UI-Mate 27B vs Muse Glimmer 30B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
UI-Mate 27B is more hardware-efficient: it needs 19.0 GB at Q4_K_M vs 19.2 GB for Muse Glimmer 30B, fitting on 78 GPUs natively.
Analysis
UI-Mate 27B and Muse Glimmer 30B are both agent-focused local releases from August 2026, six days apart (Muse Glimmer on 10 August from Meta, UI-Mate on 16 August from Tencent), but built for different jobs: UI-Mate is a dedicated GUI-automation agent fine-tuned to read screenshots and drive a desktop, while Muse Glimmer is a general tool-calling and agentic-workflow model. Both also solve the "keep the KV cache small" problem, using completely different tricks: UI-Mate inherits Qwen 3.6 27B's linear-attention stack, Muse Glimmer uses Meta's own sliding-window design.
UI-Mate 27B carries Qwen 3.6 27B's hybrid stack over unchanged: 64 layers, 16 of them (4 KV heads x 256 head dim) real Gated Attention with a growing KV cache, the other 48 Gated DeltaNet layers running a fixed-size recurrent state instead. Muse Glimmer 30B takes a different approach: every one of its 52 layers is real attention, but 39 of them (a repeating 3-local/1-global pattern) cap their cache to a 2,048-token sliding window, combined with a narrow 32-query/2-KV-head ratio. At 131,072 tokens, the context length where Muse Glimmer's native window ends (UI-Mate's continues to 262,144, inherited from Qwen 3.6 27B), UI-Mate's KV cache is 8.6 GB against Muse Glimmer's 1.8 GB. Add each model's Q4_K_M weights and the full picture is 28.0 GB total for UI-Mate against 21.0 GB for Muse Glimmer, past a single 24 GB card for UI-Mate at that context length, comfortably inside it for Muse Glimmer. At a more typical 8,192-token context the gap nearly disappears: 19.0 GB for UI-Mate against 19.2 GB for Muse Glimmer, functionally identical. Capability isn't a fair fight in either direction, because the two were trained for different jobs and evaluated on different suites: Muse Glimmer reports general benchmarks (GPQA Diamond 83.5, SWE-bench Verified 76.0, SWE-bench Pro 51.2, Terminal-Bench 2.1 51.7) plus Meta's own agentic-eval table (MCP Atlas, DeepSearch QA, Gaia2), none of which Tencent evaluated UI-Mate on; UI-Mate reports OSWorld-Verified (77.0), WindowsAgentArena (66.2), and its own OSWorkerBench (41.0% strict success), none of which Meta evaluated Muse Glimmer on. The two do overlap on one suite, loosely: Meta's own comparison table measured Muse Glimmer at 65.9 on OSWorld-Verified, and Tencent's own figure for UI-Mate on the same suite is 77.0, but these are two different vendors' separate harnesses on two different checkpoints, not a shared measurement, so treat the gap as directional rather than exact. Licensing is identical (Apache 2.0, no usage restrictions either way), and both are text-plus-vision, neither handles video. Tooling favors neither cleanly at launch: Muse Glimmer's only day-one Ollama tag is Apple Silicon (MLX) only, and UI-Mate shipped with no GGUF or Ollama build of any kind, only BF16 safetensors for vLLM.
Bottom line: These two aren't really competing for the same job. If you need a general local agent for tool use, search, or long-running assistant workflows, Muse Glimmer 30B is the more practical pick today: real GGUF and Ollama support are already forming, its footprint stays smaller at long context, and it reports scores across a broad agentic suite. If your task is specifically driving a desktop GUI from screenshots, UI-Mate 27B is the only one of the two actually built and evaluated for it, but budget for the fact that there's no consumer-GPU path yet: an 80GB+ card through vLLM is what it takes until a community GGUF appears.
What a shared 131,072-token context costs each model
Muse Glimmer 30B's native context window ends at 131,072 tokens; UI-Mate 27B's continues to 262,144, inherited unchanged from Qwen 3.6 27B. Restricted to the range both models can reach, their KV cache still scales completely differently: UI-Mate keeps a per-token cache on 16 of 64 layers with no window cap, while Muse Glimmer caps 39 of 52 layers to a 2,048-token window.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At 131,072 tokens, the most Muse Glimmer 30B supports, its KV cache is 1.8 GB. UI-Mate 27B's KV cache at the same context is 8.6 GB. Add each model's Q4_K_M weights and the full picture is 21.0 GB total for Muse Glimmer 30B against 28.0 GB for UI-Mate 27B, the difference between comfortably fitting a single 24 GB card and needing to spill into partial CPU offload or a larger card at that context length.
