ClaimsJensen Huang

“3x Double Precision”

NVIDIA keynote slide (Jensen Huang, CEO) May 2012 GTC 2012 Jen-Hsun Huang Keynote (SlideShare)

Factual AI compute, hardware and data

The Tesla K20 offers 3x double precision performance (baseline not stated).

Mostly true True in substance, wrong in a detail.

Factual: Statements about the present or the past, checked against the record: did they hold up?

The second judge would not resolve this claim; the verdict shown is the first judge's.

The evidence is the person's or their organisation's own records.

Said for an organisation they lead or work for; it counts on both records.

What would count as true or false

True: NVIDIA's official Tesla K20 materials from around May 2012 claim 3x double-precision performance relative to the prior (Fermi) generation, and the published peak DP specifications support a ratio of about 3x (roughly 2.7x to 3.3x versus the comparable Fermi Tesla part). Mostly true: NVIDIA made the 3x claim, but the actual spec ratio is somewhat different (about 2.4x to 2.7x, or 3.3x to 3.6x), or the 3x applied to a closely related baseline. Mixed: the 3x figure applies only to a different metric, such as performance per watt, while raw DP gain is materially lower. Mostly false: the DP improvement was around 2x or less, and the 3x claim distorts a different figure. False: there was no such claim, or the DP performance did not improve substantially.

Set before the research began.

Evidence (4)

  1. E1 [PDF] nvidia® tesla® kepler - gpu computing accelerators NVIDIA · May 2012 Own record

    “Designed for double precision applications and the broader supercomputing market, the Tesla K20 delivers 3x the double precision performance compared to the previous generation Fermi-based Tesla M2090, in the same power envelope.”
  2. E2 [PDF] nvidia® tesla® kepler - gpu computing accelerators NVIDIA · May 2012 Own record

    “1Based on DGEMM performance: Tesla M2090 (Fermi) = 330 gigaflops, Tesla K20 (expected) > 1000 gigaflops”
  3. E3 About NVIDIA NVIDIA · 15 May 2012 Own record

    “Tesla K20にはGK110 Kepler GPUが採用されています。このGPUはFermiアーキテクチャーをベースとしたTesla製品に対して倍精度能力が3倍に強化されている”
  4. E4 [PDF] nvidia® tesla® kepler - gpu computing accelerators NVIDIA · May 2012 Own record

    “With more than one teraflop peak double precision performance, the Tesla K20 is ideal for a wide range of high performance computing workloads”

Reasoning

NVIDIA's May 2012 Tesla Kepler datasheet says the K20 delivers 3x the double-precision performance of the Fermi-based Tesla M2090 in the same power envelope (E1). The May 2012 Japanese press release makes the same 3x claim (E3). The datasheet's footnote bases this on DGEMM: 330 gigaflops for the M2090 against an expected figure above 1000 gigaflops for the K20, a ratio of about 3x (E2). That figure is a projection, and while the datasheet gives the K20's peak double precision as more than one teraflop (E4), the evidence never states the M2090's peak, so the peak-to-peak ratio the criteria ask for cannot be confirmed; all of these sources are NVIDIA's own.

Cite this claim

Jensen Huang, “The Tesla K20 offers 3x double precision performance (baseline not stated).” (said May 2012). Verdict: mostly true. Reliability API, claim c_928751c7b6b5, method 0.1.0+03d23c0e, dataset release v1.1.2. https://epistacklabs.com/claims/c_928751c7b6b5

This claim is in dataset release v1.1.2, with the same verdict.

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More factual claims by Jensen Huang

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  3. Mostly true Factual 1 June 2026
    “It’s the first one to be PCI Express Gen 6.”

    Vera is the first CPU to support PCI Express Gen 6.

  4. Mixed Factual 17 March 2026
    “We have demonstrated that NVFP4 can achieve inference without loss of precision, while delivering significant performance and energy efficiency improvements, and it is equally suitable for training.”

    NVIDIA has demonstrated that NVFP4 can achieve inference without loss of precision, with significant performance and energy-efficiency gains, and is equally suitable for training.

  5. Mixed Factual 17 March 2026
    “Over the past two years, the computational demands for inference have increased approximately 10,000 times, while usage has increased approximately 100 times.”

    Over the past two years, computational demand for inference rose about 10,000 times while usage rose about 100 times.

All of them

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