QU SSM 130M MoE — Hardware Requirements & GPU Compatibility
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QU SSM 130M MoE is a 135M-parameter open language model from Prannesshkva. At BF16 it needs about 0.30 GB of VRAM — see which GPUs and Macs can run it below.
2.6K downloads
2 likesSpecifications
- Publisher
- Prannesshkva
- Parameters
- 135M
- Architecture
- QUSSMForCausalLM
- Vocabulary Size
- 50,257
- Release Date
- 2026-08-31
- License
- Other
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HuggingFace
How Much VRAM Does QU SSM 130M MoE Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.3 GB | — | 0.27 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run QU SSM 130M MoE?
BF16 · 0.3 GBQU SSM 130M MoE (BF16) requires 0.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run QU SSM 130M MoE?
BF16 · 0.3 GB59 devices with unified memory can run QU SSM 130M MoE, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does QU SSM 130M MoE need?
QU SSM 130M MoE requires 0.3 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 135M × 16 bits ÷ 8 = 0.3 GB
VRAM usage by quantization
BF160.3 GB- Can I run QU SSM 130M MoE on a Mac?
QU SSM 130M MoE requires at least 0.3 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run QU SSM 130M MoE locally?
Yes — QU SSM 130M MoE can run locally on consumer hardware. At BF16 quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is QU SSM 130M MoE?
At BF16, QU SSM 130M MoE can reach ~16000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2184 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 0.3 × 0.65 = ~17333 tok/s
Estimated speed at BF16 (0.3 GB)
~17333 tok/s~2184 tok/s~17333 tok/s~16000 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of QU SSM 130M MoE?
At BF16, the download is about 0.27 GB.
- Which GPUs can run QU SSM 130M MoE?
50 consumer GPUs can run QU SSM 130M MoE at BF16 (0.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run QU SSM 130M MoE?
59 devices with unified memory can run QU SSM 130M MoE at BF16 (0.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.