aither-kvcache 1.3.1

Near-optimal KV cache quantization + graph-aware eviction for LLM inference

aither-kvcache

Near-optimal KV cache quantization for LLM inference. Implements the TurboQuant algorithm from Zandieh et al. (arXiv:2504.19874).

Compresses KV cache vectors to 2-4 bits per value with MSE within 2.7x of the information-theoretic lower bound. No calibration data. No retraining. Works on streaming tokens.

Installation

pip install aither-kvcache            # core library
pip install aither-kvcache[vllm]      # + vLLM plugin (v0.15+)
pip install aither-kvcache[triton]    # + fused GPU kernels
pip install aither-kvcache[all]       # everything

Quick Start

from aither_kvcache import TurboQuant

tq = TurboQuant(head_dim=128, bits=4, device="cuda")

packed, norms = tq.encode(kv_vectors)   # [..., 128] float16 -> [..., 64] uint8 + [...] f32
decoded = tq.decode(packed, norms)       # [..., 64] uint8 + [...] f32 -> [..., 128] float16

vLLM Integration

Works with vLLM v0.15+ via the official plugin system. No monkey-patching.

pip install aither-kvcache[vllm]
vllm serve your-model --attention-backend CUSTOM

The plugin auto-registers at startup in all vLLM processes (API server + engine workers) via Python entry points. It provides:

• TurboQuantBackend: registered as the CUSTOM attention backend
• TurboQuantImpl: fused TQ decode (single-token) + standard Triton prefill (multi-token)
• TQGPUCache: GPU-resident TQ-compressed KV storage with DDR5 cold tier (spill/warm)
• ColdTierCache: Phase 1 fallback -- async background GPU-to-CPU TQ encode

Decode reads directly from TQ-compressed GPU storage -- no decompression buffer. 3.8x compression vs FP16 at 4-bit, up to 7.1x at 2-bit (1.9x vs FP8 at 4-bit).

# Env vars:
AITHER_TQ_BITS=4              # 2, 3, or 4 (default: 4)
AITHER_TQ_FUSED=1             # 1 = fused decode, 0 = standard fallback
AITHER_TQ_EAGER=0             # 0 = torch.compile+CUDA graphs (recommended)
AITHER_TQ_FORCE_TRITON=1      # Required on Blackwell (SM_100+)

Validated: RTX 5090 (Blackwell SM_120) -- 23.6 tok/s single-request, 120 tok/s 5x concurrent, 27,541 KV blocks (3.8x vs FP8), 7/7 CUDA graphs captured. 174 unit tests + 38 integration tests.

Hook-based integration (v0.9.1+)

For maximum performance with torch.compile + CUDA graphs, use the hook-based approach instead of the custom backend. This monkey-patches TritonAttentionImpl.forward() to intercept encode/decode without registering a custom backend (avoids Inductor corruption bugs):

from aither_kvcache.vllm.hooks import apply_tq_hooks

# Call AFTER vLLM model is loaded:
apply_tq_hooks()

The hook path merges encode + fused attention into a single @torch.compiler.disable call per layer, eliminating redundant graph breaks and CPU-GPU synchronization. Measured: 40 tok/s single-request decode on RTX 5090, up from 11 tok/s with separate encode/decode calls.

# Or register the plugin-based backend:
from aither_kvcache.vllm import register
register()

Hybrid modes (tq35/tq25) -- v1.0+

Split-group quantization with QJL residual encoding. Better quality at the same compression ratio as uniform TQ:

from aither_kvcache import HybridTurboQuant

htq = HybridTurboQuant(head_dim=128, mode="tq35", device="cuda")
htq.calibrate_uniform()
packed = htq.encode(kv_vectors)   # single tensor, norms embedded
decoded = htq.decode(packed)

Mode	Avg Bits	Strategy
tq35	3.5	50% dims @ 4-bit + 50% @ 3-bit (MSE + QJL)
tq25	2.5	25% dims @ 3-bit + 75% @ 2-bit (MSE + QJL)

PRIMARY mode -- TQ IS the KV cache

Instead of maintaining a shadow copy, PRIMARY mode patches vLLM to allocate TQ-compressed blocks directly. 3.8x more blocks for the same VRAM budget:

export AITHER_TQ_MODE=tq4-primary
export AITHER_TQ_BITS=4

Requires engine patches + hook-based integration:

from aither_kvcache.vllm.engine import apply_tq_patches
from aither_kvcache.vllm.hooks import apply_tq_hooks

apply_tq_patches(bits=4)  # Before vLLM starts
apply_tq_hooks()           # After model loads

