Tag: Inference Optimization
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Speculative Decoding: How LLMs Generate 3x Faster
Speculative decoding achieves 3-4x LLM speedup with zero output quality loss. The math proof, EAGLE-2’s 4.26x result, and when it does not help.
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DeepSeek V4’s Hybrid Attention Cuts KV Cache by 10x. Here’s the Architecture.
DeepSeek V4-Pro processes one million tokens using 10% of the KV cache V3.2 needed. The mechanism is Hybrid Attention: two complementary compressors interleaved across 61 layers. Here’s how…
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30 Days After QJL: What’s Actually Compressing the KV Cache
After QJL failed, three approaches own the KV cache frontier: TriAttention’s pre-RoPE selection, LRKV architectural compression, and adaptive bit-width.
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Darkbloom Has 8 Security Layers, Not 4: What the Press Missed
Eigen Labs launched Darkbloom on April 15 as a decentralized inference network routing requests to idle Apple Silicon Macs. Every outlet has covered the four-layer privacy architecture. The…
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Every Grok 4.20 Explainer Named the Four Agents. xAI’s Documentation Names Zero of Them.
xAI shipped Grok 4.20 multi-agent in February 2026. Every explainer published since then describes four named agents called Grok, Harper, Benjamin, and Lucas debating in parliament. Those names…
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ASML Is the Only Company That Can Make AI Chips Possible. Its Next Machine Costs $400 Million.
The current generation of ASML’s EUV machines is approaching the physical limit of what it can print. The High-NA EUV successor, at $400 million per unit, is now…
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70 Million TB/s: The Three-Lever Mechanism Driving AI’s Memory Bandwidth Growth
NVIDIA’s B200 delivers 8 TB/s of HBM3e memory bandwidth per chip. Aggregate AI cluster bandwidth exceeds 70 million TB/s. Memory bandwidth, not compute FLOPS, is the bottleneck for…
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How Google TurboQuant Compresses LLM Memory by 6x With Zero Accuracy Loss
Google Research published TurboQuant on March 25, 2026: a KV cache compression algorithm that reduces LLM inference memory by 6x at 3-bit precision with zero accuracy loss and…
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