Deterministic GPU Continuous Batching Heuristic

Stop Burning GPU Budgets with Naive FCFS Scheduling.

Eliminate KV-cache starvation, reduce P99 SLA latency by up to 18.0 ms, and recover up to 16.4% of GPU inference capacity without buying more hardware.

Goodput (SLA Respect)

39.0%

vs FCFS standard (33.5%)

P99 Latency SLA

2044.0 ms

vLLM default was 2062.02 ms

GPU Cluster Savings

+16.4%

Fewer GPUs for same load

Algorithm Status

Predictive-Throughput-Optimiser-v3.0

SHA: 9ef0c044ac9f6880...

Interactive FinOps Engine

GPU Cluster ROI & Cloud Savings Calculator

Estimate annual enterprise cloud infrastructure savings unlocked by adopting TokenBatch dynamic scheduling.

GPU Model Selection

Cluster Size (Active GPUs): 16 GPUs

Hourly Cost per GPU ($): $3.50/hr

Projected Annual Savings

$166,615 / yr

Equivalent to saving 5 idle GPUs

One-time or recurring license pays for itself within less than 2 weeks of production traffic.

Deploy TokenBatch Engine →

In-Browser Real-Time Emulation

Live 500-Request Burst Simulator

Watch standard FCFS suffer from head-of-line blocking while TokenBatch preserves strict P99 deadlines.

Industry Default (FCFS / FIFO) Ready

Completed

0 / 500

SLA Met (Goodput)

--

KV Evictions

--

TokenBatch-Lab Engine Ready

Completed

0 / 500

SLA Met (Goodput)

--

KV Evictions

0

Enterprise Deliverable

Zero-Dependency Drop-in For Any LLM Router

Upon activation, you receive the raw Python production core (algorithme_champion.py) and the complete executive white-paper (RAPPORT_EXECUTIF_GPU_LLM.md).

✓ Compatible with vLLM custom scheduling policies
✓ Compatible with Triton Inference Server BLS & Python backends
✓ Zero heavy local dependencies (pure Python standard library + numpy)
✓ Cryptographically signed with immutable SHA-256 validation

Unlock Full Python Core on Polar.sh →

Package Livrable (Polar.sh Instant Download)

├── algorithme_champion.py      # Noyau mathématique pur & Router vLLM
├── RAPPORT_EXECUTIF_GPU_LLM.md # Audit FinOps H100/A100 & Preuves P99
└── PROSPECTS_DU_JOUR.md        # Dossier d'intégration MLOps

Signature d'Intégration Python (8 Lignes) Verified SHA-256

def score_request_priority(
    prompt_len: float, est_decode_len: float, wait_time_ms: float,
    sla_target_ms: float, free_kv_blocks: float, active_batch_tokens: float,
    prefix_shared_ratio: float
) -> float:
    # [PROPRIETARY MATHEMATICAL CORE - UNLOCKED IN COMMERCIAL LICENSE]
    # Algorithm: Predictive-Throughput-Optimiser-v3.0 | SHA-256: 9ef0c044ac9f6880e72251eb...
    return float(optimal_gittins_sla_priority_score)