Unverified50% confidenceFactExact time
The Betelgeuse inference engine can continuously run the 68GB Qwen 3.8 Flash Next MoE model at 22 tok/s on a PC with an RTX 3060 12GB graphics card and 16GB DDR4 memory, and the output is 100% bit-identical to the original model.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/31/2026
First Seen
Valid until: 1/29/2027
Sources
Running a 68GB Large Model on a 12GB GPU: Betelgeuse Engine Benchmarked at 22 tok/s
redditr/LocalLLaMA10/31/2026
Related Entities
Related Claims
UnverifiedThe Qwen3.8 Flash Next (Q3 quantized version) weights are approximately 84 GB and run on an AMD 7900XTX 24 GB GPU with an average decoding output speed of approximately 90 tokens/s.78% similarUnverifiedStrata enables a single GPU with 12 GB of VRAM to smoothly run Qwen3.8 Flash Next, a large model with 125 billion activated parameters and an overall size of 176B.76% similarUnverifiedThe open-source project StraightA claims to be able to run the 125B-parameter Qwen 3.8 Flash Next model on a consumer-grade PC with more than 12G of VRAM, provided the system is equipped with 35 to 55G of RAM.75% 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 H3.75% similarUnverifiedA ~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.75% similar
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