Tear down the memory wall.
Replace copy-heavy data infrastructure with a single GPU-native runtime. One representation. Every access pattern. No reformatting.
1B
Entries validated
100%
Recall
38.5 ms
p50 latency
24 hours
Soak test
The problem
Data isn't expensive to store.
It's expensive to move.
Every access pattern demands its own copy in its own format. The data is the same. The cost is the translation.
Compute
Processor time
Every transformation, score, index build, and model run burns CPU or GPU seconds.
Memory
RAM
Fast, volatile, expensive. Sub-millisecond latency means you're paying for RAM.
Storage
Disk
Cheapest resource. Data sits here and waits. The cost per GB is almost nothing.
Network
Data transfer
Every replication, fan-out, and cross-AZ copy. The most invisible line on every bill.
What happens to 200 TB of production data
200 TB
Raw data
Kafka
Avro
ETL
3-4 fmts
Feature Store
Float32
Vectors
1536-dim
Search
Inverted
Redis
JSON
Pipeline
TFRecord
DR
Parquet
ETL out
CSV
Nine copies. Nine formats. Same data.
Storage is 6% of the real cost. The other 94% is reformatting.
The solution
One format. Every consumer.
HX-SDP stores data in a structural representation that natively supports every access pattern. No reshaping. No copies.
Caching
Sub-millisecond retrieval from GPU VRAM. No Redis. No Memcached.
Vector search
100% recall at 38.5ms p50. Native to the same representation.
Feature serving
ML features served directly from the structural store. No feature store.
Full-text search
Text queries against the same data. No Elasticsearch. No copy.
Today
14
services
9
copies
9
formats
With HX-SDP
5
services
1
copy
1
format
The engine
3.35 TB/s vs 7 GB/s.
Every query that touches disk pays a 450x bandwidth tax. QTT restructures data into compressed tensor form that fits entirely in VRAM —277 bytes per entry at fp64.
GPU HBM3
3.35 TB/s
NVMe SSD
7 GB/s
450x
Every query that touches disk pays this tax
Precision tiers
Pick your precision.
Exact
fp64
277 bytes/entry
100% recall
Up to 200M entries
Healthcare, pharma, science.
Production
fp32
132 bytes/entry
100% recall
Up to 500M entries
Enterprise production workloads.
Scale
fp16
66 bytes/entry
100% recall
Up to 2B entries
High-volume workloads.
Evidence
Signed artifacts. Public benchmark.
1B
entries
100%
recall
38.5 ms
p50 latency
24 hours
soak test
29
artifacts
1K to 1B entries
NVIDIA H100 80GB HBM3. ML-DSA-65 signed.
109/109 operations audited
Every operation verified. No hidden assumptions.
Status