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TurboQuant+ — KV-cache compression beyond the Google baseline

Tracking issue: #91 (proposal + planned scope).

This document describes the TurboQuant+ (TQ+) integration in Atlas: what changed vs upstream, why each piece matters, before/after numbers, and exactly how to reproduce them.

It's deliberately long because the change is large (~12 kernel files, 9 new KvCacheDtype variants, per-side cache-pool refactor, two new host drivers) and because reviewers should be able to A/B every claim from a fresh clone.

Primary upstream research dumping ground for the TQ+ work cited below (across the Rademacher signs, matched-norm L2, sparse V, asymmetric K/V, InnerQ, weight pre-rotation, and the broader paper set): TheTom/turboquant_plus. The llama.cpp implementation reference is the sibling repo TheTom/llama-cpp-turboquant.

If you only want the headline (single sentence): TQ+ ships canonical-form TurboQuant kernels + Turbo2 (was crashing on upstream) + 9 asymmetric KvCacheDtype variants + per-side cache pool refactor + 9 enum-coverage unit tests. On Qwen3.6-35B-FP8 at greedy decode, output is byte-identical to upstream on the symmetric line — i.e. zero perf or quality regression; the kernel-level math fixes are correct on paper (-DTQ_PLUS_SIGNS, matched-norm L2, real Turbo3 prefill kernel) but greedy decoding on this model's attention scores absorbs the numerical differences without flipping argmax winners. The value of this PR is the new capabilities and the dispatcher safety nets, not a benchmark win on the existing symmetric dtypes.

Background

Atlas already shipped a TurboQuant variant of the KV cache (Walsh-Hadamard rotation + Lloyd-Max codebook with per-group FP8 scales) under --kv-cache-dtype turbo{3,4,8}. That implementation was a strictly weaker form of the TurboQuant paper (Zandieh et al., arXiv:2504.19874, April 2025):

  • Plain WHT (no sign mask). The paper's Randomized Hadamard Transform applies a random sign vector before the WHT so that the rotation is a true uniform-random orthonormal transform (a uniform random rotation, in the limit). Without the sign mask the rotation is deterministic and does not Gaussianise the coordinate marginals, which is the property that lets a fixed Lloyd-Max codebook attain near-optimal distortion.
  • Per-group amax scaling. The codebook-friendly L2-minimising scale is ||original|| / ||reconstructed||, not MAX / amax.
  • Turbo3 prefill routed through the NVFP4_64 paged-prefill kernel, which reads 4-bit nibbles. Turbo3 stores 3-bit packed data (8 values per 3 bytes); the nibble reads silently sampled wrong codebook indices.

TurboQuant+ ("TQ+") is the beyond-Google research line published as TheTom/turboquant_plus (the umbrella research repo, ~15 papers + reference implementations across multiple downstream engines) with the llama.cpp engine reference at TheTom/llama-cpp-turboquant. The pieces ported into Atlas in this work:

Feature TQ+ paper Atlas commit
Canonical Randomized Hadamard signs TurboQuant paper (Zandieh et al., arXiv:2504.19874) kernel commit
Matched-norm L2 correction matched-norm-l2.md kernel commit
Sparse V dequant (attention-gated row skip) sparse-v-dequant.md kernel commit
fp16 centroid LUT (halves shmem) TQ+ optimisation kernel commit
Turbo2 — 2-bit Lloyd-Max codebook low-bit-codebooks.md kernel commit
Real Turbo3 prefill (fixes NVFP4 misroute) (Atlas-specific fix) kernel commit
Bf16K + Turbo3V safer-asym asymmetric-kv-compression.md kernel commit
InnerQ per-channel Q/K equalisation inner-q.md both commits
256/512 sign arrays + weight pre-rotation TQ+ scaling work both commits
Boundary V dtype (LA-V7 substrate) layer-aware-v-compression.md Rust commit

What landed (file-by-file)

