Two cost centers, one compression. Model the fleet-level economics of Syntropic KV-cache compression — GPU serving and vector search — then pick the view that matches your deployment.
Illustrative model — not a measured Syntropic claim. Scales per-GPU unit economics across a fleet of memory-bound serving GPUs. The big saving is needing fewer GPUs once compression removes the memory pressure. Choose how GPU cost is paid (rented all-in, or owned + power) so energy isn't double-counted. Real figures are telemetry-verified per deployment; in compute-bound workloads savings fall toward zero.
Total estimated annual savings — full fleet
$16.4M
Basis: ~$16,425 / serving GPU / year × 1,000 GPUs
$2.1M
Syntropic revenue · 12.5% of savings
750
GPUs eliminated
525 kW
GPU power saved
75.0%
GPU power reduction
Fleet size
Total GPUs serving memory-bound inference in the deployment / data center
Per-GPU drivers
4.0×
How memory-limited vs compute. 1 = compute-bound → no memory savings
8.0×
Realized KV memory reduction
700 W
H100 SXM class ≈ 700 W
Cost & physical assumptions
Neocloud rental ($/GPU-hr) already includes power — energy is shown as footprint, not added.
Energy & carbon detail (full fleet)
683 kW
Facility power saved (×PUE)
6.0 GWh
Annual energy saved
2,391 t
Annual CO₂ avoided
657 W
Spillover-movement power saved — smallest term
Sensitivity — GPU power reduction vs memory-bound factor
What Syntropic delivers
More users & agents per GPU — frees the memory the KV cache was eating; one GPU serves far more concurrent sessions.
Fewer GPUs for the same workload — the dominant cost saving: lower rental and capex in memory-bound serving.
Longer context windows — push back the memory wall without adding hardware.
Less GPU↔CPU spillover — fewer stalls and lower latency; less data moved across the slow bridge.
Lower energy & carbon per token — more of the fixed power budget goes to revenue tokens.
Language-agnostic — compresses tensors, not language.
Encrypted, per-tenant KV — compression with per-user key isolation (AES-256).
Aligned pricing — a percentage of telemetry-verified savings; Syntropic only wins when the customer does.
How it scales: savings are computed per memory-bound serving GPU, then multiplied by fleet size. Consolidation = the smaller of compression and memory-bound factor. In "Rented" mode the dollar total is avoided rental (power already inside it); in "Owned" mode it is avoided hardware + separately-billed electricity.
Patents pending. Confidential — internal modelling & investor discussion. Defaults: H100 SXM ~700 W, rental ~$2.40–$3.00/GPU-hr (GMI/Lambda/SemiAnalysis, 2026); off-chip data movement ~10–35 pJ/bit (SemiAnalysis/Intel/Horowitz); PUE ~1.3; representative grid carbon. Reference scale: a 50,000-GPU cluster ≈ 35 MW. Continuous model; figures derived from adjustable assumptions, not validated benchmarks.
Illustrative model — not a measured Syntropic claim. Vectors (embeddings) must sit in RAM for fast search — that memory is the cost. Syntropic compresses them and searches in compressed form. Validated: 0.96 recall@10 at ~8× on 1,536-dim OpenAI embeddings. The dollar figures below are illustrative; the compression is measured.
Estimated annual RAM savings — full index
$516K
$65K
Syntropic revenue · 12.5% of savings
1.5 TB
RAM after compression (was 12.3 TB)
1.30M
Vectors per GB (was 163K)
87.5%
RAM reduction
Index size
Total embeddings held resident for search across the deployment
Drivers
1536
OpenAI text-embedding-3-large = 3072 · -small = 1536 · many open models = 768
8.0×
Validated 8× at 0.96 recall@10 · higher ratios trade some recall
2.0×
Copies kept resident for reliability / throughput
RAM cost assumptions
Memory-optimized cloud capacity typically runs ~$3–5 / GB / month all-in. Compression applies to the resident index; search runs directly on compressed vectors.
Cost: before vs after
$590K
Annual RAM cost — uncompressed
$74K
Annual RAM cost — with Syntropic
Savings by compression ratio
How it reads. Same searches, same answers — the index simply holds in a fraction of the RAM. The saving scales linearly with vector count and dimension, so the largest indexes save the most. Recall is the measured part; the dollar translation is illustrative and is telemetry-verified per deployment in practice.