Kevin (KVin) — Production KV-Cache Compression Engine

H100 Benchmarks — 5 Models, 13 Configurations

Tested on NVIDIA H100 80GB (RunPod Cloud) and locally via Ollama with real model tensors. Compression ratio is architecture-dependent: 3.03x for 32-layer models, 3.67x for 80-layer GQA models.

Model	Params	Context	Compression	Cosine Sim	Saved
Llama 3 70B	70B	128K	3.67x	0.876 (4K)	30.5 GB
Llama 3 70B	70B	32K	3.67x	0.808 (16K)	7.8 GB
Llama 3 70B	70B	4K	3.67x	0.876	980 MB
Qwen 72B	72B	128K	3.67x	0.875 (4K)	30.5 GB
Qwen 72B	72B	32K	3.67x	0.738	7.8 GB
Qwen 72B	72B	4K	3.67x	0.875	980 MB
Qwen3 30B	30B	4K	3.54x	0.825	771 MB
Llama 3 8B	8B	128K	3.03x	0.880 (4K)	11.2 GB
Llama 3 8B	8B	4K	3.03x	0.880	360 MB
Mistral 7B	7.3B	128K	3.03x	0.874 (4K)	11.2 GB
Mistral 7B	7.3B	16K	3.03x	0.810	1.4 GB
Mistral 7B	7.3B	4K	3.03x	0.877	360 MB

Cosine similarity at production context lengths (4K) is 0.87-0.88 across all models. Larger models benefit most — your most expensive GPUs get the biggest savings.

Memory Savings by Model & Context Length

Compression Ratio by Architecture

80-Layer GQA (70B+)

3.67x

73% memory reduction

64-Layer (30B)

3.54x

72% memory reduction

32-Layer (7B/8B)

3.03x

67% memory reduction

Key Finding: Compression Scales With Model Size

80-layer GQA models (70B/72B) achieve 3.67x compression. 64-layer models hit 3.54x. 32-layer models reach 3.03x. Your largest, most capital-intensive models benefit most from KVin.

The 30 GB Number

30.5 GB

freed per Llama 70B session @ 128K

38%

of an H100's 80 GB VRAM

3.5x

more concurrent sessions

Dashboard — What Production Looks Like

KVin ships with a live monitoring dashboard. This is what operators see when the engine is running.

KVin GPU-Native KV-Cache Orchestration Engine v0.0.25

Engine Running

Tokens Stored

1,247,832

Compression Ratio

3.54x

Arena Utilization

27%

Prefix Cache Hits

94.2%

Per-Head Precision Distribution

2-bit

52%

K4/V2

29%

4-bit

18%

Request Throughput

229K KV pairs/sec

This is a static rendering of the live dashboard. In production, all metrics update in real-time via WebSocket.

Throughput & Performance

KVin is engineered for inference-speed operation. Every component on the hot path is designed for zero-allocation, constant-time performance. CPU-path numbers measured on Windows 11 / RTX 3060 12GB. GPU benchmarks on NVIDIA H100 80GB (RunPod Cloud).

229K

KV pairs / sec (CPU)

763 ms

128K token pipeline

1-2 ns

Position lookup

O(1)

Request cleanup

malloc() Calls on Hot Path

Arena allocator uses bump-pointer allocation. O(1) batch reset between requests. No fragmentation, no GC pauses, no allocation jitter during inference. The same pattern that powered sub-microsecond latency on 2001 hardware.

1-2 ns

O(1) Position Lookup

3-level radix tree resolves any position at any sequence length up to 128K tokens in 3 pointer dereferences. Hash maps: 30-50 ns. Linear scan: unbounded. This is the difference at scale.

1 kernel

Fused Decompress + Attend

fused_attend decompresses and computes attention in a single CUDA kernel launch. No intermediate fp16 staging buffer. No extra memory round-trip. Decompress and attend are one operation.

SPSC + MPMC

Lock-Free Queues

KVinSPSCQueue for single-producer pipelines, KVinMsgQueue for multi-producer dispatch. Both lock-free, both ported from a production platform where microsecond contention was unacceptable.

Operational Stack — Ships Complete

KVin is not an algorithm. It is a fully instrumented production system. Every component needed to deploy, monitor, alert, and operate ships out of the box. No assembly required.

▣

API Server

FastAPI with metered endpoints, per-key usage tracking, tiered API keys, Stripe-ready billing. Health checks, versioned endpoints, CORS-ready. Self-serve or embed behind your existing API gateway.

▣

Live Dashboard

Real-time dark-themed monitoring: tokens stored, compression ratio, arena utilization, prefix cache hit rate, per-head entropy distribution, request throughput. WebSocket live updates. No external dependencies.

▣

Telemetry & Metrics

ScopedTimer instrumentation on every critical path. JSON stats API for Prometheus/Grafana scraping. MQTT telemetry for fleet-wide monitoring. Per-head, per-layer, per-request granularity. The same instrumentation depth that ran 24/7 in mission-critical production environments.

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System Tray Controller

Desktop sidecar: green/yellow/red health indicator, hover tooltip with live stats (tokens, compression, arena %). Right-click: status, open dashboard, open logs. Alerts operators before arena pressure becomes a problem.

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vLLM Drop-In Backend

One-line CacheEngine replacement: import kvin; kvin.activate(). Intercepts all KV cache operations transparently. Your serving layer doesn't know it's running. Zero changes to model code, prompts, or serving config.

