Muse Glimmer 30B Requirements: VRAM, GPU & Mac (2026)

Muse Glimmer 30B is a 30-billion-parameter dense multimodal model released by Meta Superintelligence Labs on August 10, 2026. With 4-bit K-Quant quantization it compresses to roughly 20GB or less, running on GPUs or Macs with a 24GB or 32GB memory envelope. This guide breaks down hardware requirements by quantization level, covers performance on the RTX 5090 and MacBook M4 Max/M5 Max, and explains what to watch for when using the full 131K context window. See the official Meta blog post for more details.

The table below shows rough estimates for each quantization level, based on general rules of thumb for a 30B dense model. Meta's official disclosure covers only the 4-bit case — approximately 20GB or less, running within a 24GB or 32GB memory envelope — so treat the other rows as estimates only.

Muse Glimmer is officially positioned as optimized for always-on local agentic workflows, trained by distillation from Meta's larger Muse Spark model (SFT plus on-policy distillation plus reinforcement learning across general, reasoning, coding, and agentic domains). Meta highlights eight capabilities: completing multi-step tasks, reliable tool calling, long reasoning chains, recovering from errors, multimodal understanding (text plus images), compatibility with agentic scaffolds, adjustable reasoning effort, and support for over 100 languages. It has a 131K-token context window, is released under the Apache 2.0 license, and its weights are published on Hugging Face as meta-models/Muse-Glimmer-30B (with a GGUF version at meta-models/Muse-Glimmer-30B-GGUF).

Quantization reduces the numeric precision used to store a model's weights, shrinking both file size and memory footprint. Moving from BF16 (16-bit floating point) down to 8-bit, 6-bit, or 4-bit representations lowers VRAM usage step by step, generally at the cost of a small accuracy trade-off. Meta's official figures show Muse Glimmer compressing to roughly 20GB or less at 4-bit K-Quant, a design aimed at making it practical to run on consumer-grade GPUs and laptops around the 24GB mark.

Meta officially lists the MacBook M4 Max and M5 Max as supported devices for Muse Glimmer. Published speculative decoding speedups show roughly 1.8x on the M5 Max and 1.5x on the M4 Max (compared to about 3.1x on the RTX 5090). Because Apple Silicon uses unified memory shared between the model and the system, running the 4-bit quantized model (roughly 20GB or less) is generally practical on Macs with 32GB of memory or more. MLX and llama.cpp support Muse Glimmer from Day 0, and it's also accessible through apps like LM Studio.

On NVIDIA GPUs, the 4-bit quantized model puts the RTX 3090/4090 (24GB) at the practical floor, while the RTX 5090 (32GB) is a more realistic choice for higher-precision quantizations like Q6_K. Combined with speculative decoding, the RTX 5090 sees roughly a 3.1x speedup. AMD has officially announced support for running Muse Glimmer on Ryzen AI Max APUs and Radeon GPUs, extending it to integrated GPU environments as well.

On machines without a GPU or Apple Silicon, it is technically possible to run Muse Glimmer using a GGUF file and enough RAM — roughly 32GB or more as a rule of thumb. However, inference speed drops significantly compared to running on a GPU or unified memory, making it a poor fit for always-on agentic workloads. CPU-only execution is best reserved for testing or small-scale use.

Muse Glimmer supports up to 131K tokens of context, but using the full window requires several extra gigabytes of memory for the KV cache. On a 24GB GPU running the 4-bit quantized model (roughly 20GB or less), there's little headroom left for a full 131K context, so keeping context length around 32K is a more realistic target in practice. For longer context windows, look at hardware with 32GB or more of VRAM or unified memory.

Muse Glimmer is supported from Day 0 by major inference engines and tools including llama.cpp, MLX, ExecuTorch, Ollama, LM Studio, vLLM, and SGLang. For a quick local test, Ollama's typical pattern is a single ollama run command to pull and launch a model (check the distribution page for the exact model name/tag). If you're deciding between GUI tools, see our Ollama vs. LM Studio comparison. The model itself is available on Hugging Face as `meta-models/Muse-Glimmer-30B`, with a GGUF version also published.

Muse Glimmer is evaluated on benchmarks including DeepSearch QA, MCP-Atlas, τ-Bench, and SWE-Bench, which measure scaffolded agentic behavior, code writing and debugging, and multi-turn problem solving. Meta names Gemma4-31B and Qwen3.6-27B as its official comparison points, both positioned as similarly sized local open-weight models. For a deeper look at hardware requirements for a comparable model, see our Gemma 4 hardware requirements guide.

Muse Glimmer 30B is Meta's agent-focused open-weight model, engineered to fit in roughly 20GB or less at 4-bit quantization and run on GPUs or Macs in the 24–32GB class. An RTX 3090/4090 is enough for 4-bit quantization, while an RTX 5090 or a Mac Studio with ample memory opens the door to higher-precision quantization and longer context windows. Use the table above as a starting point to check which quantization level fits your own hardware.