Service
SLM infrastructure
Pain
A trained SLM is only useful when inference is reliable: right-sized GPUs, observable latency and errors, safe promotion of new versions, and clarity on who operates the stack when something breaks at 2 a.m.
Outcome
We help you stand up serving on your own metal or cloud accounts (Path A), or source dedicated GPU capacity through SLM-Works where that fits your procurement model (Path B) - with runbooks, monitoring hooks, and explicit responsibility splits in the statement of work.
Differentiator
We document who runs hosts, networks, backups, and escalation paths before go-live. Marketing copy here is descriptive; binding SLAs and resale terms exist only in signed agreements.
Legal and commercial review
Language about GPU resale, partner data centres, capacity commitments, and SLA splits on this page is draft until your counsel and ours approve it for public use. Do not treat this page as a contractual offer. Request the latest commercial pack during sales discussions.
Path A. On-premises or your cloud accounts
You retain ownership of subscriptions, regions, and access policies. We install and configure serving runtimes, wire observability into your toolchain, and hand over artifacts your platform team can repeat.
- Install and baseline inference stacks aligned to your security baseline (containers, VM images, or managed services you approve).
- Integrate with identity, logging, and metrics systems you already operate.
- Define promotion paths from staging to production with rollback checkpoints.
- Produce runbooks for scaling events, certificate rotation, and model version swaps.
Path B. Dedicated GPU capacity via SLM-Works
When you prefer to buy capacity through SLM-Works rather than contracting hyperscaler resources directly, we can arrange dedicated GPU footprints with partner providers. Commercial terms, regions, and uptime targets are specified in contract - not on this website.
- Capacity is framed as dedicated resources for your workloads - not shared public API pools.
- Roles are split explicitly: what SLM-Works operates end-to-end, what you operate (applications, data ingress), and what the underlying facility provides (power, physical security, host SLA).
- Network and data-residency choices are agreed before workloads move.
- Billing and change windows follow the ordering document; this page does not quote prices or SLAs.
Architecture at a glance
Illustrative comparison only - your signed architecture diagram supersedes this figure.
SLM infrastructure
On-prem footprint vs dedicated capacity
Support and operations matrix
Typical split of responsibilities; the statement of work and runbooks are authoritative.
| Area | SLM-Works | Customer | Provider (Path B) |
|---|---|---|---|
| Model artifacts & version promotion | Defines promotion playbooks; assists cutover | Approves releases; owns business risk | N/A unless hosted registry is bundled |
| Inference runtime & model serving config | Implements baseline; documents tuning knobs | Owns change control in prod | Host OS / hypervisor only (Path B) |
| GPU / accelerator hosts | Specifies sizing; may procure in Path B | Owns or approves SKUs (Path A) | Physical hardware & facility (Path B) |
| Monitoring & alerting | Integrates dashboards & SLO templates | Owns on-call rosters & escalation | Facility/host metrics per contract |
| Backups & disaster recovery (non-model data) | Documents recommended patterns | Owns policies & execution | May offer snapshots per SKU (Path B) |
| Incident response (P1) | Participates per support tier in SOW | Incident commander for product impact | Per facility runbooks (Path B) |
Plan capacity, ownership, and observability with us
Start with discovery; we bring reference patterns - not a one-size-fits-all appliance.
Related on this site
Frequently asked questions
Practical answers for technical buyers; validate resale, SLA, and capacity wording with legal and sales before public launch and paid campaigns.
When should we choose Path A versus Path B?
Who owns the GPUs in Path B?
What SLAs apply?
Can you run inside our existing Kubernetes platform?
How do we monitor latency and errors in production?
How does this relate to custom SLM development?
Which regions or countries are supported?
Ready to align infra, procurement, and on-call ownership?
Use the contact page for a PoC or a short discovery call.