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.

Workbench / Online

Model / Llama 405BMode / EstimateUnits / GB (10⁹ B)

A / Model configuration

ModelMeta

Parameters (billions)

Editing the count switches to a custom model with an estimated architecture.

Precision0.5 B / param

Context length8,192 tokens

Batch sizeConcurrent sequences

Workload

Target hardwarePinned

Architecture / published config

Layers: 126
Hidden: 16,384
KV heads: 8 × 128
KV / token: 504 KB

B / Estimated VRAM

Inference

227.5GB

Serving: weights + KV cache + runtime overhead. Estimate — not a guarantee.

Model weights405.9B params × 0.5 B202.9 GB
KV cache8,192 tokens × batch 14.2 GB
Runtime overhead≈10% of weights20.3 GB

Memory headroom

Free after estimated load

Utilization

Target ≤ 90% of 256.0 GB

Decode ceiling

~71 tok/s

Theoretical, batch 1, bandwidth-bound

VRAM usage

227.5 GB / 256.0 GB

0 GB8 × 32 GB

Pinned configuration

8 × NVIDIA GeForce RTX 5090

Total VRAM: 256.0 GB
Topology: Single node · TP
Interconnect: PCIe 5.0
Board power: 4,600 W

Compatibility / 8 × RTX 5090

Inference
227.5 GB · 89% of 8 × RTX 5090
Ready
Fine-tuning
305.8 GB · Needs 16 GPUs — exceeds one consumer node
Cluster
Training
7,191 GB · Needs 256 GPUs — exceeds one consumer node
Cluster

Alternative configurations

MuseBoard gives first-order estimates for planning conversations. It does not replace profiling, capacity testing or detailed ML infrastructure planning.