Workbench / Online
BUILD YOUR COMPUTE STACK
Start with the model. MuseBoard maps the memory and hardware. Settings are mirrored to the URL, so any configuration can be shared.
Compute workbench
B / Estimated VRAM
Inference26.8GB
Serving: weights + KV cache + runtime overhead. Estimate — not a guarantee.
- Model weights23.4 GB
- KV cache1.1 GB
- Runtime overhead2.3 GB
Memory headroom
Free after estimated load
Utilization
Target ≤ 90% of 48.0 GB
Decode ceiling
Theoretical, batch 1, bandwidth-bound
VRAM usage
26.8 GB / 48.0 GB
Pinned configuration
2 × NVIDIA GeForce RTX 4090
- Total VRAM
- 48.0 GB
- Topology
- Single node · TP
- Interconnect
- PCIe 4.0
- Board power
- 900 W
Compatibility / 2 × RTX 4090
- Ready
Inference
26.8 GB · 56% of 2 × RTX 4090
- Ready
Fine-tuning
34.6 GB · 72% of 2 × RTX 4090
- Cluster
Training
826.7 GB · Needs 40 GPUs — exceeds one consumer node
Alternative configurations
- Mixture-of-experts: all 46.7B parameters stay resident in memory; only ~12.9B are active per token.
- 01SERVE / READY
Inference
Estimate memory requirements for serving models.
weights + KV cache + overhead
Open in workbench - 02ADAPT / LORA
Fine-tuning
Explore memory requirements for adapting existing models.
frozen base + adapters + activations
Open in workbench - 03TRAIN / ADAM
Training
Understand large-scale compute requirements.
weights + grads + optimizer + activations
Open in workbench
MuseBoard gives first-order estimates for planning conversations. It does not replace profiling, capacity testing or detailed ML infrastructure planning.