poolside-laguna-hackathon/kv-landlords

4-bit KV Cache Quantization (Laguna-XS.2)

A study of low-bit KV-cache quantization for vLLM. Full writeup, findings, and implementation notes are in PROBLEM.md.

Finding

The lever for 4-bit KV is the block layout, not the number format or the calibration. Uniform INT4 with per-channel-K (KIVI) blocking — a 16-token block per channel for K, per-token for V — beats vLLM's shipped NVFP4 / head_dim / absmax baseline by ~25% on K reconstruction error at identical memory (3.56× vs BF16), and the advantage grows with context (neutral below ~512 tokens, ~20–25% KL reduction beyond 1k). 4-bit is the quality floor; INT3 costs ~2–3× the distortion. The catch: per-channel blocking can't ride NVFP4's hardware microscale, so capturing it needs a software INT4 KV path.

Layout

• kv_quant.py — quantization primitives: formats (INT4 / INT3 / NVFP4-e2m1), block layouts (per-head-dim, per-channel), absmax / MSE-optimal calibration, the {format} × {layout} × {calib} sweep, and roundtrip.
• scripts/quant_sweep.py — reconstruction-RMSE grid over real KV activations.
• scripts/quant_ab.py — frozen-page teacher-forced KL A/B vs BF16 (incl. INT3).
• scripts/quant_longctx.py — long-context KL-by-position via an SDPA patch.
• PROBLEM.md — full analysis, vLLM kernel notes, cost/benefit.

Run

uv sync
.venv/bin/python scripts/quant_sweep.py     # RMSE grid
.venv/bin/python scripts/quant_ab.py        # downstream KL A/B
.venv/bin/python scripts/quant_longctx.py   # long-context trend

Scripts target poolside/Laguna-XS.2 and run on the Blackwell node.