Kernel commit — pure CUDA

File Why
kernels/gb10/common/tq_plus_signs.cuh Vendored seed=42 Rademacher sign tables (128/256/512). Byte-identical to TURBO_WHT_SIGNS{1,2} in turbo-quant.cuh from the llama.cpp fork.
kernels/gb10/common/wht_bf16.cu TQ_PLUS_SIGNS-gated S2·H·S1 path for hd=128 + new wht_bf16_inplace_inv since (S2·H·S1)·(S2·H·S1) ≠ I when S1 ≠ S2.
kernels/gb10/common/reshape_and_cache_turbo.cu Matched-norm L2 correction across turbo2/3/4 write paths; Turbo2 (2-bit, 4 values/byte) write kernel; combined Bf16K+Turbo3V write kernel.
kernels/gb10/common/paged_decode_attn_turbo{2,3,4,8}*.cu (9 files) fp16 __half LUT (halves shmem) + sparse V gated on per-row softmax exp factor on both remainder and BC=4 batched paths.
kernels/gb10/common/paged_decode_attn_turbo2_128.cu (new) hd=128 Turbo2 decode.
kernels/gb10/common/paged_decode_attn_bf16k_turbo3v_128.cu (new) Combined K-bf16 + V-turbo3 decode at hd=128.
kernels/gb10/common/inferspark_prefill_paged_turbo{2,3}.cu (new) Real prefill kernels for Turbo2 (didn't exist) and Turbo3 (replaces NVFP4 misroute).
kernels/gb10/common/inferspark_prefill_paged_bf16k_turbo3v.cu (new) Asymmetric K-bf16 / V-turbo3 prefill.
kernels/gb10/common/prefill_paged_compute_asym.cuh (new) FA template with per-side LOAD_K_TILE / LOAD_V_TILE macro hooks — lets future asym combos (Bf16K+Turbo4V, Fp8K+Turbo3V, ...) reuse the FA pipeline by supplying different load macros.
kernels/gb10/common/tq_plus_innerq.{cu,cuh} + _apply.cu (new) InnerQ device state, calibration accumulator, Q pre-WHT and K post-WHT scale apply.
kernels/gb10/*/nvfp4/KERNEL.toml Register the new modules for every model target.

Rust commit — dispatch + tests + host drivers

File Why
crates/spark-runtime/src/kv_cache.rs 9 new KvCacheDtype asym variants + kv_pair() / is_asymmetric() / FromStr aliases. Per-side k_block_bytes_for_layer / v_block_bytes_for_layer APIs so asym pools can size independently.
crates/spark-runtime/src/kv_cache/paged_impl.rs Per-side K/V pool allocation; k_block_stride_bytes_for_layer / v_block_stride_bytes_for_layer exposed for kernel launchers.
crates/spark-runtime/src/kv_cache/tests_tq_plus.rs (new) ALL_VARIANTS / ASYM_VARIANTS / SYM_VARIANTS arrays force new dtypes to be added to tests when added to the enum. Pins per-dtype block-byte arithmetic.
crates/spark-model/src/layers/qwen3_attention/decode/write_kv_cache.rs Symmetric turbo write w/ WHT bookend (Turbo2/3/4/8) + asym Bf16K+Turbo3V dispatch + InnerQ K-side apply.
crates/spark-model/src/layers/qwen3_attention/decode/run_paged_decode.rs Turbo decode dispatch incl. asym.
crates/spark-model/src/layers/qwen3_attention/decode/attention_forward.rs V-type-aware iWHT guard + InnerQ Q-side apply.
crates/spark-model/src/layers/qwen3_attention/prefill/paged_attn.rs Turbo2 + Bf16K+Turbo3V + real Turbo3 prefill routing.
crates/spark-model/src/layers/qwen3_attention/{init,types,mod}.rs New KernelHandle fields + loaders + module exports.
crates/spark-model/src/layers/qwen3_attention/innerq_driver.rs (new) InnerQDriver::from_env() reads TURBO_INNERQ=N + TURBO_INNERQ_STRENGTH. start() at boot, maybe_finalize(128) per scheduler chunk (idempotent kernel-side).
crates/spark-model/src/weight_loader/qwen35/load_layers/tq_plus_weight_rotation.rs (new) apply_canonical_rotation_inplace helper. Wired into attention_arms.rs between sharding and NVFP4 quant for Q/K/V projections. Gated on TQ_PLUS_WEIGHT_ROTATION=1.
crates/spark-server/src/main_modules/{kv_dtypes.rs,serve_phases/kv_cache.rs} + crates/spark-model/src/factory/build.rs boundary_dtype parameter on build_layer_kv_dtypes (LA-V7 substrate).
tests/test_kv_dtype_smoke.py (new) Per-dtype container start + 64-tok generation smoke. Catches dispatch-arm fall-through that unit tests can't (e.g. the original Turbo2 → FP8 ABI mismatch).

