Unverified50% confidenceFactExact time
The official tweet did not provide specific benchmark scores, tokens-per-second generation speed, or accuracy comparison data after 2-bit quantization for Nemotron 3.5 Bolt 74B-A9B.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/18/2026
First Seen
Valid until: 11/1/2026
Sources
Related Entities
Related Claims
UnverifiedNemotron Bolt 3.5 74B A9B uses 2-bit quantization combined with quantization-aware training (QAT) to compress the entire model to about 24GB of VRAM usage.69% similarUnverifiedWhen running Nemotron Super (approximately 120 billion parameters), the unoptimized speed is about 16.6 tokens per second; after enabling MTP, Flash Attention, and KV cache, it improves to 27.2 tokens per second (at a context length of around 17,000), a speedup of approximately 64%.65% similarUnverified13B–14B class models (4-bit quantization) see speeds drop to 2–5 tokens per second under this configuration64% similarVerifiedNVIDIA claims that the weights of Nemotron 3.5 Bolt 74B-A9B can run in 24GB of memory, and that 24GB refers only to the weights themselves, excluding context, KV cache and runtime overhead.64% similarUnverifiedRunning gemma4:26b (GGUF Q4_K_M) via Ollama on a Colab A100 instance achieves a generation speed of 179.42 tok/s, taking 2.16 seconds for 369 tokens63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1063516API
curl https://kongchang.com/api/v1/knowledge/claims/1063516MCP
get_claim(id=1063516)