Bananamind2 Mini IT — Hardware Requirements & GPU Compatibility
Chat
Bananamind2 Mini IT is a 25M-parameter open language model from hauser458b. It supports a context window of up to 4,096 tokens. At BF16 it needs about 0.37 GB of VRAM — see which GPUs and Macs can run it below.
158 downloads
4 likesSpecifications
- Publisher
- hauser458b
- Parameters
- 25M
- Architecture
- BananaMind2MiniForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 8,194
- Release Date
- 2026-07-17
Get Started
HuggingFace
How Much VRAM Does Bananamind2 Mini IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.4 GB | 0.4 GB | 0.05 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 Bananamind2 Mini IT?
BF16 · 0.4 GBBananamind2 Mini IT (BF16) requires 0.4 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 Bananamind2 Mini IT?
BF16 · 0.4 GB59 devices with unified memory can run Bananamind2 Mini IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Bananamind2 Mini IT need?
Bananamind2 Mini IT requires 0.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 25M × 16 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.3 GB (at full 4K context)
VRAM usage by quantization
BF160.4 GBBF16 + full context0.4 GB- Can I run Bananamind2 Mini IT on a Mac?
Bananamind2 Mini IT requires at least 0.4 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 Bananamind2 Mini IT locally?
Yes — Bananamind2 Mini IT can run locally on consumer hardware. At BF16 quantization it needs 0.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Bananamind2 Mini IT?
At BF16, Bananamind2 Mini IT can reach ~11892 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1771 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 ÷ 0.4 × 0.65 = ~14054 tok/s
Estimated speed at BF16 (0.4 GB)
~14054 tok/s~1771 tok/s~14054 tok/s~11892 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Bananamind2 Mini IT?
At BF16, the download is about 0.05 GB.
- Which GPUs can run Bananamind2 Mini IT?
50 consumer GPUs can run Bananamind2 Mini IT at BF16 (0.4 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 Bananamind2 Mini IT?
59 devices with unified memory can run Bananamind2 Mini IT at BF16 (0.4 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.