Reproduction

All numbers were measured on:

  • Hardware: NVIDIA GB10 (ASUS Ascent GX10), 128 GB unified memory
  • Model: Qwen3.6-35B-FP8 at /home/pidtom/models/qwen3.6-35b-fp8
  • OS: Ubuntu 24.04 inside the Atlas runtime container
  • Driver/CUDA: NVIDIA driver supporting CUDA 13.0, container base nvidia/cuda:13.0.0-runtime-ubuntu24.04

1. Build

git clone <atlas-repo> && cd atlas
git checkout feature/tq-plus-clean   # or whichever branch carries the integration

docker build -f docker/gb10/Dockerfile -t atlas-gb10-tqplus .
# ~8 min cold (cargo build --release -p spark-server inside builder stage)

2. Run per-dtype

For each dtype D in {bf16, fp8, turbo2, turbo3, turbo4, turbo8} (and the asym variants below), start a fresh container:

docker stop atlas-bench 2>/dev/null; docker rm atlas-bench 2>/dev/null
docker run -d --name atlas-bench --gpus all --ipc=host \
  -p 8889:8888 -v /path/to/qwen3.6-35b-fp8:/model \
  -e RUST_LOG=warn \
  atlas-gb10-tqplus \
  serve --model-from-path /model \
    --port 8888 --bind 0.0.0.0 --max-seq-len 32768 \
    --kv-cache-dtype $D --kv-high-precision-layers 0

Wait for /v1/models to respond, then run the bench harness:

python3 tests/atlas_matrix_no_hp.py --dtypes $D --out /tmp/$D.json

The harness (in tests/atlas_matrix_no_hp.py; the script started life as ad-hoc local tooling at /tmp/atlas_matrix_no_hp.py on the original test host and the same code is reproduced here for reviewer convenience) does 4 things per dtype:

  1. PPL similarity: Continues a fixed WikiText Manhattan-Project prompt for 64 tokens and reports difflib.SequenceMatcher ratio against the reference completion ("J. Robert Oppenheimer was the director of the Los Alamos Laboratory that designed the actual bombs."). Higher is better; 0.84 is the baseline.
  2. Decode short: 3 calls of "Explain attention." at 256 tokens. Median tok/s.
  3. Prefill 8K: 3 calls of an 8000-character prompt at max_tokens=1. Reports prompt_tokens / wall_time.
  4. Decode after 8K: 2 calls of the same 8K prompt at max_tokens=128. This is wall-time including prefill, NOT steady-state decode rate.

3. End-to-end smoke for every dtype

python3 tests/test_kv_dtype_smoke.py \
  --image atlas-gb10-tqplus \
  --model-path /path/to/qwen3.6-35b-fp8

Iterates every dtype, starts a container, generates 64 tokens, checks non-empty completion. Distinguishes load-time SKIP (model weights don't fit the K-side dtype) from runtime FAIL (kernel dispatch crash). Total ~3 min per dtype × 15 dtypes.

4. Unit tests (no GPU needed)

docker run --rm --entrypoint /bin/bash \
  -e ATLAS_SKIP_BUILD=1 -e CUDARC_CUDA_VERSION=13000 \
  -v $(pwd):/atlas \
  atlas-gb10-tqplus-dev \
  -c "cd /atlas && cargo test -p spark-runtime --tests kv_cache::"

Expected: test result: ok. 32 passed; 0 failed.

Results

All numbers in this section are from tests/atlas_bench_comprehensive.py on Qwen3.6-35B-FP8, single GPU, --max-seq-len 32768 --kv-high-precision-layers 0. Per-metric median across the harness's repeated calls (5 for prefill + decode_short, 3 for dec_after_8K). PPL sim is difflib.SequenceMatcher ratio against the WikiText Manhattan-Project reference continuation — the same fixed prompt across every run so deltas are apples-to-apples.

pre_2K / 8K / 16K are prefill throughput at max_tokens=1 for prompts that produce ≈ 405 / 1595 / 3177 input tokens respectively. dec_short is 256-token completion after a short prompt. dec_after_8K is 128-token completion after an 8K-token prefill — wall-time including the prefill, not steady-state decode rate. It's retained because it's what tests/atlas_matrix_no_hp.py in the existing repo already reports.

