Muse Glimmer 30B
29.8B parameters · 128k context · Apache 2.0 · released 2026-08-09
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In short
Muse Glimmer 30B is an open model from meta-models with 29.8B parameters. At Q4_K_M, Muse Glimmer 30B needs about 17 GB of memory at 8k context and 17 GB at 32k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Germany that runs Muse Glimmer 30B fully in memory at 32k context is the AMD Radeon RX 7900 XT, 899 € at Amazon, at about 25 tok/s (estimated).
Updated . Speeds are estimates; how they are worked out.
What to run it on in Germany
Fully in memory at 32k context, priced at retailers today.
Cheapest
AMD Radeon RX 7900 XT25tok/sest.Q4_K_M
899 €
Best value
AMD Radeon RX 7900 XT25tok/sest.Q4_K_M
899 €
Fastest
NVIDIA GeForce RTX 509046tok/sest.Q6_K
6.199 €
Mac
Mac Studio M3 Ultra 96GB16tok/sest.Q8_0
5.999 €
All hardware that runs it
Everything we price, best value first. Two cards split the model by layers.
| Hardware | Quant | Fits | Decode | Price | tok/s per 1,000 |
|---|---|---|---|---|---|
| NVIDIA Tesla P40 | Q6_K | fits | 9 tok/s | 369 € | 24.4 |
| AMD Radeon RX 7900 XT | Q4_K_M | fits | 25 tok/s | 899 € | 27.5 |
| Intel Arc Pro B60 | Q5_K_M | fits | 11 tok/s | 953 € | 11.6 |
| Mac mini M4 32GB | Q5_K_M | fits | 4 tok/s | 1.450 € | 2.6 |
| AMD Radeon RX 7900 XTX | Q5_K_M | fits | 26 tok/s | 1.555 € | 16.5 |
| NVIDIA GeForce RTX 3090 | Q6_K | fits | 24 tok/s | 1.850 € | 13.1 |
| AMD Radeon AI PRO R9700 | Q6_K | fits | 15 tok/s | 2.049 € | 7.2 |
| NVIDIA GeForce RTX 3090 Ti | Q6_K | fits | 26 tok/s | 2.500 € | 10.4 |
| Mac mini M4 Pro 48GB | Q8_0 | fits | 5 tok/s | 2.649 € | 2.0 |
| Mac mini M4 Pro 64GB | Q8_0 | fits | 5 tok/s | 3.249 € | 1.7 |
| GMKtec EVO-X2 Ryzen AI Max+ 395 128GB | Q8_0 | fits | 4 tok/s | 3.680 € | 1.2 |
| NVIDIA GeForce RTX 4090 | Q6_K | fits | 26 tok/s | 3.915 € | 6.7 |
| Mac Studio M4 Max 64GB | Q8_0 | fits | 11 tok/s | 3.999 € | 2.7 |
| Mac Studio M3 Ultra 96GB | Q8_0 | fits | 16 tok/s | 5.999 € | 2.7 |
| NVIDIA GeForce RTX 5090 | Q6_K | fits | 46 tok/s | 6.199 € | 7.5 |
| ASUS Ascent GX10 128GB | Q8_0 | fits | 5 tok/s | 6.406 € | 0.8 |
| NVIDIA DGX Spark 128GB | Q8_0 | fits | 5 tok/s | 8.615 € | 0.6 |
| Mac Studio M4 Max 128GB | Q8_0 | fits | 11 tok/s | 9.000 € | 1.2 |
| Mac Studio M3 Ultra 256GB | Q8_0 | fits | 16 tok/s | 15.555 € | 1.0 |
| Mac Studio M3 Ultra 512GB | Q8_0 | fits | 16 tok/s | 17.995 € | 0.9 |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | fits | 34 tok/s | 19.588 € | 1.7 |
| Framework Desktop Ryzen AI Max+ 395 128GB | Q8_0 | fits | 4 tok/s | – | – |
| Framework Desktop Ryzen AI Max+ 395 64GB | Q8_0 | fits | 4 tok/s | – | – |
| Beelink GTR9 Pro Ryzen AI Max+ 395 128GB | Q8_0 | fits | 4 tok/s | – | – |
| NVIDIA GeForce RTX 3060 12GB | Q4_K_M | offload | 6 tok/s | 350 € | 16.6 |
| Intel Arc A770 16GB | Q4_K_M | offload | 10 tok/s | 390 € | 25.1 |
| Intel Arc B580 | Q4_K_M | offload | 6 tok/s | 404 € | 14.1 |
| NVIDIA GeForce RTX 3080 10GB | Q4_K_M | offload | 6 tok/s | 500 € | 11.0 |
| AMD Radeon RX 9060 XT 16GB | Q4_K_M | offload | 7 tok/s | 565 € | 13.1 |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q4_K_M | offload | 8 tok/s | 599 € | 13.0 |
| NVIDIA GeForce RTX 5060 Ti 16GB | Q4_K_M | offload | 10 tok/s | 720 € | 14.2 |
| NVIDIA GeForce RTX 5070 | Q4_K_M | offload | 7 tok/s | 760 € | 8.8 |
| AMD Radeon RX 7600 XT | Q4_K_M | offload | 7 tok/s | 813 € | 8.5 |
| AMD Radeon RX 9070 XT | Q4_K_M | offload | 11 tok/s | 835 € | 13.2 |
| AMD Radeon RX 7800 XT | Q4_K_M | offload | 11 tok/s | 857 € | 12.7 |
| NVIDIA GeForce RTX 4070 Super | Q4_K_M | offload | 6 tok/s | 859 € | 7.3 |
| NVIDIA GeForce RTX 5070 Ti | Q4_K_M | offload | 14 tok/s | 1.286 € | 11.1 |
| NVIDIA GeForce RTX 4070 Ti Super | Q4_K_M | offload | 13 tok/s | 1.458 € | 8.6 |
| NVIDIA GeForce RTX 5080 | Q4_K_M | offload | 15 tok/s | 1.500 € | 9.8 |
| NVIDIA GeForce RTX 4080 Super | Q4_K_M | offload | 13 tok/s | 1.990 € | 6.6 |
1 other card or machine we price cannot hold it at this context.
