MonadGPT — Hardware Requirements & GPU Compatibility
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32 downloads
108 likesSpecifications
- Publisher
- Pclanglais
- Parameters
- 7.2B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,002
- Release Date
- 2023-11-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MonadGPT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 15.1 GB | 19.1 GB | 14.48 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run MonadGPT?
BF16 · 15.1 GBMonadGPT (BF16) requires 15.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 19.1 GB. 17 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 MonadGPT?
BF16 · 15.1 GB27 devices with unified memory can run MonadGPT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does MonadGPT need?
MonadGPT requires 15.1 GB of VRAM at BF16. Full 33K context adds up to 4.0 GB (19.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 16 bits ÷ 8 = 14.5 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
BF1615.1 GBBF16 + full context19.1 GB- Can I run MonadGPT on a Mac?
MonadGPT requires at least 15.1 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 MonadGPT locally?
Yes — MonadGPT can run locally on consumer hardware. At BF16 quantization it needs 15.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MonadGPT?
At BF16, MonadGPT can reach ~194 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~44 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 15.1 × 0.55 = ~194 tok/s
Estimated speed at BF16 (15.1 GB)
AMD Instinct MI300X~194 tok/sNVIDIA GeForce RTX 4090~44 tok/sNVIDIA H100 SXM~145 tok/sAMD Instinct MI250X~120 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MonadGPT?
At BF16, the download is about 14.48 GB.
- Which GPUs can run MonadGPT?
17 consumer GPUs can run MonadGPT at BF16 (15.1 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MonadGPT?
27 devices with unified memory can run MonadGPT at BF16 (15.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.