Muse Glimmer 30B.
A dense 30B open-weights model from Meta that takes text and images and is tuned for local agent work, with a 4-bit build that fits in 24 to 32 GB of VRAM.
1× Nvidia Q RTX 6000 · $0.13/hr.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
Frequently asked.
How do I run Muse Glimmer 30B?
Where can I access Muse Glimmer 30B?
How much does it cost to run Muse Glimmer 30B?
Is Muse Glimmer 30B open-source or proprietary?
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr is the cheapest rate on offer now. A multi-card setup uses a real instance of that size when one is rented; otherwise it is the single-card rate times the count.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
96 GB |
$0.50/hr
est. 4 × single-card rate
Vast.ai · on-demand
|
— | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
48 GB |
$0.25/hr
est. 2 × single-card rate
Vast.ai · on-demand
|
— | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
24 GB |
$0.13/hr
Vast.ai · on-demand
|
31.4 | Compare → |
What it costs per month across providers.
Estimate your monthly bill for Muse Glimmer 30B across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
Rent the GPU instead of paying per token.
For an open-weights model like Muse Glimmer 30B, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month. Power is included in a rental's hourly price.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.
About Muse Glimmer 30B.
Muse Glimmer is a dense 30B-class model (about 29.6B parameters including its vision encoder) from Meta Superintelligence Lab, released with open weights under Apache 2.0. It reads text and images, outputs text, and handles over 131,000 tokens of context. It is built for agent tasks that run locally: tool calls, multi-step reasoning, recovering from failed calls and reading screenshots or documents. Meta compresses it to roughly 4-bit so it fits in 24 to 32 GB of VRAM, and ships a small drafter model for faster generation.