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Private AI glossary · updated 2026-10-16

TFLOPS
A peak-speed rating for arithmetic, and why two vendors’ figures rarely compare.

Short answer. TFLOPS means trillions of floating-point operations per second, where tera stands for 10 to the power 12. It is a peak-speed rating for a processor’s arithmetic, quoted for one number format. NVIDIA lists 40 TFLOPS of single-precision performance for the RTX PRO 4000 Blackwell, a peak figure at boost clock, while AMD lists up to 60 TFLOPS of FP16 AI compute for the Ryzen AI Max+ 395. The two use different precisions, so they cannot be compared directly. For an office, TFLOPS mostly affect how fast long prompts are processed; the speed of writing each word depends more on memory bandwidth.

01

What the number counts

A floating-point operation is one addition or multiplication on a number that has a decimal part. Tera is the SI prefix for 10 to the power 12, so 40 TFLOPS is 40 trillion such operations in a second.

Vendors quote peaks. NVIDIA’s footnotes say its figures are peak rates at the GPU boost clock, and that its 1,290 TOPS AI figure is a theoretical FP4 number that assumes sparsity. A figure like that is a ceiling under ideal conditions, not what a job will see.

02

Same card, different numbers

The table lists the figures NVIDIA and AMD publish. Note that the 1,290 figure is more than 30 times the 40 TFLOPS figure for the same card, because it counts four-bit numbers and assumes sparsity. Neither is wrong; they answer different questions.

FigureValueWhat it measures
Single-precision performance, RTX PRO 4000 Blackwell40 TFLOPS32-bit floating point, peak at boost clock (NVIDIA)
RT core performance, RTX PRO 4000 Blackwell122 TFLOPSRay-tracing hardware used for graphics (NVIDIA)
AI performance, RTX PRO 4000 Blackwell1,290 TOPSTheoretical FP4 with sparsity (NVIDIA)
FP16 AI compute, Ryzen AI Max+ 395Up to 60 TFLOPS16-bit floating point (AMD)
NPU, Ryzen AI Max+ 395Up to 50 TOPSThe NPU block alone (AMD)
Overall AI, Ryzen AI Max+ 395Up to 126 TOPSAMD’s overall figure for the chip
03

Where TFLOPS matter in AI work

NVIDIA describes two phases in a language-model answer. Reading the prompt (prefill) is arithmetic-heavy and keeps a GPU busy, so TFLOPS help there: a long tender document or a batch of pages is processed sooner. Writing the answer (decode) is memory-bound, so bandwidth sets the pace instead.

For that reason a card with high TFLOPS and modest memory bandwidth can feel quick at starting and slow at finishing.

04

How to compare fairly

  • Compare the same number format: FP32 with FP32, FP16 with FP16, INT8 with INT8.
  • Check whether the figure assumes sparsity, and whether it is a peak or a sustained rate.
  • Read the footnote: it names the format, the clock and the conditions.
  • Do not compare a CPU’s or NPU’s TOPS with a GPU’s TFLOPS.
  • Look at power as well: a rating at 145 W and one at 600 W are different machines.
  • Prefer measured results on your own task over any peak rating.
05

Where ANVE stands

ANVE has published no speed figures for its launch units. Treat any peak rating, ANVE’s or anyone else’s, as a ceiling and ask for a measured result.

06

Questions people ask

How many TFLOPS do I need for local AI?
There is no fixed number. Memory capacity decides which models fit and memory bandwidth decides how fast answers appear. TFLOPS matter for long prompts and batches.
Is TOPS the same as TFLOPS?
Both count operations per second. TFLOPS counts floating-point operations, while TOPS usually counts integer operations, often 8-bit, or another format named in a footnote. Our NPU and TOPS page explains the difference.
What is FP32?
A 32-bit floating-point number, also called single precision. FP16 and BF16 use 16 bits.
Why is a card with high TFLOPS slow at answering?
Because writing an answer is limited by memory bandwidth, not arithmetic. NVIDIA calls the decode phase memory-bound.
07

What is not real yet

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.