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AI model

nemotron-3.5-lightning

Nvidia's open Nemotron 3.5 Lightning is a 30B-parameter mixture-of-experts model (3B active) that runs on a single consumer GPU.

Known aliases

  • Nemotron 3.5
  • Nemotron 3.5 Lightning
  • Nemotron 3.5 Lightning 30B
  • nemotron-3-5-lightning-30b-a3b
  • Nvidia Nemotron 3.5 Lightning

Current stories

productOne report1 publisher

Twenty model calls turn a two-second step into a 45-second wait

Red Hat clocks the same 20-call agent task at roughly 45 seconds on a slow backend and about 13 on a fast one. Model choice for agents is turning into a per-call latency budget, with capability as one input.

Publishers:redhat.com

Reality

Evidence45
Adoption
Not scored
Hype gap+10
Incentives50
Confidence55
buildOne report1 publisher

A 30B model that activates 3B still has to keep all 30B in VRAM

NVIDIA's dense-versus-MoE explainer uses Nemotron 3.5 Lightning to walk through per-layer routing. Its own text puts the throughput number and the memory number on two different parameter counts.

Reality

Evidence58
Adoption20
Hype gap+15
Incentives82
Confidence62
investOne report1 publisher

Five model releases in three days push the re-benchmarking bill onto buyers

Gartner puts this year's AI spending at $2.59 trillion, but the software and services layer the labs actually compete for is barely $1 trillion of it, which is what the compressed release calendar is defending.

Publishers:cnbc.com

Reality

Evidence54
Adoption31
Hype gap+33
Incentives76
Confidence55
buildOne report1 publisher

NVIDIA's edge-agent case rests on compact open models matching data-center capabilities

Nemotron 3.5 Lightning touches a tenth of its weights per token, which is what makes an agent loop plausible next to the sensors, but memory still has to hold all thirty billion and NVIDIA's own loop still escalates off-device.

Reality

Evidence42
Adoption
Not scored
Hype gap+38
Incentives84
Confidence54
buildOne report1 publisher

NVIDIA's inference sizing framework weighs latency, concurrency, cache hit rate and more to right-size GPUs

The framework's inputs are token lengths, concurrency, a latency percentile and the share of prompt tokens already sitting in the KV cache, and each of those changes arithmetic that a per-GPU throughput rating leaves untouched.

Reality

Evidence46
Adoption
Not scored
Hype gap+12
Incentives72
Confidence58
productOne report1 publisher

Perplexity's local agent keeps your files at home, if you own a $1,500 graphics card

Portable Computer runs Perplexity's agent on 27B models on your own hardware. Paid tiers only, Linux first, Windows in September, and a 24GB VRAM floor most desktops miss.

Publishers:zdnet.com

Reality

Evidence32
Adoption12
Hype gap+42
Incentives66
Confidence40
buildOne report1 publisher

A 30B model with 3B active arrives on JumpStart, aimed at the cheap middle of agent work

NVIDIA's Nemotron 3.5 Lightning is now deployable from SageMaker JumpStart and fits on one GPU. The argument underneath it is about billing, not benchmarks.

Publishers:aws.amazon.com

Reality

Evidence38
Adoption22
Hype gap+32
Incentives82
Confidence45
buildConfirmed4 publishers

Three frontier launches in a day, all pitched on price. Open weights set the ceiling.

Grok 4.6, Qwen3.8-Max and DeepSeek V4-Pro shipped inside about 24 hours, and two of the three came with downloadable weights. The benchmarks existed to justify a cheaper invoice.

Perspective Coverage

4 publishers
Builder
Builder 41%
Operator
Operator 31%
Investor
Investor 28%

Reality

Evidence68
Adoption52
Hype gap+22
Incentives74
Confidence63
buildOne report1 publisher

The zero-false-accept memory result belonged to the proxy, not the model

A verify-on-read experiment rerun across 14 live models on a fingerprinted 50-fact set found false-accept rates up to 0.38, and run-to-run noise wide enough to swallow a prompt fix.

Publishers:dev.to

Reality

Evidence57
Adoption14
Hype gap−12
Incentives31
Confidence44