Or use the provided sitecustomize hook for automatic patching:

cp $(python -c "from aither_kvcache.vllm import sitecustomize; print(sitecustomize.__file__)") /path/to/sitecustomize.py
export PYTHONPATH="/path/to:$PYTHONPATH"
vllm serve your-model --attention-backend TRITON_ATTN --compilation-config '{"cudagraph_mode":"piecewise"}'

Zero graph breaks -- full CUDA graph support (v1.0+)

All modes now register torch.library.custom_op for zero-graph-break decode:

Mode	Custom Op	Decode Strategy
tq4/tq3/tq2	tq::decode_step	Fused rotated-domain Triton attention
tq35/tq25	tq::hybrid_decode_step	Clamp-gather decompress + masked SDPA

Requires PyTorch 2.4+. Falls back to @torch.compiler.disable graph breaks on older PyTorch.

Where This Fits

Custom inference loop

If you manage your own KV cache, drop encode() where you write and decode() where you read:

from aither_kvcache import TurboQuant

tq = TurboQuant(head_dim=128, bits=4, device="cuda")

# Write to cache: compress
packed, norms = tq.encode(key_proj)       # [batch, heads, 128] -> [batch, heads, 64] uint8

# Read from cache: decompress
key_restored = tq.decode(packed, norms)   # -> [batch, heads, 128] float16

Paged KV cache

Works with block-structured caches (like vLLM's). Handles arbitrary batch dimensions:

# Compress a block of 16 tokens across 8 heads
block = cache[block_idx]                   # [16, 8, 128]
packed, norms = tq.encode(block)           # [16, 8, 64] uint8 + [16, 8] f32
restored = tq.decode(packed, norms)        # [16, 8, 128]

Zero-buffer fused attention

Compute attention directly from compressed data without ever decompressing:

from aither_kvcache.fused_attention import TQPagedAttention

attn = TQPagedAttention(tq, num_query_heads=32)
output = attn.forward(
    query, k_packed, k_norms, v_packed, v_norms,
    block_tables, context_lens,
)

The math: rotate the query forward once, dot-product in the rotated domain against codebook-decoded values, accumulate weighted values in the rotated domain, rotate back once. Two matrix multiplies total regardless of context length.

Uses fused Triton kernels on GPU (Ampere through Blackwell). Falls back to PyTorch reference on CPU. Set AITHER_TQ_FORCE_TRITON=1 on Blackwell (SM_120) GPUs -- validated on RTX 5090 at 26 tok/s.

Research / benchmarking

tq = TurboQuant(head_dim=128, bits=4)
print(tq.validate(num_vectors=50000))

python -m turboquant.bench

Compression Ratios

For head_dim=128:

Bits	Bytes/vector	vs FP16	vs FP8
4	68	3.8x	1.9x
3	52	4.9x	2.5x
2	36	7.1x	3.6x

Validated MSE

Bits	MSE	Theory Lower	Theory Upper	Ratio to LB
4	0.0095	0.0039	0.0184	2.4x
3	0.0345	0.0156	0.0736	2.2x
2	0.1175	0.0625	0.2945	1.9x

Algorithm

1. Normalize: extract L2 norm, project onto unit sphere
2. Rotate: multiply by a fixed random orthogonal matrix (data-oblivious). Makes each coordinate ~N(0, 1/d).
3. Quantize: each coordinate via precomputed Lloyd-Max codebook
4. Pack: indices into uint8 bytes
5. Store: packed bytes + float32 norm

Decoding reverses steps 4-1.

API Reference

class TurboQuant:
    def __init__(self, head_dim=128, bits=4, seed=42, device="cuda", ...)
    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]
    def decode(self, packed: Tensor, norms: Tensor) -> Tensor
    def validate(self, num_vectors=10000) -> dict
    def benchmark(self, num_vectors=32768) -> dict
    def compression_ratio(self) -> float
    def memory_report(self, seq_len, num_layers=32, num_kv_heads=8) -> dict

class HybridTurboQuant:
    def __init__(self, head_dim=128, mode="tq35", seed=42, device="cuda")
    def calibrate_uniform(self, num_kv_heads=1)
    def calibrate(self, sample_vectors: Tensor)
    def encode(self, x: Tensor) -> Tensor           # packed only (norms embedded)
    def decode(self, packed: Tensor) -> Tensor
    def validate(self, num_vectors=10000) -> dict
    def compression_ratio(self) -> float

    @staticmethod
    def packed_dim_for_mode(head_dim: int, mode: str) -> int

class TQPagedAttention:
    def __init__(self, tq: TurboQuant, num_query_heads: int)
    def forward(self, query, k_packed, k_norms, v_packed, v_norms,
                block_tables, context_lens, block_size=16) -> Tensor

Graph-Aware KV Cache Eviction (v1.1+)

Standard KV cache eviction (LRU/FIFO) doesn't know the difference between your system prompt and throwaway generation tokens. KVCacheGraph builds a relationship graph over physical KV cache blocks so eviction decisions understand what the blocks actually mean.

