Tag: Inference Optimization
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GGUF vs GPTQ vs AWQ: Which Quantization to Use
GGUF, GPTQ, AWQ, NF4, FP8 and MXFP4 solve different problems. How each format works, where each one wins, and which fits your deployment.
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KV Cache Explained: The Memory Math of LLM Inference
The KV cache, not parameter count, decides how many users an LLM can serve. The exact memory math, GQA and MLA compression, and caching economics.
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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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