model
An 8-billion-parameter open-weight large language model in Meta's Llama 3.1 family, used for local inference, agent tasks, and benchmarking.
No evidence-backed relationships are recorded.
Open-source tool runtape traced an agent's unrequested invoice forward to one sentence in a tool result, 10 of 10 reruns with it against 0 of 10 without. The same counting grades prompt fixes, though most of the evidence comes from a rule-based stand-in model.
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Llama 8B on an RTX 4060 Ti 16GB fell from 42.5 to 3.8 tokens a second with a fifth of the model in system RAM, according to a dev.to benchmark. For a local coding agent, that makes VRAM for weights plus context the first spec to check, ahead of bandwidth.
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Both field test reports pointed at replay and matcher calibration, but v0.3.0 fixed the recall denominator with one list comprehension that scopes each candidate's references to its own domain, and the reported number roughly doubled.
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CauterRule's v0.3.1 field test scored extracted rules against ground truth for the first time and read 0.08 on the golden corpus. The trigger half of those rules was matching at 0.6 or better, while the directive comparator counted tokens.
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A dev.to writeup instrumented 1,180 local requests over 24 hours and counted 214 cold model loads, including a summarizer on a 10-minute cron that came up cold every time.
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Single-user inference streams weights out of memory, so the sizing question for a private document assistant is RAM and prompt length rather than which accelerator a vendor quoted. The laptop in question was three years old.
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At Hot Chips 2026 Samsung detailed a validated LPDDR5X-PIM part claiming 614 GB/s of internal bandwidth and 2.28x to 3.01x inference gains over plain LPDDR5X.
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A residency policy that barred even embedding calls from leaving the building forced a full local RAG stack. The hardware math turns out to be the easy part.
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Leaked internal tests put the Exynos 2700 ahead of an unshipped Snapdragon. The number that matters is the 7.44 trillion won Samsung spent on outside application processors in six months.
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The acc and acc_norm split in lm-eval-harness can move in opposite directions on one checkpoint. Pick the metric before you train, and say which one you picked.
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A dev.to explainer on local inference benchmarks makes a point worth pinning up: tokens per second is a function of how many users you tested with, not a property of the hardware.
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A UNICAMP team tested 21 models against left-, right- and unlabelled users. All of them moved toward the user, which makes any neutrality audit run without a user profile close to useless.
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