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Quantization Explained: What Q4, Q8, and FP16 Actually Do to a Local Model
Article · aiDeep read

Quantization Explained: What Q4, Q8, and FP16 Actually Do to a Local Model

Quantization shrinks AI model weights from 16-bit floats to 4- or 8-bit. Here is exactly what that trade-off costs you, and how to pick between Q4_K_M, Q8, and FP16 for the model you actually want to run, with an interactive memory calculator.

BitByteCore Silicon Desk · Aug 6, 2026 · 8 min read

Fresh
CUDA Lock-In Is Real: A Precise Cost Accounting of What Switching GPU Vendors Actually Breaks
Article · hardwareDeep read

CUDA Lock-In Is Real: A Precise Cost Accounting of What Switching GPU Vendors Actually Breaks

Switching away from CUDA isn't one migration — it's five separate porting problems, hidden revalidation costs, and an org chart that fights you the whole way.

BitByteCore Silicon Desk · Aug 6, 2026 · 9 min read

Fresh
Mixture-of-Experts Models: How They Work and Why They Cut Inference Costs
Article · aiDeep read

Mixture-of-Experts Models: How They Work and Why They Cut Inference Costs

MoE models activate only a fraction of their parameters per token: DeepSeek-V3 fires 37B of 671B, delivering large-model quality at small-model compute cost. How routers, experts, and load balancing actually work, and where the savings are and are not real. With an interactive dense-vs-MoE view.

BitByteCore Silicon Desk · Aug 6, 2026 · 10 min read

Why only a few foundries make the leading-edge chips
Article · chipsDeep read

Why only a few foundries make the leading-edge chips

Leading-edge chip manufacturing has collapsed to a handful of companies, in practice one clear leader and two challengers. The reasons are structural: staggering cost, an unforgiving learning curve, and a feedback loop that punishes falling behind. Here is how it works.

BitByteCore Silicon Desk · Jul 31, 2026 · 8 min read

What a Nanometer Process Node Really Means
Article · chipsDeep read

What a Nanometer Process Node Really Means

The number on a process node, 7nm, 3nm, now 2nm, is a marketing label, not a measurement of anything physical on the chip. Here is what it actually tracks, and the metrics that describe a chip honestly.

BitByteCore Silicon Desk · Jul 31, 2026 · 9 min read

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