AllocateMuse Glimmer 30B GPU requirements
What it takes to run Muse Glimmer 30B locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Muse Glimmer 30B serves up to 131,072 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: RTX 4090 (24 GB)
How it works
Muse Glimmer 30B at 8K context across FP16, 8-bit, and 4-bit, with the smallest single device that fits each.
Longer context grows the KV cache. The slider shows exactly how much memory that adds.
If your hardware fits it, run it there. If not, use managed serving.
Common questions
How much VRAM does Muse Glimmer 30B need?
At 8K context: roughly 68 GB at FP16, 34 GB at 8-bit, and 19 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Muse Glimmer 30B?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a A100 (80 GB) or larger.
Does quantization hurt Muse Glimmer 30B's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is Muse Glimmer 30B under?
The catalog does not list a license for this model. Check the lab’s model card for the exact terms before commercial deployment.
Is there an alternative to buying GPUs for Muse Glimmer 30B?
Yes: managed inference. Allocate serves Muse Glimmer 30B token-metered from $0.35 per million input tokens at provider list price. Idle time costs nothing.
More free tools
Allocate serves open-weight models like Muse Glimmer 30B token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.