VRAM at each quantization (8k context)
| Quant | UI-Mate 27B | Muse Glimmer 30B | Diff |
|---|---|---|---|
| FP32 | 121.6 GB | 124.8 GB | -3% |
| BF16 | 61.1 GB | 62.5 GB | -2% |
| FP16 | 61.1 GB | 62.5 GB | -2% |
| Q8_0 | 32.8 GB | 33.3 GB | -2% |
| Q6_K | 25.4 GB | 25.8 GB | -1% |
| Q5_K_M | 22.1 GB | 22.4 GB | -1% |
| Q4_K_M | 19.0 GB | 19.2 GB | -1% |
| Q3_K_M | 15.2 GB | 15.2 GB | -0% |
| Q2_K | 12.1 GB | 12.1 GB | +0% |
| NVFP4 | 15.7 GB | 15.8 GB | -0% |
Diff is UI-Mate 27B relative to Muse Glimmer 30B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | UI-Mate 27B | Muse Glimmer 30B |
|---|---|---|
| Org | Tencent | Meta |
| Parameters | 27B | 27.8B |
| Architecture | Dense | Dense |
| Context | 256k tokens | 128k tokens |
| Modalities | text, vision | text, vision |
| License | Apache 2.0 | Apache 2.0 |
| Commercial | Yes | Yes |
| Released | 2026-08-16 | 2026-08-10 |
| GPUs (native) | 78 / 112 | 78 / 112 |
GPUs that run only UI-Mate 27B(0)
Every GPU that runs UI-Mate 27B also runs Muse Glimmer 30B.
GPUs that run only Muse Glimmer 30B(0)
Every GPU that runs Muse Glimmer 30B also runs UI-Mate 27B.
GPUs that run both natively(78)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 508016 GB
- NVIDIA RTX 5070 Ti16 GB
- NVIDIA RTX 5060 Ti 16GB16 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4070 Ti SUPER16 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA B300 288GB288 GB
- NVIDIA B200 180GB180 GB
- +66 more GPUs run both
Which should you use?
- • You have limited VRAM: it's a smaller model needing 19.0 GB vs 19.2 GB
- • Long context matters: it supports 256k tokens vs 128k
- • It's the newer release (2026-08-16 vs 2026-08-10); check the benchmark table above for what actually improved
- • You want maximum capability and have a 20 GB+ GPU
- • You're running coding tasks
- • You need chain-of-thought reasoning
Frequently asked questions
- Which is better, UI-Mate 27B or Muse Glimmer 30B?
- UI-Mate 27B has 27B parameters vs 27.8B for Muse Glimmer 30B, so Muse Glimmer 30B is the larger model. UI-Mate 27B is more hardware-efficient, needing 19.0 GB at Q4_K_M vs 19.2 GB.
- How much VRAM does UI-Mate 27B need vs Muse Glimmer 30B?
- At Q4_K_M quantization with 8k context, UI-Mate 27B needs approximately 19.0 GB of VRAM, while Muse Glimmer 30B needs 19.2 GB. At FP16, UI-Mate 27B requires 61.1 GB vs 62.5 GB for Muse Glimmer 30B.
- Can you run UI-Mate 27B on the same GPUs as Muse Glimmer 30B?
- Yes, 78 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 5080, NVIDIA RTX 5070 Ti. However, no GPU can run UI-Mate 27B without also fitting Muse Glimmer 30B, and no GPU can run Muse Glimmer 30B without also fitting UI-Mate 27B.
- What is the difference between UI-Mate 27B and Muse Glimmer 30B?
- UI-Mate 27B has 27B parameters (dense) with a 256k context window. Muse Glimmer 30B has 27.8B parameters (dense) with a 128k context window.
- Which model fits in 24 GB of VRAM, UI-Mate 27B or Muse Glimmer 30B?
- Both fit in 24 GB of VRAM at Q4_K_M: UI-Mate 27B needs 19.0 GB and Muse Glimmer 30B needs 19.2 GB.
- Which handles long context better, UI-Mate 27B or Muse Glimmer 30B?
- At 131,072 tokens, the most Muse Glimmer 30B supports, its KV cache is 1.8 GB. UI-Mate 27B's KV cache at the same context is 8.6 GB. Add each model's Q4_K_M weights and the full picture is 21.0 GB total for Muse Glimmer 30B against 28.0 GB for UI-Mate 27B, the difference between comfortably fitting a single 24 GB card and needing to spill into partial CPU offload or a larger card at that context length.