Nations priced out of Big AI are building with frugal models
Article URL: https://restofworld.org/2026/frugal-ai-big-tech/ Comments URL: https://news.ycombinator.com/item?id=47626851 Points: 2 # Comments: 0
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The end of predictable storage economics and what that means for infrastructure planning
The enterprise storage market is currently experiencing unprecedented SSD price volatility driven by massive AI demand and multi-year capacity commitments from hyperscalers. Between Q2 2025 and Q1 2026, for instance, 30TB TLC SSD pricing increased by 257% (from $3,062 to $10,950), while HDD pricing remained relatively stable, increasing by 35%. The situation is challenging some fundamental, long-term assumptions about storage architecture strategy, particularly the collective experience that flash pricing declines over time. Until recently, it was a trend fully supported by the facts, and even factoring in cyclical variation, long-term cost curves have generally supported predictable cost-per-GB reductions. This generally solid predictability has underpinned everything from multi-year infr
My biggest Issue with the Gemma-4 Models is the Massive KV Cache!!
I mean, I have 40GB of Vram and I still cannot fit the entire Unsloth Gemma-4-31B-it-UD-Q8 (35GB) even at 2K context size unless I quantize KV to Q4 with 2K context size? WTF? For comparison, I can fit the entire UD-Q8 Qwen3.5-27B at full context without KV quantization! If I have to run a Q4 Gemma-4-31B-it-UD with a Q8 KV cache, then I am better off just using Qwen3.5-27B. After all, the latter beats the former in basically all benchmarks. What's your experience with the Gemma-4 models so far? submitted by /u/Iory1998 [link] [comments]
DenseNet Paper Walkthrough: All Connected
When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where the weight update of a model during training slows down or even stops, hence causing the model not to improve. When a network is very deep, the [ ] The post DenseNet Paper Walkthrough: All Connected appeared first on Towards Data Science .
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My biggest Issue with the Gemma-4 Models is the Massive KV Cache!!
I mean, I have 40GB of Vram and I still cannot fit the entire Unsloth Gemma-4-31B-it-UD-Q8 (35GB) even at 2K context size unless I quantize KV to Q4 with 2K context size? WTF? For comparison, I can fit the entire UD-Q8 Qwen3.5-27B at full context without KV quantization! If I have to run a Q4 Gemma-4-31B-it-UD with a Q8 KV cache, then I am better off just using Qwen3.5-27B. After all, the latter beats the former in basically all benchmarks. What's your experience with the Gemma-4 models so far? submitted by /u/Iory1998 [link] [comments]
DenseNet Paper Walkthrough: All Connected
When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where the weight update of a model during training slows down or even stops, hence causing the model not to improve. When a network is very deep, the [ ] The post DenseNet Paper Walkthrough: All Connected appeared first on Towards Data Science .
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