The appeal is iteration speed: private prompts, quick quantization checks, and prototype runs without waiting on hosted queues. It stops making sense when teams pretend it will handle every production path. Power, heat, and VRAM ceilings show up fast once context windows and concurrent users grow.
Buy when privacy, iteration speed, and repeated local experiments matter every week.
Skip if you mainly need production concurrency, burst capacity, or models above the card's memory ceiling.
Wait if your expected utilization is unclear or a new memory tier is within budget soon.
Measured fit
- VRAM
- 24GB class
- Power profile
- Workstation
- Best workload
- Local inference iteration
- Scaling limit
- Concurrent users
- VRAM headroom
- Good
- Noise
- Manageable
- Production fit
- Limited
Evidence and caveats
- Private eval loops were smoother than hosted queues for small and mid-size models.
- VRAM, not raw compute, became the deciding limit during longer-context tests.
- Power and cooling planning changed the value equation more than benchmark deltas.
Idle time kills the economicsConcurrency ceiling arrives quickly