Beste Cloud GPU's met meer dan 300 FP16 TFLOPS — June 2026

What a 300 FP16 TFLOPS floor actually filters for

Setting a minimum of 300 FP16 TFLOPS is a deliberate way to screen out everything but serious accelerator-class hardware. FP16 (half precision) throughput is the headline number for AI math, and on modern data-center GPUs that figure is delivered by dedicated tensor cores (NVIDIA) or matrix engines (AMD), not the general shader pipeline. A 300-teraFLOP-per-second threshold is high enough that consumer cards and older accelerators fall away, leaving the cards that were designed from the ground up for transformer-scale training and high-throughput inference.

One subtlety worth understanding before you read the comparison above: vendors quote FP16 numbers in two very different ways. The lower figure is dense FP16 with FP32 accumulation; the much larger figure is the structured-sparsity rating, which roughly doubles the dense number and only applies when your model is pruned to a 2:4 sparsity pattern. A 300 TFLOPS floor lands squarely in dense territory for current flagship cards, which is the conservative and honest way to read it. When you compare entries, check whether a listed TFLOPS value is dense or sparse so you are not comparing a sparse peak against a dense one.

What hardware clears this bar

GPUs that comfortably exceed 300 dense FP16 TFLOPS share a recognizable profile. They are not defined by clock speed but by tensor throughput, memory, and interconnect working together:

• HBM memory, not GDDR: cards in this class almost always carry high-bandwidth memory (HBM2e or HBM3-class stacks), with capacities typically ranging from tens of gigabytes up to roughly 80GB+ per GPU. This matters because at 300+ TFLOPS the compute can outrun the memory; large VRAM and very high bandwidth keep the tensor cores fed.
• Multiple precision modes: alongside FP16 you usually get BF16 (better numerical range for training), and on newer generations FP8, plus INT8 for inference. The FP16 rating is the floor you are filtering on, but the same silicon often unlocks even higher FP8 throughput.
• Fast interconnect: NVLink, NVSwitch, or Infinity Fabric on the high end; PCIe on more cost-sensitive variants. Above 300 TFLOPS per GPU, the bottleneck in multi-GPU jobs shifts to how quickly cards can exchange gradients, so interconnect becomes as important as the raw FLOPS.
• High power and thermal class: these are 300W-to-700W-class parts that need data-center cooling. That is part of why you rent them rather than run them at a desk.

Crucially, raw FP16 throughput is necessary but not sufficient. A card can hit the TFLOPS target yet still bottleneck on memory capacity for a large model, or on bandwidth for memory-bound inference. Read the FLOPS figure in the comparison alongside VRAM and bandwidth, not in isolation.

Workloads that justify this threshold

• Large-model and multi-GPU training: pre-training or full fine-tuning of multi-billion-parameter models, where every extra TFLOPS shortens wall-clock time and every dollar of rental is amortized over a long run.
• High-throughput batch inference: serving many requests per second, where you batch aggressively to keep the tensor cores saturated and the 300+ TFLOPS ceiling translates directly into tokens or images per second.
• Heavy fine-tuning and LoRA at scale, mixed-precision scientific computing, and large-batch diffusion or rendering pipelines that are genuinely compute-bound.

When 300+ TFLOPS is overkill

If your job is small or latency-sensitive rather than throughput-bound, this tier can be wasteful. Single-stream, low-batch real-time inference often cannot keep that much compute busy, so you pay for FLOPS you never use. Light fine-tuning of small models, prototyping, notebook experimentation, and most inference of compact models run perfectly well on cheaper cards below this floor. The 300 TFLOPS filter is for when you are confident the workload can actually consume the throughput.

Rental economics at this performance level

Cards that clear 300 FP16 TFLOPS sit toward the premium end of the rental spectrum, and the price reflects scarcity as much as silicon. Because the same hardware is in demand for frontier AI work, on-demand availability fluctuates and the newest generations are the most contended. A few things shape what you actually pay above this bar:

• On-demand vs spot/interruptible: spot or preemptible capacity can dramatically cut the effective rate on these cards, which suits checkpointable training and batch inference but is risky for long uninterrupted runs.
• Per-second vs per-hour billing: at premium hourly rates, fine billing granularity meaningfully changes the cost of short, bursty jobs.
• Configuration: a PCIe variant and an NVLink variant of the same class can clear the same FP16 floor yet rent at different prices and scale very differently across multiple GPUs.
• Region and generation: older accelerators that still pass 300 TFLOPS are usually cheaper and more available than the latest flagships at identical throughput.

Because these figures move constantly, treat the comparison above as the source of truth for live pricing and availability. Use the 300 TFLOPS filter to guarantee a performance floor, then sort within that set by VRAM, interconnect, billing model, and current rate.

Frequently asked questions

Does 300 FP16 TFLOPS refer to dense or sparse performance?

Treat it as dense FP16 with FP32 accumulation unless a listing states otherwise. Sparse (structured 2:4) ratings are roughly double and only apply to pruned models, so a card advertised with a high sparse figure may deliver materially less in dense FP16. Always confirm which number a provider is quoting.

Is a GPU above 300 TFLOPS automatically the right choice?

Not necessarily. High FP16 throughput only helps if your workload can keep the tensor cores busy, typically large-batch or multi-GPU training and high-throughput inference. For small models or single-stream real-time inference, a card below this floor is often faster per dollar because you would otherwise pay for idle compute.

Why do two cards at the same FP16 TFLOPS rent for different prices?

FP16 throughput is only one axis. Memory capacity and bandwidth, interconnect (NVLink versus PCIe), generation, power draw, and on-demand scarcity all move the rate. A newer card and an older one can hit the same FLOPS yet differ sharply on VRAM, scaling efficiency, and availability, which is reflected in the live prices above.

Should I use spot instances for hardware in this tier?

Spot or interruptible capacity is well suited to this class when your work is checkpointable, such as training runs that can resume or batch inference you can retry, because the premium on-demand rates make the discount substantial. For uninterruptible jobs with hard deadlines, on-demand is safer despite the higher cost.