MustaqiLLM — Hardware Requirements & GPU Compatibility
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MustaqiLLM is a 5.2B-parameter open language model from NeuronUz. It supports a context window of up to 4,096 tokens. At BF16 it needs about 10.78 GB of VRAM — see which GPUs and Macs can run it below.
2.2K downloads
11 likesSpecifications
- Publisher
- NeuronUz
- Parameters
- 5.2B
- Architecture
- NeuronLMForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 48,000
- Release Date
- 2026-08-13
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MustaqiLLM Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 10.8 GB | 10.9 GB | 10.33 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 MustaqiLLM?
BF16 · 10.8 GBMustaqiLLM (BF16) requires 10.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 15+ GB is recommended. Using the full 4K context window can add up to 0.2 GB, bringing total usage to 10.9 GB. 37 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run MustaqiLLM?
BF16 · 10.8 GB48 devices with unified memory can run MustaqiLLM, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does MustaqiLLM need?
MustaqiLLM requires 10.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 5.2B × 16 bits ÷ 8 = 10.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
BF1610.8 GBBF16 + full context10.9 GB- Can I run MustaqiLLM on a Mac?
MustaqiLLM requires at least 10.8 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 MustaqiLLM locally?
Yes — MustaqiLLM can run locally on consumer hardware. At BF16 quantization it needs 10.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MustaqiLLM?
At BF16, MustaqiLLM can reach ~445 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~61 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 ÷ 10.8 × 0.65 = ~482 tok/s
Estimated speed at BF16 (10.8 GB)
~482 tok/s~61 tok/s~482 tok/s~445 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MustaqiLLM?
At BF16, the download is about 10.33 GB.
- Which GPUs can run MustaqiLLM?
37 consumer GPUs can run MustaqiLLM at BF16 (10.8 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6700 XT. 8 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MustaqiLLM?
52 devices with unified memory can run MustaqiLLM at BF16 (10.8 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.