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 / Qwen 1.5BMode / EstimateUnits / GB (10⁹ B)

A / Model configuration

ModelAlibaba

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: 28
Hidden: 1,536
KV heads: 2 × 128
KV / token: 28 KB

B / Estimated VRAM

Inference

1.1GB

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

Model weights1.54B params × 0.5 B0.77 GB
KV cache8,192 tokens × batch 10.23 GB
Runtime overhead≈10% of weights0.08 GB

Memory headroom

Free after estimated load

Utilization

Target ≤ 90% of 24.0 GB

Decode ceiling

~1,309 tok/s

Theoretical, batch 1, bandwidth-bound

VRAM usage

1.1 GB / 24.0 GB

0 GB24 GB

Pinned configuration

1 × NVIDIA GeForce RTX 4090

Total VRAM: 24.0 GB
Topology: Single GPU
Interconnect: PCIe 4.0
Board power: 450 W

Compatibility / 1 × RTX 4090

Inference
1.1 GB · 5% of 1 × RTX 4090
Ready
Fine-tuning
7.7 GB · 32% of 1 × RTX 4090
Ready
Training
33.8 GB · Needs 2 × RTX 4090
Limited

Alternative configurations

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