Unverified50% confidenceFactExact time
Nemotron 3.5 Bolt 74B-A9B uses native 2-bit quantization; the model was designed for low-bit precision from the training stage rather than compressed after the fact.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedNemotron 3.5 Bolt uses native 2-bit quantization (IQ2, with quantization-aware training QAT) to compress the model into 24GB of VRAM, allowing it to run on a single consumer-grade or single-card workstation.80% similarUnverifiedThe competition programming model is fine-tuned from Nemotron-3-Ultra-550B-A55B and uses the NVFP4 low-precision quantization format.75% similarVerifiedNemotron Bolt 3.5 74B A9B is a mixture-of-experts (MoE) model with about 74B total parameters, activating only about 9B parameters per token.75% similarUnverifiedNemotron 3.5 Super VL's backbone was initialized from Nemotron 3 Super Base, distilled 1T tokens via Nemotron 3 Ultra Base, extended to a 1M context, then underwent 67B tokens of software engineering mid-training, and only then was vision training done.72% similarUnverifiedNVIDIA claims that Nemotron Bolt 3.5 is faster than most popular dense models while being smarter than mainstream MoE models with the same memory footprint.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1063513API
curl https://kongchang.com/api/v1/knowledge/claims/1063513MCP
get_claim(id=1063513)