-bit execution.

SOFTWARE & PERFORMANCE

Before you deploy

Software compatibility

Use an NVIDIA CUDA-enabled framework/container compatible with this GPU and the host driver. Check PyTorch build and inference-engine kernel requirements before deployment. vLLM documents NVIDIA compute capability 7.5+ as a baseline; support for each model and quantization method still needs checking.

Measured benchmarks

A reproducible, comparable application benchmark has not yet been recorded for this edition. Peak TFLOPS above are not measured tokens per second or image-generation speed.

Compare tests using the same model, precision, GPU count, engine version, input/output length and batch or concurrency. A provider’s server configuration can change the result.

COMPARE YOUR OPTIONS

Alternatives to consider

NVIDIA H100 SXM ↗

80 GB · Hopper

Compare a higher-bandwidth SXM deployment.

NVIDIA A100 PCIe 80GB ↗

80 GB · Ampere

Compare an 80 GB Ampere PCIe alternative.

Technical sources & review date

Specifications reviewed 2026-09-29. Workload recommendations are editorial guidance based on the hardware capabilities. Model-memory figures are arithmetic estimates.

Reference hardware; rental provider cooling configuration may differ. Hardware image source ↗

BUDGET YOUR RUN

GPU cost estimator

GPU compute only: hourly rate × billable hours × GPU count. CPU, RAM, storage, egress, taxes, billing increments and minimum terms may add charges. Serverless hours mean active GPU task time; spot capacity can be interrupted. Reserved commitments and quote-only offers are excluded from this simple hourly estimator.

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