▣

Deployment Ready

GPU-ready Dockerfile with CUDA runtime. pip-installable Python package. C++/CUDA shared library (.so/.dll). Helm chart ready for Kubernetes GPU clusters. MQTT fleet telemetry for multi-node observability.

What "Production-Ready" Actually Means

C++ tests passing (100%)

8,114

lines C++/CUDA (53 files)

custom CUDA kernels

block_k4

4-bit key quantization, block amortized

kmeans_v2

2-bit values, shared memory k-means

fused_attend

decompress + attend, single kernel

KVin ships with binary record/replay (KVinJournal) for deterministic debugging, NVMe offload for overflow to CPU/disk, and a pluggable quantizer interface (I_QuantizerInstance) so you can swap TurboQuant for KIVI, GearKV, or your own custom backend without touching the engine.

25 Years of Production Heritage

KVin is not derived from a research paper. The engineering foundation — zero-malloc arenas, lock-free queues, subject-based routing, ring-buffer caching — ran in mission-critical, high-throughput production environments for over two decades. That foundation was then extended with GPU-native optimizations: 3 custom CUDA kernels, per-head adaptive entropy, fused decompress+attend, speculative branch-and-prune, and NVMe offload — none of which existed in the original domain. Proven architecture, purpose-built extensions.

895

Heritage Source Files

The original production caching platform: framework, bus, cache engine, transport plugins, arena allocator, message queues, replication, capture/playback. All retained and available for inspection.

Direct+

Port, Plus GPU Optimizations

Every KVin component has a named, auditable ancestor in the heritage codebase. Then extended: 3 custom CUDA kernels, per-head adaptive entropy, fused decompress+attend, speculative branch-and-prune, NVMe offload tier. The production foundation is proven; the GPU extensions are net-new engineering.

Architecture

Inference Engine (vLLM / TensorRT-LLM / SGLang) | KVin AttentionBackend ← drop-in, one import | KVinEngine / | | \ Bus LVC Store Arena route fp16 compressed zero-malloc | | | | Quantizer Plugins (TurboQuant / KIVI / GearKV / custom) | CUDA Kernels (block_k4, kmeans_v2, fused_attend)

Bus: Subject-based message router dispatches per-(layer, head) precision policies. Each attention head gets its own quantization strategy based on entropy.

LVC: Per-head ring buffer keeps the last 128 tokens at full fp16. Quality-critical recent attention is never degraded.

ContextStore: Compressed KV storage organized by layer/head hierarchy. Append-only, arena-allocated.

Arena: Bump-pointer allocator for both CPU and CUDA. Zero malloc on the hot path. O(1) cleanup between requests.

Adaptive precision: KVin measures per-head Shannon entropy in real time. Low-entropy heads (sharp attention) drop to 2-bit. High-entropy heads stay at 4-bit. 52% of heads in Qwen3 30B compress to 2-bit automatically — no configuration.

Component Lineage

Every KVin component traces to a production ancestor — then extends it with GPU-native optimizations that didn't exist in the original domain.

Heritage (Production)	KVin (GPU)	Role
Framework::Bus	KVinBus	Subject-based routing + precision policies
LastValueCache	KVinLVC	Per-head fp16 ring buffer (recent tokens)
TasCache	KVinContextStore	Compressed sequence storage (layer/head)
TasTickMemory	KVinArena	Bump-pointer allocator (CPU + CUDA)
TasSeqMap (radix)	KVinPositionIndex	O(1) position lookup (1-2 ns @ 128K)
I_TransportInstance	I_QuantizerInstance	Pluggable compression backends
MsgQueue<T>	KVinMsgQueue	MPMC + SPSC lock-free queues
SDObject::Record	KVinRecord	Copy-on-Write typed metadata carrier
Capture/Playback	KVinJournal	Binary record/replay for debugging
Multicast handler	KVinSpeculative	Branch-and-prune for speculative decoding

Every component has a direct lineage to a production-proven ancestor. No pattern was invented from a paper.

Why Acquire Instead of Build

Factor	Build from Paper	Acquire KVin
Time to production	6-12 months, 2-3 senior GPU engineers	Immediate (89 tests passing)
Engineering cost	$500K-$1.5M salary burn	Fraction of that
Memory management	malloc/free, fragmentation	Arena allocator, zero-malloc hot path
Position lookup	Linear scan or hash table	Radix tree, 1-2 ns O(1)
Adaptive precision	Not addressed in papers	Per-head entropy-driven, automatic
Cross-request reuse	Not addressed	Hash-based prefix cache + LRU
Speculative decoding	Separate concern	Integrated branch-and-prune
Observability	None	API + MQTT + live dashboard
Plugin interface	Hardcoded quantizer	Abstract interface, swap quantizers

Integration

vLLM (Two Lines)

import kvin kvin.activate() # vLLM now uses KVin for all KV-cache operations. # Dashboard: http://localhost:8099

Custom Inference Stacks (C API)

// 1. Initialize with model config KVinEngine* engine = kvin_init(layers, heads, head_dim); // 2. Store new KV tensors on each forward pass kvin_store(engine, layer, key_tensor, value_tensor, seq_len); // 3. Fused decompress + attend in one kernel launch kvin_attend(engine, query, output); // 4. O(1) cleanup between requests kvin_reset(engine);

Integration timeline: 1-2 weeks for basic deployment. 4-6 weeks for full production hardening with custom precision policies.

KVin