Reproduce one cell:

python3 tests/atlas_bench_comprehensive.py \
  --image atlas-gb10-tqplus \
  --model-path /path/to/qwen3.6-35b-fp8 \
  --config-name "tqplus-default" \
  --dtypes turbo3 \
  --out /tmp/turbo3.json

Symmetric KV-dtype matrix (TQ+ default, no env knobs)

dtype PPL sim dec_short pre_2K pre_8K pre_16K dec_after_8K
fp8 0.8485 72.15 627.97 1239.06 1402.00 41.65
nvfp4 0.8485 72.10 636.28 1239.39 1410.56 41.53
bf16 0.8485 71.89 630.69 1227.54 1390.46 40.87
turbo8 0.8384 71.73 640.08 1248.85 1416.28 41.11
turbo4 0.8384 71.62 638.01 1238.62 1403.65 40.94
turbo3 0.8384 71.65 634.15 1230.51 1400.57 40.85
turbo2 0.6465 71.46 692.35 1336.50 1519.93 41.56

Two columns to draw your eye to:

  • PPL sim is uniform at 0.848 / 0.838 across every non-Turbo2 dtype. The 0.01 gap between bf16/fp8/nvfp4 (0.848) and turbo3/4/8 (0.838) is within the harness's tokeniser-level granularity at this prompt length (one different output token can flip the SequenceMatcher ratio by ~0.01).
  • Turbo2 prefill is the fastest at every context length: 692 / 1337 / 1520 tok/s vs ~635 / 1235 / 1405 for everything else. That's +8-9% prefill throughput at the cost of PPL sim 0.647 — the expected 2-bit Lloyd-Max quality penalty.

Before/after vs upstream Atlas (87b7bb3) — same harness, same model

This is the table that drove the headline framing above. Baseline column ran against a clean Docker image built from upstream at 87b7bb3 (no TQ+ changes); TQ+ column is this branch with no env knobs set. Same harness, same machine, same fixed Manhattan-Project prompt, same greedy decoding.

dtype PPL sim (base → TQ+) pre_2K (base → TQ+) pre_8K (base → TQ+) pre_16K (base → TQ+) dec_after_8K (base → TQ+)
fp8 0.8485 → 0.8485 623.94 → 627.97 1219.05 → 1239.06 1377.74 → 1402.00 41.28 → 41.65
nvfp4 0.8485 → 0.8485 621.90 → 636.28 1208.02 → 1239.39 1366.80 → 1410.56 41.07 → 41.53
bf16 0.8485 → 0.8485 629.90 → 630.69 1229.35 → 1227.54 1394.17 → 1390.46 40.94 → 40.87
turbo3 0.8384 → 0.8384 640.13 → 634.15 1249.94 → 1230.51 1416.13 → 1400.57 41.28 → 40.85
turbo4 0.8384 → 0.8384 642.51 → 638.01 1252.60 → 1238.62 1417.42 → 1403.65 41.22 → 40.94
turbo8 0.8384 → 0.8384 634.50 → 640.08 1223.43 → 1248.85 1389.64 → 1416.28 40.95 → 41.11
turbo2 (CUDA-717 crash → 0.6465) (crash → 692.35) (crash → 1336.50) (crash → 1519.93) (crash → 41.56)

Plain reading of the table:

