Unverified50% confidenceBenchmarkExact time
Janas runs the 48.4GB Qwen3-Next-80B on a 32GB-memory laptop at 37 tok/s; on the same machine and same model llama.cpp reaches only 2.8 tok/s.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/13/2026
First Seen
Valid until: 10/27/2026
Sources
Related Entities
Related Claims
UnverifiedA ~25 GB Q3 model running entirely within the RTX 4090's VRAM (bandwidth ~1 TB/s) theoretically achieves ~40 tokens per second, whereas running it in DDR5 memory (~80 GB/s) drops throughput to 1–3 tokens per second.73% similarUnverifiedIn a llama.cpp environment with Qwen3 27B, the R9700 achieved 27.1 tokens/s (approximately 65% of bandwidth ceiling) and the B70 achieved 21.4 tokens/s (just over approximately 50%).71% similarUnverifiedCerebras WSE-3片上内存高达44GB SRAM，带宽达到21 PB/s，比GPU的HBM内存快约1000倍70% similarUnverifiedFor a model of approximately 160 GB (such as the UD-Q3_K_XL quantized version of Qwen3.5-397B), running the lens requires approximately 20 GB of additional memory.70% similarUnverifiedA mid-range machine equipped with a 5060Ti graphics card, 16GB of VRAM, and 32GB DDR4 RAM can fully run the ComfyUI workflow collection combining Qwen3 and MiniMax H3.70% similar
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