Unverified50% confidenceFactExact time
The sharing granularity of large-model inference is finer than that of traditional SaaS—not only are compute resources shared, but also intermediate computation results (KV cache) and batch slots.
1
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50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
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Related Claims
Unverified随着大模型能力成熟，越来越多SaaS工具正在从「呈现信息」向「生成决策」进化70% similarUnverifiedDense models have simpler inference logic and better hardware compatibility in local deployment scenarios, whereas MoE models, despite activating fewer parameters during inference, still require all weights to be loaded into memory, placing even higher demands on hardware.68% similarVerifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.68% similarUnverifiedThe MAX level essentially extends the model's chain of thought within a single inference, allowing the model to perform more internal iterations before output, rather than invoking more parallel resources.67% similarVerifiedModel fine-tuning typically requires a large investment in annotated data and GPU computing resources.66% similar
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