Starter-kit pricing depends on installation environment and requirements. Hardware, setup, support and model terms must be confirmed; no USD/token or per-question rate is established.
Published model name is not a verified API request selector.
Access
GPTQ quantization named gemma-3-Ricoh-27b-20251030-gptq; provider announced preinstallation with Dify. Hardware and commercial configuration are quote-specific.
Regions
Customer-selected private environment; region and residency depend on the deployment agreement.
Endpoint pricing has not been collected. Check the provider for applicable rates.
Data & enterprise terms
Retention, training use, regional processing and enterprise terms have not been summarized for this endpoint.
Caching
Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.
01 / INITIAL INPUTProcess & retain
The system processes eligible input and retains reusable state.
02 / LATER REQUESTCheck available state
Reuse depends on matching input, session state and the cache policy.
03 / CACHE HITReuse & generate
Reusing state can reduce repeated work. Any charges follow the billing rules below.
Ricoh private deployment
Not verified
How it works
Not collected
Minimum reusable input
Not collected
Lifetime & refresh
Not collected
How to enable it
Not collected
Usage fields
Not collected
Billing rules
Not collected
Limits & data boundaries
Not collected
No verified cache rate for this endpoint. An empty rate is not a zero charge.
Caching reviewed 14 Oct 2026
Ricoh / Fsas Private AI Platform on PRIMERGY
Not verified
How it works
Not collected
Minimum reusable input
Not collected
Lifetime & refresh
Not collected
How to enable it
Not collected
Usage fields
Not collected
Billing rules
Not collected
Limits & data boundaries
Not collected
No verified cache rate for this endpoint. An empty rate is not a zero charge.
Caching reviewed 14 Oct 2026
What the measurements say
Japanese MT-Bench / base
Ricoh · vendor-reported
9.26score / 10
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
December 2025 provider table. ELYZA scores are multiplied by two. Original and GPTQ observations are distinct configurations, not distinct foundation models.
Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.
The record behind the model
Why it is included
Provider identifies this separately trained model for research or private deployment. No matching public derivative was verified in the two provider-linked weight inventories. Closed-weight classification is inferred for this exact revision, not its public upstream base; private distribution terms remain to be confirmed.
