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CORRECTION-NOTICE.md

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+# Correction Notice — KV Cache Benchmark v3
+
+**Date:** 2026-04-01
+**Original publication:** 2026-03-31
+**Status:** Correcting methodology and findings
+
+---
+
+## What Changed
+
+### Finding 2 (Memory "Paradox") — RETRACTED
+
+**Original claim:** "q4_0 uses MORE memory than f16 on unified memory (+6%)"
+**Methodology used:** Process RSS via `ps`
+**Problem:** RSS measures CPU-side process memory, not GPU/unified memory allocations. On GB10 unified memory, KV cache allocations are not visible in RSS.
+
+**Corrected measurement (nvidia-smi + llama.cpp internals):**
+
+| Cache Type | KV Buffer (llama.cpp) | Total GPU (nvidia-smi) | vs f16 |
+|-----------|----------------------|----------------------|--------|
+| f16 | 768 MiB | 23,092 MiB | baseline |
+| q8_0 | 408 MiB | 22,732 MiB | **-360 MiB (saves 47%)** |
+| q4_0 | 216 MiB | 22,540 MiB | **-552 MiB (saves 72%)** |
+
+**Conclusion:** KV cache quantization DOES save memory on GB10, as expected. The "paradox" was a measurement error.
+
+### Finding 1 (Speed Cliff) — RETRACTED
+
+**Original claim:** "92.5% prompt throughput collapse at 64K with q4_0"
+**Corrected finding:** No prompt throughput cliff exists. q4_0 prompt processing is essentially identical to f16 at all context lengths including 110K tokens.
+
+**What actually happens:** Generation (decode) throughput degrades ~37% at 110K context with q4_0 vs f16. This is a real effect but it is generation speed, not prompt processing, and it is 37% not 92.5%.
+
+**Corrected throughput data (full range):**
+
+| Context | f16 prompt | q8_0 prompt | q4_0 prompt | f16 gen | q8_0 gen | q4_0 gen |
+|---------|-----------|-------------|-------------|---------|---------|---------|
+| ~1.5K | 923 | 925 | 926 | 45.2 | 45.3 | 45.6 |
+| ~6K | 1,211 | 1,207 | 1,206 | 44.7 | 44.9 | 45.0 |
+| ~12K | 1,188 | 1,184 | 1,191 | 44.9 | 42.9 | 42.7 |
+| ~24K | 1,153 | 1,149 | 1,152 | 44.6 | 39.7 | 39.3 |
+| **~110K** | **815** | **810** | **813** | **38.0** | **25.0** | **24.0** |
+
+The original 92.5% collapse was likely caused by failed completion requests returning error data, not actual throughput measurements.
+
+### Finding 3 (q8_0 Sweet Spot) — PARTIALLY VALID
+
+q8_0 does offer genuine KV buffer savings (47%) with minimal throughput impact at short-to-medium context. The generation speed degradation at 24K (~10%) needs further investigation at longer contexts.
+
+---
+
+## Root Cause of Original Errors
+
+1. **RSS is the wrong metric** for KV cache on unified memory. RSS reflects CPU-side process allocations, not GPU-side unified memory usage.
+2. **nvidia-smi + llama.cpp verbose output** provide the correct measurements.
+3. **The original benchmark did not verify that completions actually succeeded** — some data points may have been from failed requests.
+
+## Credit
+
+Community feedback from u/audioen on r/LocalLLaMA identified the RSS measurement flaw. Their critique was correct.