A look at DeepSeek's model optimization to reduce HBM use, potentially enabling domestic memory, ASIC, and CPU makers to create a Chinese AI hardware ecosystem
Have you ever wondered, how DeepSeek may make money, and lot of it? They didn't come up with competitive coding plans like GLM, MoonShot and MiniMax.
@bookwormengr
Context & Ripple Effects
DeepSeek’s earlier coverage emphasized an open-source, efficiency-oriented approach: commodity and disconnected hardware, lower-cost V3 training, and model architectures aimed at reducing computational burden. It later said V3.1 was customized for next-generation Chinese-made AI chips.
This optimization matters because it extends that software strategy into component choice. Related reporting that DeepSeek is developing an inference chip suggests the company is trying to make model design and hardware deployment reinforce one another.
First-order effects
Reducing HBM requirements could widen the set of memory, ASIC, and CPU configurations on which DeepSeek models can be deployed, lowering a key hardware constraint for its Chinese supply-chain partners.
Domestic component makers gain a clearer model-workload target: hardware optimized for DeepSeek-style inference and memory use rather than only for systems built around high-HBM accelerators.
Second-order effects
Chinese AI-chip and server vendors would face pressure to demonstrate competitive performance on memory-efficient models, not merely match the hardware configurations associated with leading foreign accelerators.
A more viable domestic deployment stack could strengthen DeepSeek’s bargaining position with chip suppliers and support its reported effort to reduce reliance on Nvidia and Huawei chips through an in-house inference design.
Third-order effects
If model-level memory optimization becomes repeatable, AI infrastructure competition may shift from access to the highest-end memory toward tighter co-design of models, accelerators, CPUs, and software runtimes.
That would not eliminate demand for advanced HBM in frontier training, but it could create a distinct, more domestically sourced inference ecosystem in China where efficiency and compatibility matter as much as peak hardware capability.
The trend: DeepSeek is part of a broader shift toward AI model–hardware co-design that turns efficiency gains into an alternative path for building national and vendor-specific AI stacks.
@bookwormengr Looks like the US will regret its policy of restricting high performance computing, GPU, and semiconductor fab technology exports to China. That set China on the course of building their own semiconductor supply chain and competency.
Great article here on DeepSeek. Their real story is not cheaper chatbots, but architecture that turns hardware scarcity into strategy. DeepSeek is not trying to sell coding seats, it is trying to make Chinese memory, accelerators, and systems useful for frontier AI. Every [image]
@mark_k @deepseek_ai pricing is the part that changes the read here. v4 pro is now $0.435 in / $0.87 out per 1m tokens. openrouter has gpt-5.5 pro at $30 / $180. not saying same quality. saying every wrapper now has to explain when it pays up.
@rohanpaul_ai this is the part people under-discuss: scarcity changes product taste. When compute is abundant, teams can hide bad UX behind bigger models. When memory and hardware are constrained, you are forced to decide what the model should actually look at, remember, and skip…
A fascinating deep dive into DeepSeek reveals a brilliantly unconventional strategy. They are completely ignoring standard industry trends. Instead of fighting for short-term multimodal profits, they are looking ahead. They use radical frameworks like MoE to absolutely crush
🇨🇳 🇺🇸 China's Huawei's new 122TB SSD shows how export controls can move innovation sideways instead of simply stopping it. Huawei just built a 122.88TB AI SSD by changing the package around the memory, not by matching Samsung's most advanced 400+ layer 3D NAND. And a 245TB [image…
Fascinating and very deep article about DeepSeek AI (@deepseek_ai). You would have never guessed what their strategy is, it's really interesting: They're not chasing quick money from coding plans or multimodal models. Instead, their radical architecture innovations (MoE, MLA,
Excellent analysis.....Who in China makes LPDDR? CXMT. They are only 0.5 Gen behind on speed for LPDDR and 1 generation behind on density. Not very far! In addition abundant NAND, Chinese ecosystem will have abundant LPDDR in near future. Can this relieve pressure on compute?
Reuters: DeepSeek just made its V4-Pro price cut permanent, pushing the price down to 25% of its original API cost. DeepSeek has not confirmed that better Ascend 950 supply caused the permanent cut, but the timing points to a cost curve moving downward as China's AI stack shifts …
Interesting read on how @deepseek_ai innovations in #AI models were driven by China's HW constraints and why its solutions will ultimately negate those constraints. 💡🐳
Let that sink in for a moment. DeepSeek v4 pro 75% discount. Permanent! In: $0.43 Out: $0.87 If you read the DeepSeek v4 tech paper you know that this model is insanely good when it comes to efficiency. Only 27% compute and only 10% cache compares to v3.2. SemiAnalysis wrote…
Wow. A massive 75% discount from DeepSeek. Either they've done some serious inference optimizations, or Huawei chips are just that much cheaper? More open-source AI models, better token economy.
Holy cow, DeepSeek just made top-tier AI services ridiculously cheap... PERMANENTLY. This is a fraction of the price of U.S. frontier models, with comparable performance in most tasks.
$0.435 per million input tokens. $0.87 per million output. $0.003625 for cache hits, basically free. For context, gpt-5.5 and claude opus 4.7 are roughly 35x to 100x more expensive per token for the same class of model.