Issues / #84
Design disaggregated inference architecture leveraging NimsForest multinode
proposed
feature
Priority: low
Project: nimsforest2
Reporter: anonymous
27 Mar 2026 18:49
Description
Design an architecture that leverages NimsForest multinode capabilities for disaggregated AI inference — splitting inference into prefill (prompt processing) and decode (token generation) stages across different nodes.
## Context
Disaggregated inference separates AI inference into two stages with different computational profiles:
- **Prefill**: processes the input prompt, natively parallel, compute-bound
- **Decode**: generates output tokens sequentially, memory-bandwidth-bound
Specializing hardware/nodes per stage yields higher throughput and lower power consumption, but at the cost of flexibility — the prefill:decode ratio is fixed at deployment time.
## Design Questions
1. How can NimsForest multinode dynamically assign nodes to prefill vs decode roles?
2. Can the orchestrator rebalance the prefill:decode ratio based on observed workload characteristics?
3. What is the KV cache transfer strategy between prefill and decode nodes?
4. How does this integrate with the existing Source → River → Tree → Wind data flow?
5. What monitoring/metrics are needed to detect workload shifts and trigger rebalancing?
## Key Tradeoffs
- Fixed hardware ratios work well for predictable workloads but fail when traffic patterns shift
- NimsForest multinode could provide the fleet flexibility that typically only hyperscalers have
- A hybrid approach (general-purpose nodes that can be specialized on demand) may be the sweet spot
## Reference
Based on industry analysis of disaggregated inference patterns used by hyperscalers vs enterprises.
## Context
Disaggregated inference separates AI inference into two stages with different computational profiles:
- **Prefill**: processes the input prompt, natively parallel, compute-bound
- **Decode**: generates output tokens sequentially, memory-bandwidth-bound
Specializing hardware/nodes per stage yields higher throughput and lower power consumption, but at the cost of flexibility — the prefill:decode ratio is fixed at deployment time.
## Design Questions
1. How can NimsForest multinode dynamically assign nodes to prefill vs decode roles?
2. Can the orchestrator rebalance the prefill:decode ratio based on observed workload characteristics?
3. What is the KV cache transfer strategy between prefill and decode nodes?
4. How does this integrate with the existing Source → River → Tree → Wind data flow?
5. What monitoring/metrics are needed to detect workload shifts and trigger rebalancing?
## Key Tradeoffs
- Fixed hardware ratios work well for predictable workloads but fail when traffic patterns shift
- NimsForest multinode could provide the fleet flexibility that typically only hyperscalers have
- A hybrid approach (general-purpose nodes that can be specialized on demand) may be the sweet spot
## Reference
Based on industry analysis of disaggregated inference patterns used by hyperscalers vs enterprises.
Comments (1)
nebula
27 Mar 2026 19:00
Grooming: changed category from bug to feature, set priority to low, changed project from cindy to nimsforest2, rewrote truncated title, restructured description from pasted article into actionable design issue with clear questions and tradeoffs
Nebula's reasoning: This is a research/design exploration, not a bug — recategorized to feature. Priority set to low because it's forward-looking architecture work with no immediate user impact. The original project "cindy" doesn't match the content which is clearly about NimsForest multinode capabilities, so moved to nimsforest2. The title was auto-truncated from the first line of the description — replaced with a concise summary. The description was a copy-pasted article about disaggregated inference with minimal actionable content — restructured to extract the core design ask, key questions to answer, and relevant tradeoffs while preserving the essential context.
Nebula's reasoning: This is a research/design exploration, not a bug — recategorized to feature. Priority set to low because it's forward-looking architecture work with no immediate user impact. The original project "cindy" doesn't match the content which is clearly about NimsForest multinode capabilities, so moved to nimsforest2. The title was auto-truncated from the first line of the description — replaced with a concise summary. The description was a copy-pasted article about disaggregated inference with minimal actionable content — restructured to extract the core design ask, key questions to answer, and relevant tradeoffs while preserving the essential context.