Quick Start

from aither_kvcache import KVCacheGraph, GraphEvictionAdvisor, EdgeType

# 1. Create the graph — protect system prompt blocks from eviction
graph = KVCacheGraph(protected_sources={"system", "tools"})

# 2. Register blocks as they enter the KV cache
graph.add_block(0, "system", importance=0.95, token_range=(0, 16))
graph.add_block(1, "system", importance=0.90, token_range=(16, 32))
graph.add_block(2, "user",   importance=0.60, token_range=(32, 48))
graph.add_block(3, "assistant", importance=0.30, token_range=(48, 64))

# 3. Feed attention patterns — edges form automatically
graph.on_attention_step([0, 1, 2, 3])   # track co-attendance
graph.on_temporal_sequence([2, 3])       # sequential generation
graph.on_prefix_hit("req_42", [0, 1])   # prefix cache reuse

# 4. Ask who to evict (system blocks are structurally protected)
victims = graph.suggest_eviction(n_blocks=2)
# -> returns least-connected, lowest-importance, non-protected blocks

# 5. Ask what to prefetch from cold tier
graph.on_spill([3])  # block 3 moved to DDR5
prefetch = graph.suggest_prefetch(active_block_idxs=[0, 1, 2])
# -> returns spilled blocks that are graph-neighbors of active set

Background Advisor (zero decode-path overhead)

For hot inference loops where you can't afford graph queries on the decode path:

from aither_kvcache import GraphEvictionAdvisor

advisor = GraphEvictionAdvisor(graph, interval=0.5, max_stale=2.0)
advisor.start()  # background thread recomputes rankings every 0.5s

# Hot decode path — lock-free, zero overhead:
candidates = advisor.get_eviction_candidates(n=16)   # pre-computed list or None
prefetch = advisor.get_prefetch_candidates([0, 1], n=8)  # graph neighbor lookup

advisor.stop()

The advisor pre-computes eviction rankings on a background thread. The decode path reads an atomically-swapped reference — no lock, no mutex, no blocking. If the ranking goes stale (>2s), returns None and the caller falls back to FIFO.

How Eviction Scoring Works

The suggest_eviction() method scores every non-protected, non-spilled block:

score = age × 0.01          # older = more evictable
      − degree × 5.0        # more graph connections = keep
      − edge_weight × 2.0   # stronger edges = keep
      − importance × 20.0   # higher importance = keep
      − hit_count × 3.0     # more prefix cache hits = keep

Protected source labels are excluded entirely — they cannot be eviction candidates.

Six Edge Types

Edge Type	Created By	Meaning
PREFIX_SHARE	on_prefix_hit()	Blocks reused across requests
CO_ATTEND	on_attention_step()	Blocks frequently attended together
SEMANTIC	add_block(embedding=...)	Similar key vector embeddings (cosine > 0.8)
TEMPORAL	on_temporal_sequence()	Consecutive in same generation
SPILL_LINK	on_spill() / on_warm()	Hot ↔ cold tier tracking

Integration with Any Inference Engine

The graph has no vLLM dependency. It works with any paged KV cache:

1. Call add_block() when blocks are allocated
2. Call remove_block() when blocks are freed
3. Call on_attention_step() with active block indices each decode step
4. Call suggest_eviction() when you need to free VRAM
5. Call suggest_prefetch() to warm cold-tier blocks preemptively

