AMD-AGI / AMD-AGI/Infera

Infera Development Roadmap (2026 Q3)

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Dominant language
Python
Stars
21
Forks
4
Avg merge
2d 7h
Merged PRs (30d)
28

Description

Infera Development Roadmap (2026 Q3)

Planned work by category — current state and the target.

Category Item Today Goal
Model support Multimodal (image / audio / video) Routed on load only (no cache locality) MM-aware routing; encode/prefill/decode (EPD) separation
Hardware support MI325 / MI300 Validated on MI355X only Validate + CI on MI325 / MI300
PD / networking / comms Transports RDMA (Mooncake / MoRI / NIXL); xGMI diagnostics only Evaluate xGMI / remote-copy for intra-node KV
Parallelism Wide expert parallelism (EP) Not supported 32+ GPU instances spanning nodes
KV-cache management Offload engine coverage vLLM only (incl. AIC GPU-Direct) SGLang (incl. GPU-Direct), then ATOM
KV-cache management Cluster-wide KV pool L3 or L4 (either/or) Composed L3 + L4; multi-model namespacing
KV-cache management Distributed prefill cache Per worker Shared across prefill workers and with decoders
Operator optimization Op injection HyperLoom Optimized OP Injection Cross-engine op/kernel injection using HyperLoom optimized by new model and user workload
Framework Dynamic scaling (Kubernetes) [DONE] Dynamic Scaling, by api, ratio and replica Load-driven autoscaling; runtime role switching
Framework SLA-aware scheduling Relative cost heuristic; throughput only Settable SLO targets and goodput reporting

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

This issue is a broad 2026 Q3 roadmap covering multimodal routing, hardware validation, transports, parallelism, KV-cache management, operator injection, Kubernetes scaling, and SLA scheduling. No files, tests, entry points, or bounded acceptance criteria are named. Start by selecting one roadmap item and locating its implementation area; the work is done only when that item has a specific scope and verifiable target.

Written by the indexing model from the issue text.

Assessment

Tech stack
kubernetes, python
Domain
backend, cloud, distributed-systems, infrastructure
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
25/100

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