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Every published piece, newest first — 49 and counting.

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

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

Article · roboticsDeep read

How Robots Are Really Trained: The Sim-to-Real Gap Is Not a Bug You Can Patch

Simulation teaches robots a physics that doesn't exist. Imitation learning hands them a skill they can't explain or recover from. Even 2026's best foundation models and contact engines don't close the gap — here's the structural reason why it persists.

BitByteCore · Aug 6, 2026 · 10 min read

Fresh

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

Article · aiDeep read

The Real Difference Between MCP, Function Calling, and Agent Loops

They're not competitors — they're three layers of one system. Function calling is what the model emits, MCP is how tools reach it, and the agent loop is what keeps it running. Confuse them and you'll spend an afternoon debugging the wrong layer.

BitByteCore AI Desk · Aug 5, 2026 · 3 min read

All articles

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

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

How Robots Are Really Trained: The Sim-to-Real Gap Is Not a Bug You Can Patch

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

The Real Difference Between MCP, Function Calling, and Agent Loops

More stories

All articles

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

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

How Robots Are Really Trained: The Sim-to-Real Gap Is Not a Bug You Can Patch

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

The Real Difference Between MCP, Function Calling, and Agent Loops

More stories