VQV Signal
According to a post on X by @togethercompute, Together says its Together Compiler beat the best InferenceX configurations on NVIDIA B200 and B300 GPUs at the same per-user speed. The company reports 2.7x higher throughput on Kimi K3, 1.8x on GLM-5.3 and DeepSeek V4.1 Flash, and 1.5x on MiniMax M3.1 Flash. These figures come from Together's own benchmarks, not independent testing.
According to a post on X by @togethercompute, Together says its Together Compiler beat the best InferenceX configurations on NVIDIA B200 and B300 GPUs at the same per-user speed. The company reports 2.7x higher throughput on Kimi K3, 1.8x on GLM-5.3 and DeepSeek V4.1 Flash, and 1.5x on MiniMax M3.1 Flash. These figures come from Together's own benchmarks, not independent testing.
AI-assisted summary based on the listed source.
Together says Together Compiler beat the best InferenceX configurations on NVIDIA B200 and B300 at the same per-user speed: 2.7x on Kimi K3, 1.8x on GLM-5.3 and DeepSeek V4.1 Flash, and 1.5x on MiniMax M3.1 Flash. These are Together's own benchmarks. Why it matters: More throughput per GPU can lower the price of...
If the gains hold up, more throughput per GPU could lower the cost of serving popular open models for builders and businesses running inference. Users of those models could also see better economics over time. Independent results and broader availability of the compiler are the next things to watch.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to AI Chips, connected to Scobleizer News Pulse (X).
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