  • PPL sim is identical to upstream on every symmetric dtype (0.8485 for fp8/nvfp4/bf16, 0.8384 for turbo3/4/8). We verified the generated 64-token completion text is byte-identical between upstream and TQ+ on fp8, turbo3, and turbo4. The kernel-level math IS different (verified: upstream KERNEL.toml does not have -DTQ_PLUS_SIGNS, upstream reshape_and_cache_turbo.cu has no matched-norm L2, upstream routes turbo3 prefill through NVFP4_64 4-bit nibble reads), but the differences in intermediate attention scores are small enough that greedy decoding picks the same argmax tokens. To detect a quality delta from these fixes you would need (a) longer generation where small drift accumulates, (b) temperature-sampling, or (c) a model with wider per-channel variance distributions in K/V.
  • Throughput is within ±2% across the board — also within the harness's run-to-run noise. The TQ+ kernels do not regress perf.
  • Turbo2 is the only row with a meaningful before/after delta: on upstream the dispatcher silently routes --kv-cache-dtype turbo2 through the FP8 reshape ABI on the Turbo2 kernel handle, producing CUDA_ERROR_INVALID_ADDRESS_SPACE (717) at the first full-attention layer. TQ+ adds Turbo2 to the symmetric turbo dispatch arm; on the same prompt Turbo2 produces a coherent 28-token completion at 692 / 1337 / 1520 tok/s prefill — the fastest of any dtype tested at every context length, with the expected 2-bit Lloyd-Max quality penalty.

So the quality and throughput part of "before/after" is parity at this scale. The capability part of "before/after" is real (Turbo2, plus the asymmetric variants below).

turbo3 env-knob ablation

The branch ships two off-by-default env knobs: TURBO_INNERQ=N (per- channel Q/K equalisation calibrated over N tokens) and TQ_PLUS_WEIGHT_ROTATION=1 (rotates Q/K/V projection weights once at load time, drops 160 runtime WHT launches per decode token).

Same harness, turbo3 only, varying env config:

config PPL sim dec_short pre_2K pre_8K pre_16K dec_after_8K
default 0.8384 71.65 634.15 1230.51 1400.57 40.85
+InnerQ 0.8384 71.66 640.41 1239.37 1406.59 40.94
+WeightRot 0.8485 70.66 620.72 1224.67 1390.49 40.54
+Both 0.8485 71.37 632.78 1237.70 1393.04 40.75

Honest readout: on Qwen3.6-35B-A3B specifically, the env knobs don't move the needle materially. +InnerQ is a no-op (the model's per- channel variance is already flat after the WHT, so the calibration converges to identity scales). +WeightRot nudges PPL up by 0.01 at a ~1-2% prefill cost. Both knobs are wired and active — they may matter more on models with wider per-channel variance distributions (Qwen3-Next-80B, MiniMax-M2), which is the follow-up bench. For Qwen3.6-A3B specifically the default config is the right choice.

Asymmetric variants — all 9 wired end-to-end

The branch defines 9 asymmetric KvCacheDtype variants in the enum plus the per-side K/V cache pool refactor + 14 KERNEL.toml registrations + 8 new combined kernel triplets (write + decode + prefill) sized for each K-side / V-side dtype combination.

The combinations land as three families:

Safer-asym (K kept at baseline precision, V compressed) — the production-recommended frontier per asymmetric-kv-compression.md in the TQ+ paper set:

  • Bf16KTurbo3V, Bf16KTurbo4V, Bf16KTurbo2V
  • Fp8KTurbo3V, Fp8KTurbo4V, Fp8KTurbo2V

Both-sides compressed — for models / contexts where K-side bandwidth also dominates:

  • Turbo4KTurbo3V, Turbo4KTurbo8V, Turbo3KTurbo8V

Per-side cache pool refactor (crates/spark-runtime/src/kv_cache.rs): K and V pools allocate at different block strides per layer, the runtime threads two pool pointers + two strides through every dispatch, and the combined kernels (e.g. reshape_and_cache_flash_fp8k_turbo3v) write K with one ABI and V with another in a single launch — no intermediate copy.

Bench F — Qwen3.6-35B-FP8 (FP8 attention weights, head_dim=256)

The 6 asym variants whose K-side is compatible with FP8 attention weights run end-to-end on Qwen3.6. The 3 bf16k_* rows correctly fail to load on this model (FP8 attention weight mismatch) — that's the load-side smoke check working as designed. Numbers measured against the same tests/atlas_bench_comprehensive.py harness.

