How much VRAM does Muse Glimmer 30B need?

About 17.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 18.1 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Muse Glimmer 30B run on a 24GB GPU?

Yes — at Q4_K_M and 4K context it needs about 17.7 GB, which leaves 6.3 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.

Which quantization of Muse Glimmer 30B should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 4 levels in the index are Q4_K_M, Q5_K_M, Q6_K, Q8_0.

Muse Glimmer 30B

30B

Meta Muse

Meta's first open-weight model since Llama 4, released in August under Apache 2.0 and built to run on one card. It is a dense model: a 28B text decoder that reads images through a separate vision encoder and answers in text. Three of every four layers use a 2K sliding window and the rest share just 2 KV heads, so long context is unusually cheap. At Q4_K_M it needs about 17.7 GB at 4K context and only 19.4 GB at its full 128K window, comfortable on a 24 GB card either way. Q5_K_M is tight on 24 GB, and Q8_0 wants a 32 GB card. The vision encoder (about 1.4 GB as a GGUF mmproj file) and Meta's optional speculative-decoding drafter load on top and are not included. Sizes come from the published GGUF file sizes; the Q4_K_M row is Meta's own official build. Meta's benchmark claims are its own, and no per-level quality loss has been published.

⬇ 268.9K HF downloads♥ 1990 likesmeta-models/Muse-Glimmer-30B· stats from 10/9/2026

Consumer GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.81	17.7 GB	—	—	Estimated	Calc HF
GGUF	Q5_K_M	5.78	21.2 GB	—	—	Estimated	Calc HF
GGUF	Q6_K	6.72	24.6 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	31.1 GB	—	—	Estimated	Calc HF

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15.3 GBmin VRAM—accuracy

Google's Gemma 4 mixture-of-experts model: about 25B parameters in the text model, roughly 4B active per token, a 256K window and image input. Only 5 of its 30 layers keep a cache that grows with context, and those use fewer, wider heads, so the cache stays small — about 0.9 GB at 32K and 5.3 GB at the full 256K. At Q4 that is about 17 GB at 32K: comfortable on a 24 GB card, which can still load the whole window (tight). A 16 GB card is just too small. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.

31B

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Google Gemma 4

Consumer GPUPro GPU

18.6 GBmin VRAM—accuracy

Google's dense Gemma 4 flagship: about 31B parameters, a 256K window and image input. Only 10 of its 60 layers cache the full context, and those use four wide KV heads, so 128K of context adds about 11 GB of cache where a conventional 60-layer design would need about 120 GB. At Q4 it is about 21 GB at 4K — on a 24 GB card that is right at the edge of comfortable, and tight from 32K. A 32 GB card holds 32K comfortably. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.

30B-A3B

Qwen3 30B-A3B Instruct

Alibaba Qwen3

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Qwen3 MoE with only 3B active params. Q4 ~19GB file; outperforms QwQ-32B on 16GB cards.

21B MoE

GPT-OSS 20B

OpenAI GPT-OSS

Consumer GPUMac / Apple Silicon

11.9 GBmin VRAM100.0%accuracy

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Running Muse Glimmer 30B locally

At Q4_K_M and 4K of context, Muse Glimmer 30B needs about 17.7 GB — 15.9 GB of weights, 0.15 GB of KV cache and a 1.6 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 31 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 0.35 GB, taking the total to 18.1 GB. That is gentler than the parameter count suggests: only 13 of 52 layers keep a KV cache that grows with context, and 39 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 19 GB.

Which build to download

This index only tracks Muse Glimmer 30B in GGUF, across 4 levels (Q4_K_M, Q5_K_M, Q6_K, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Muse Glimmer 30B need?: About 17.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 18.1 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Muse Glimmer 30B run on a 24GB GPU?: Yes — at Q4_K_M and 4K context it needs about 17.7 GB, which leaves 6.3 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.
Which quantization of Muse Glimmer 30B should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 4 levels in the index are Q4_K_M, Q5_K_M, Q6_K, Q8_0.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 309024GB · +6.3 Radeon RX 7900 XTX24GB · +6.3 RTX 409024GB · +6.3 Tesla P40 24G24GB · +6.3 Mac M5 32G32GB · +6.3
Tight but possible: Radeon RX 7900 XT20GB Mac M4 24G24GB Mac M4 Pro 24G24GB
Just misses: RTX 4060 Ti 16G+1.7GB over Radeon RX 7600 XT+1.7GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-09 RSS → /feed.xml