輝達 B200 晶片市場需求極度殷切 殘值逆勢狂飆超原價 58%
記者孟圓琦／編譯
隨著大型語言模型（LLM）的快速發展，人工智慧（AI）晶片的二手殘值已成為觀察當前 AI 超級週期健康狀況的重要指標。
根據市調機構 Silicon Data 的最新數據顯示，儘管 Anthropic 執行長 Dario Amodei 與 OpenAI 執行長 Sam Altman 等業界領袖呼籲放緩 AI 模型開發節奏，但市場對高階算力的需求依然強勁，輝達（NVIDIA）B200 GPU 的市場價值更呈現逆勢上漲趨勢。
數據指出，輝達較早期的 A100 與 H100 GPU 目前殘值遠高於傳統 3 年或 5 年直線折舊法所計算出的預估值。更令人矚目的是，在大規模上市約一年後，B200 GPU 的二手殘值高達原始發售價格的 158%，相當於享有 58% 的溢價幅度。分析認為，此現象主因於產品相對稀缺性與市場無間斷的強勁需求。
GPU financing has become a core pain point in AI infrastructure. AI startups report that instead of reserving one year of compute, they’re increasingly asked to reserve three while putting down 30-40% upfront.
Compute providers aren’t imposing these onerous terms arbitrarily: demand is outstripping supply, and building a data center is capital-intensive, with financing itself expensive to obtain. Part of why: financial markets still treat GPUs as fast-depreciating assets.
Standard market practice still leans on 3-year straight-line schedules to assess GPU residual value, an assumption that flows straight through to the terms providers can offer, and the terms they in turn ask of their customers.
We plotted in the quoted post the actual evolution of residual values for major GPU generations as a share of their original purchase prices. These are market-based fair value estimates, derived from the forward curves implied by observed GPU rental contracts.
The A100 and H100 are both holding value well in excess of what 3 or 5-year straight-line schedules would imply. In fact, B200, whose supply is scarce, we estimate a current residual value well in excess of its original purchase price!
We believe that the financial institutions and capital markets will eventually come view GPUs as the long-lived income-generating capital assets they are and allow them to be financed accordingly. Silicon Data is committed to bringing price transparency and indices to help improve the capital efficiency around GPU financing.
— Silicon Data (@Silicon_Data) September 16, 2026
SemiAnalysis 的估算亦顯示，B200 在推理工作負載方面具備極高效率，以運行 DeepSeek R1 為例，在每位使用者每秒 76 個詞元（Token） 的速率下，每百萬詞元（Token） 的成本僅需 0.20 美元。
此外，晶片租賃市場亦反映出相同的供需緊繃態勢。今年 1 月，輝達 B200 GPU 的每小時租賃價格低於 5 美元；至 8 月時，該價格已攀升至 5.50 美元至 5.80 美元之間，漲幅達 50% 至 80%。強勁的租賃與二手市場表現，再次凸顯了輝達在 AI 硬體市場中難以撼動的龍頭地位。
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資料來源：wccftech