Memory needed
Weights, KV cache and runtime buffers at each context length (KV cache at fp16).
| Quant | File | @8k | @32k | @64k | @128k |
|---|---|---|---|---|---|
| Q4_K_Mgguf | 16.1 GB | 17.0 GB | 17.5 GB | 18.1 GB | 19.3 GB |
| mlx-4bitmlx | 18.1 GB | 19.0 GB | 19.5 GB | 20.0 GB | 21.2 GB |
| Q5_K_Mgguf | 18.7 GB | 19.6 GB | 20.1 GB | 20.7 GB | 21.9 GB |
| mlx-5bitmlx | 21.3 GB | 22.3 GB | 22.7 GB | 23.3 GB | 24.5 GB |
| Q6_Kgguf | 21.8 GB | 22.7 GB | 23.2 GB | 23.8 GB | 25.0 GB |
| mlx-6bitmlx | 24.6 GB | 25.5 GB | 26.0 GB | 26.5 GB | 27.7 GB |
| Q8_0gguf | 30.1 GB | 31.0 GB | 31.5 GB | 32.1 GB | 33.3 GB |
| mlx-8bitmlx | 31.1 GB | 32.0 GB | 32.5 GB | 33.0 GB | 34.2 GB |
Run it
Copy a command for the runtime you use.
- Q4_K_M
- Ollama
ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M - LM Studio
lms get bartowski/Muse-Glimmer-30B-GGUF@q4_k_m - llama.cpp
llama-server -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
- Ollama
- mlx-4bit
- LM Studio
lms get mlx-community/Muse-Glimmer-30B-4bit - MLX
mlx_lm.server --model mlx-community/Muse-Glimmer-30B-4bit
- LM Studio
- Q5_K_M
- Ollama
ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q5_K_M - LM Studio
lms get bartowski/Muse-Glimmer-30B-GGUF@q5_k_m - llama.cpp
llama-server -hf bartowski/Muse-Glimmer-30B-GGUF:Q5_K_M
- Ollama
- mlx-5bit
- LM Studio
lms get mlx-community/Muse-Glimmer-30B-5bit - MLX
mlx_lm.server --model mlx-community/Muse-Glimmer-30B-5bit
- LM Studio
- Q6_K
- Ollama
ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q6_K - LM Studio
lms get bartowski/Muse-Glimmer-30B-GGUF@q6_k - llama.cpp
llama-server -hf bartowski/Muse-Glimmer-30B-GGUF:Q6_K
- Ollama
- mlx-6bit
- LM Studio
lms get mlx-community/Muse-Glimmer-30B-6bit - MLX
mlx_lm.server --model mlx-community/Muse-Glimmer-30B-6bit
- LM Studio
- Q8_0
- Ollama
ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q8_0 - LM Studio
lms get bartowski/Muse-Glimmer-30B-GGUF@q8_0 - llama.cpp
llama-server -hf bartowski/Muse-Glimmer-30B-GGUF:Q8_0
- Ollama
- mlx-8bit
- LM Studio
lms get mlx-community/Muse-Glimmer-30B-8bit - MLX
mlx_lm.server --model mlx-community/Muse-Glimmer-30B-8bit
- LM Studio
Notes
Dense 30B model with a ~1.8B vision encoder; 4-bit quantized variants target 24/32 GB VRAM (full precision ~64GB). Ships with a DFlash speculative decoding drafter.
Questions
- How much VRAM does Muse Glimmer 30B need?
- At Q4_K_M, Muse Glimmer 30B needs about 17 GB of memory at 8k context and 17 GB at 32k, counting the weights, the KV cache and runtime buffers.
- What is the best GPU for running Muse Glimmer 30B locally?
- The cheapest hardware in Germany that runs Muse Glimmer 30B fully in memory at 32k context is the AMD Radeon RX 7900 XT, 899 € at Amazon, at about 25 tok/s (estimated). The fastest we price is the NVIDIA GeForce RTX 5090, at about 46 tok/s.
- Can I run Muse Glimmer 30B on a 16 GB GPU?
- Only partly: at 32k context it does not fit in 16 GB, so part of it runs from system RAM and it slows to about 10 tok/s on the Intel Arc A770 16GB (estimated).
- Can I run Muse Glimmer 30B on a 24 GB GPU?
- Yes: at Q6_K it fits fully in 24 GB at 32k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 26 tok/s (estimated).
- How do I run Muse Glimmer 30B locally?
- With Ollama: ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
- What licence is Muse Glimmer 30B released under?
- Apache 2.0, which allows commercial use.