API

class KVCacheGraph:
    def __init__(self, protected_sources={"system"}, coattend_threshold=3,
                 semantic_threshold=0.8)
    def add_block(self, block_idx, source_label, importance, token_range,
                  embedding=None) -> KVBlockNode
    def remove_block(self, block_idx) -> None
    def add_edge(self, source, target, edge_type, weight=1.0) -> Optional[KVEdge]
    def on_attention_step(self, active_block_idxs: List[int]) -> None
    def on_prefix_hit(self, request_id: str, block_idxs: List[int]) -> None
    def on_spill(self, block_idxs: List[int]) -> None
    def on_warm(self, block_idxs: List[int]) -> None
    def on_temporal_sequence(self, block_idxs: List[int]) -> None
    def suggest_eviction(self, n_blocks, protect_sources=None) -> List[int]
    def suggest_prefetch(self, active_block_idxs, max_suggestions=16) -> List[int]
    def neighbors(self, block_idx, edge_type=None, max_depth=1) -> Set[int]
    def subgraph(self, block_idxs) -> Dict
    def get_stats(self) -> Dict

class GraphEvictionAdvisor:
    def __init__(self, graph=None, interval=0.5, max_stale=2.0, eviction_batch=256)
    def start(self) -> None
    def stop(self) -> None
    def get_eviction_candidates(self, n: int) -> Optional[List[int]]
    def get_prefetch_candidates(self, active_block_idxs, n=8) -> Optional[List[int]]
    def get_stats(self) -> Dict

def reorder_by_ranking(block_indices: List[int], ranked: List[int]) -> List[int]

Benchmarks

KV Cache Memory by Model (at 32K context)

Model	Layers	KV Heads	FP16	FP8	TQ4 (4-bit)	TQ3 (3-bit)	TQ2 (2-bit)
Llama 3.1 8B	32	8	4.0 GB	2.0 GB	1.1 GB	0.8 GB	0.6 GB
Mistral 7B v0.3	32	8	4.0 GB	2.0 GB	1.1 GB	0.8 GB	0.6 GB
Qwen2.5 14B	40	8	5.0 GB	2.5 GB	1.3 GB	1.0 GB	0.7 GB
Llama 3.1 70B	80	8	10.0 GB	5.0 GB	2.7 GB	2.0 GB	1.4 GB
Qwen2.5 72B	80	8	10.0 GB	5.0 GB	2.7 GB	2.0 GB	1.4 GB

KV Cache Memory by Context Length (Llama 3.1 8B)

Context	FP16	FP8	TQ4	TQ3	TQ2
8K	1.0 GB	512 MB	272 MB	208 MB	144 MB
32K	4.0 GB	2.0 GB	1.1 GB	0.8 GB	0.6 GB
128K	16.0 GB	8.0 GB	4.3 GB	3.3 GB	2.3 GB

Decode Throughput (RTX 5090, Llama 3.1 8B)

Integration	Single Request	5x Concurrent	CUDA Graphs
Hook mode (recommended)	40 tok/s	120 tok/s	7/7 captured
Plugin mode (CUSTOM backend)	23.6 tok/s	120 tok/s	7/7 captured
Baseline (FP8, no TQ)	45 tok/s	130 tok/s	7/7 captured

Hook mode reaches ~89% of baseline FP8 throughput while storing 3.8x more KV cache blocks.

Max Context Window (RTX 5090 32GB, single model)

Shows maximum tokens that fit in KV cache VRAM after model weights.

Model	Weights	FP8	TQ4	TQ3	TQ2
Llama 3.1 8B (util=0.90)	~5 GB	353K	665K	869K	1.26M
Qwen2.5 14B (util=0.90)	~9 GB	247K	466K	609K	880K
Llama 3.1 70B (util=0.90)	~37 GB	N/A	N/A	N/A	N/A

70B requires multi-GPU or offloading — KV savings still apply per-GPU.

Quantization Quality (head_dim=128, 50K vectors)

Mode	Avg Bits	MSE	Compression vs FP16	Compression vs FP8
TQ4	4.0	0.0095	3.8x	1.9x
tq35	3.5	0.0130	4.4x	2.2x
TQ3	3.0	0.0345	4.9x	2.5x
tq25	2.5	0.0520	5.8x	2.9x
TQ2	2.0	0.1175	7.1x	3.6x

All MSE values within 2.7x of the information-theoretic lower bound (matches paper).

Run python -m aither_kvcache.bench to reproduce on your hardware.

Quickstart Notebook

See notebooks/vllm_quickstart.ipynb for a step-by-step walkthrough covering installation, validation, vLLM integration, and graph-aware eviction.

Reference

@article{zandieh2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate},
  author={Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  journal={arXiv preprint arXiv:2504.19874},
  year={2025}
}

Community

• GitHub Discussions — Questions, ideas, show & tell
• GitHub Issues — Bug reports and feature requests

License

CC BY 4.0

vLLM Integration (upstream)

Native vLLM integration is in progress via PR #39008:

# Once merged:
vllm serve your-model --kv-cache-dtype tq4

Until then, use the hook-based or plugin-based integration from this package (see above).