Free tools Windows power users keep installed
One-click scans. No signup required.
DeepSeek’s rise helped trigger a sharp Nvidia selloff on January 27, 2025, because investors questioned whether AI companies would need as much expensive computing hardware as expected. Nvidia shares fell 17% that day amid a broader market reaction. That is a historical event—not evidence that DeepSeek is causing a current decline in Nvidia shares.
Why DeepSeek spooked Nvidia investors
Investors were reacting to what DeepSeek might mean for future sales, not simply judging the quality of one AI model. DeepSeek’s reasoning model, R1, drew attention to the possibility that developers could produce useful AI capabilities with more efficient methods and less costly infrastructure than some investors had assumed.
That possibility challenged a key expectation behind the AI boom: that continued progress in models would translate into ever-growing demand for premium GPUs and data-center equipment. If each model or task required less computing power, investors might revise down expectations for the returns on large AI infrastructure budgets. Contemporary coverage described this as a concern about future demand, not proof that Nvidia’s orders had collapsed. The Associated Press and CBS News covered the January 2025 market reaction; analysts quoted in contemporaneous coverage also considered the selloff potentially overdone.
What the January 2025 figures show
| Figure | What it refers to | What it does not establish |
|---|---|---|
| 17% | Nvidia’s one-day share-price fall on January 27, 2025, reported by the Associated Press amid a broader market reaction. | It is not a current performance figure, nor does it show that DeepSeek alone explains Nvidia’s share-price movements after that date. |
| 2,048 Nvidia H800 GPUs | The hardware count DeepSeek’s V3 technical report gives for training V3. | It does not represent all compute used across the model’s research and development. |
| About 36x higher throughput and about 32x lower cost per token | Nvidia’s 2025 Blackwell benchmark for DeepSeek-R1, compared with Nvidia’s January 2025 baseline. | These are company-reported benchmark results, not an independent comparison or a guarantee of results in other conditions. |
The H800 figure is especially important for interpreting claims that DeepSeek showed AI could be built without Nvidia hardware. DeepSeek’s own V3 technical report identifies Nvidia GPUs in its training setup and describes techniques intended to use computing resources efficiently. The report documents a more efficient approach; it does not show that GPUs were unnecessary.
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11#1 Best Overall
- AI Performance: 767 AI TOPS
- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
Why efficient models do not automatically mean less GPU demand
Efficiency can reduce the compute required for a given model or task. But lower costs can also make AI accessible to more developers and encourage more applications, which may increase the total amount of computing used. The balance between those effects depends on adoption, model use and infrastructure choices; the available figures do not quantify the net effect for Nvidia.
It also matters whether the question is about training or inference. Training builds a model; inference is the repeated computation involved in responding to users. A more efficient model might use fewer resources per interaction while still attracting enough usage to require substantial aggregate inference capacity. A single training setup cannot, by itself, settle the longer-term demand question.
Rank #2
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5070 Ti
- Integrated with 16GB GDDR7 256bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
Why the widely repeated training-cost figure needs context
A frequently repeated figure about DeepSeek’s training cost refers to a final training run, not the full cost of research, data, earlier experiments, staff, infrastructure or the company as a whole. Comparing that narrow figure directly with another company’s total AI spending would mix unlike measures. It does not establish what it would cost another developer to reproduce the work or how much infrastructure the AI industry will need.
What Nvidia says about demand and risk
In its 2026 quarterly SEC filing, Nvidia described significant demand for AI training and inference compute. The company also said that AI cloud providers and model developers may have difficulty securing long-term infrastructure contracts and investment-grade financing. Those disclosures show how Nvidia characterizes its business conditions; they are not a forecast of its share price or proof of what future demand will be.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
The filing also warns that export-control restrictions could affect products or services supporting third-party models originating in China, explicitly naming DeepSeek alongside Qwen and Kimi, and could affect Nvidia’s business and financial condition. This is a separate risk from the question of how efficiently a model uses compute: restrictions may affect where Nvidia can sell or support products, while efficiency concerns relate to how much hardware customers may want.
How to read Nvidia’s later Blackwell benchmark
Nvidia later published a Blackwell benchmark for DeepSeek-R1 reporting about 36 times higher throughput and about 32 times lower cost per token relative to its January 2025 baseline. The comparison is Nvidia’s own benchmark, with its stated conditions and vendor framing. It shows Nvidia presenting its hardware as capable of running the model efficiently; it should not be treated as an independent verdict on the cost or performance of every AI deployment.
Rank #4
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
What the selloff does—and does not—mean for investors
The January 27 reaction reflected uncertainty about whether AI progress would require as much costly infrastructure as investors had expected. DeepSeek’s work made that question more immediate, but the technical report also documents Nvidia hardware in V3’s training, and Nvidia’s later disclosures describe continuing demand for training and inference compute alongside financing and export-control risks.
Those facts do not settle how much Nvidia will sell or explain every later move in its shares. They show why the debate is about competing forces—compute efficiency, expanding AI use, infrastructure investment and market access—rather than a simple claim that one model made GPUs obsolete.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Quick Recap
Best Value
- Powered by the NVIDIA Blackwell architecture and DLSS 4 OC mode: 2640MHz/Default mode: 2610MHz (Boost Clock)
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.125-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