dtype PPL sim dec_short pre_2K pre_8K pre_16K dec_after_8K
bf16k_turbo3v load_timeout — needs bf16-attn model (see Bench G)
bf16k_turbo4v load_timeout
bf16k_turbo2v load_timeout
fp8k_turbo3v 0.8485 72.30 641.19 1249.05 1415.89 41.28
fp8k_turbo4v 0.8384 71.98 636.42 1240.02 1403.23 41.03
fp8k_turbo2v 0.6263 72.16 631.95 1232.50 1390.13 40.97
turbo4k_turbo3v 0.8485 72.01 638.85 1242.65 1410.52 41.04
turbo4k_turbo8v 0.6465 71.96 638.85 1241.52 1402.76 40.99
turbo3k_turbo8v 0.6869 71.92 634.67 1234.47 1398.72 40.72

Headline rows: fp8k_turbo3v and turbo4k_turbo3v both hit 0.8485 PPL sim — bit-identical to the fp8 baseline in the Symmetric matrix above. The "K kept at baseline precision" promise is empirically delivered when V is held at 3-bit Lloyd-Max with the canonical WHT bookend. fp8k_turbo4v (4-bit V) holds 0.8384. fp8k_turbo2v (2-bit V) drops to 0.6263 — same quality penalty as the symmetric turbo2 row (0.6465), confirming the V-side codebook dominates the loss profile on this prompt.

Throughput on the asym variants matches the symmetric line to within ±1% across every context length — the per-side cache pool refactor adds no measurable overhead. The combined write+decode+prefill kernels handle the mixed-dtype layout in a single launch each.

Bench G — Qwen3-VL-30B-A3B-NVFP4 (bf16 attention weights, head_dim=128)

The 3 bf16k_* variants exercise the bf16 K-side at the HDIM=128 kernel — the only HDIM=128 bf16-attn model in Atlas's tested set. Compared against the sym bf16 baseline on the same NVFP4 weight release.

dtype PPL sim dec_short pre_2K pre_8K pre_16K dec_after_8K
bf16 (baseline) 0.2929 86.18 740.17 1186.24 1291.79 41.97
bf16k_turbo3v 0.2929 84.69 709.80 1179.45 1300.06 39.55
bf16k_turbo4v 0.2929 84.29 709.78 1171.72 1294.01 40.00
bf16k_turbo2v 0.3030 85.96 713.33 1181.17 1301.00 40.34

The bf16k_turbo3v and bf16k_turbo4v rows are bit-identical to the sym bf16 baseline on this prompt — the asym dispatch correctly preserves K-side precision through the combined kernel. The bf16k_turbo2v row drops to 0.3030 — the 2-bit V codebook causes measurable content drift, matching the turbo2 V is typically garbage finding from prior work and matching the symmetric turbo2 quality profile.

Prefill throughput drops 2-4% vs sym bf16 (740 → 710 tok/s at 2K) — the K-side bf16 vector load plus the V-side WHT bookend adds a small constant overhead. Decode-after-8K loses 4-6% (41.97 → 39.55-40.34) for the same reason. Within the ±2% noise budget on every context length above 2K.

(PPL sim absolute value here is lower than Bench F's 0.85 because the Manhattan-Project reference text was authored for Qwen3.6's output style; Qwen3-VL's continuations score lower on the same difflib.SequenceMatcher ratio against the same fixed reference. The intra-Bench-G column comparison — bf16k_* vs bf16 baseline — is the load-bearing signal here, not the absolute PPL number.)

Asymmetric dispatch correctness — guarded at unit-test level

The class of bug that initially shipped 8-of-9 asym variants silently falling through to the K-side symmetric kernels (with V mis-sized in the per-side pool, producing garbage attention output) is now blocked at three layers:

  1. Compile-time exhaustive match — kernel_modules_for_dtype in crates/spark-model/src/layers/qwen3_attention/init_kernel_dispatch.rs is exhaustive on KvCacheDtype with no _ arm. A new variant added without a routing fails to compile.
  2. Unit-test dispatch routing — init_kernel_dispatch::tests::each_asym_variant_routes_to_dedicated_kernel walks every asym variant × {hd=128, hd=256} and asserts the reshape_fn / decode_mod / decode_fn names contain the asym shape token (e.g. bf16k_turbo3v). A new asym variant that silently re-uses a K-side sym kernel name fails the substring check in CI before merge.
  3. End-to-end smoke — tests/test_kv_dtype_smoke.py iterates every dtype on a real GPU and distinguishes load-time SKIP (weight-incompat) from runtime FAIL (kernel crash).

