Workload guide · AI
Code assistant (local LLM): hardware requirements guide
Local code completion and chat models (Ollama, Continue).
Hardware requirements by tier
Three tiers for every workload: the minimum that works, the recommended sweet spot, and the comfortable headroom level. These are the same tiers the WisePC decision engine uses when it plans a build around your goal.
| Tier | CPU cores | RAM | Storage | GPU | Network |
|---|---|---|---|---|---|
| Minimum | 8 cores | 16 GB | 1 TB (ssd) | ai | 1 GbE |
| Recommended | 12 cores | 32 GB | 1 TB (ssd) | ai | 2.5 GbE |
| Comfortable | 16 cores | 64 GB | 2 TB (ssd) | ai | 2.5 GbE |
Which tier do you need?
- Pick Minimum (8 cores, 16 GB RAM) only for a single-purpose machine on a tight budget — expect little headroom.
- Recommended (12 cores, 32 GB RAM) is the sweet spot: enough for the workload plus the usual side-services, without overspending.
- Pick Comfortable (16 cores, 64 GB RAM) when this workload shares the machine with others or will grow — you pay for headroom, not for anxiety.
- GPU rule for this workload: ai.
Frequently asked questions
How much RAM does code assistant (local llm) need?
16 GB is the sensible minimum, 32 GB covers most real setups, and 64 GB gives comfortable headroom for growth and extra services.
How many CPU cores does code assistant (local llm) need?
A 8-core CPU is the minimum, 12 cores is the recommended sweet spot, and 16 cores is comfortable when it shares the machine with other workloads.
Does code assistant (local llm) need a dedicated GPU?
A dedicated GPU is strongly recommended — this workload does AI compute.
What storage and network does code assistant (local llm) expect?
Storage: 1 TB of SSD is the recommended baseline (1 TB minimum, 2 TB comfortable). Network: 2.5 GbE is the recommended baseline.
What runs well alongside code assistant (local llm)?
It pairs naturally with: Local AI inference, Dev environments.