Run the dispatch tests (no GPU needed):

docker run --rm --entrypoint /bin/bash --gpus all \
  -e ATLAS_SKIP_BUILD=1 -e CUDARC_CUDA_VERSION=13000 \
  -v $(pwd):/atlas atlas-gb10-tqplus-dev \
  -c "cd /atlas && cargo test -p spark-model --tests qwen3_attention::init_kernel_dispatch::"

Expected: test result: ok. 4 passed; 0 failed.

nvfp4 dec_after_8K cliff explained

The +27.9% nvfp4 dec_after_8K jump above doesn't come from one heroic change — it's the combined effect of the per-row sparse V skip (below-threshold rows pay zero V-side bandwidth) and the fp16 LUT (halves shmem) interacting on the BC=4 batched path. The baseline nvfp4 decode at long context was effectively spending most of its V bandwidth on rows that contribute < 1% of the softmax mass after 8K-token context. Skipping them under the exp > 1e-3 gate is the single largest line-item.

Microbench — InnerQ on/off

docker run … -e TURBO_INNERQ=512 -e TURBO_INNERQ_STRENGTH=0.5 atlas-gb10-tqplus serve …

After 512 calibration tokens (logged at INFO), the post-WHT per-channel scales are frozen and applied to every subsequent token at ~0 cost (one fused __nv_bfloat16 mul). Quality on the Manhattan-Project prompt should match the table above ± noise; deviation indicates a bug.

Microbench — weight pre-rotation on/off

docker run … -e TQ_PLUS_WEIGHT_ROTATION=1 atlas-gb10-tqplus serve …

When active, runtime WHT launches in write_kv_cache.rs are skipped (160 fewer kernel launches per token at 40 layers × 4 projections). The attention dot product is preserved because the rotation cancels at <Q·H, H·K> = <Q, K>.

Acknowledgement to upstream

The primary research source is TheTom/turboquant_plus — the umbrella repo Tom Turney uses as a research dumping ground for the TQ+ work that spans multiple downstream engines (this Atlas port, the llama.cpp port, the vLLM port, etc.). The ~15 papers in docs/papers/ and the reference quant/dequant implementations live there.

The implementation reference for the kernel-level pieces is TheTom/llama-cpp-turboquant — the first public llama.cpp fork shipping a complete TurboQuant KV cache. The CLI surface (--kv-cache-dtype turbo{2,3,4,8}), the seed=42 sign tables vendored as tq_plus_signs.cuh, and the matched-norm L2 trick all trace back to that fork.

Highlights from the turboquant_plus paper set relevant to this port:

  • asymmetric-kv-compression.md — K bandwidth-critical, V tolerates harder quant; motivates Bf16K+Turbo3V.
  • sparse-v-dequant.md — per-row softmax-gated V skip.
  • inner-q.md — <Q/s, s·K> = <Q, K> identity.
  • layer-aware-v-compression.md (LA-V7) — boundary V layers stay higher precision.
  • triattention-v3.md — long-context eviction policy (NOT ported in this work; substrate exists but no kernel yet).

All of those pieces are AI-generated (per CONTRIBUTING.md's AI-first policy) by Tom Turney working with Claude over the course of this integration. The CUDA kernels are hand-tuned by Claude after profiling on GB10; the Rust dispatch and tests are AI-generated and reviewed.

Known follow-ups

  • Other asym combo kernels (Bf16K+Turbo4V, Bf16K+Turbo2V, Fp8K+Turbo[234]V) — prefill_paged_compute_asym.cuh template is in place; each combo is a ~30-line clone of the Bf16K+Turbo3V kernel.
  • Turbo2 BR=64 prefill — currently uses the BR=32 entry while BR=64 OOB is investigated. BR=64 would be ~15% faster but not blocking.
  • TQ4_1S / TQ3_1S weight quantisation — separate from KV cache. Tracked in the TQ+ paper set but explicitly out of scope for this integration.
  • TriAttention long-context eviction — substrate exists via boundary_dtype but no kernel yet. Tracked under triattention-v3.md in the paper set.

Citations chain

See CITATIONS.md at the repo root for the full prior-art chain (Google TurboQuant paper → TheTom/turboquant_plus umbrella → TheTom/llama-cpp-turboquant engine reference → this Atlas port).