Bananamind2 Mini IT — Hardware Requirements & GPU Compatibility
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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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 4K)Best on 24 GB (BF16 · ~1771 tok/s · 4K)Macs from 8 GB (BF16 · ~194 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Bananamind2 Mini IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve hauser458b/bananamind2-mini-it --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · BF16
- Generation speed
- ~632 tok/s
- generation speed
- Cost per 1M output tokens
- $0.033
- per 1M output tokens
How Much VRAM Does Bananamind2 Mini IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~1771 tok/s |
Selected BF16
Brain floating point 160.4GBat 4K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.6 GB free · ~1771 tok/s
≥50 tok/s needs ≥28 GB/s · RTX 5090 ~3148, RTX 3090 Ti ~1771
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Bananamind2 Mini IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.6 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1336Simulate output on RTX 3080RTX 2080 Ti~1082Simulate output on RTX 2080 TiRTX 3070 Ti~1069Simulate output on RTX 3070 TiM3*8 GB~194Simulate output on iMac 24" M3 (8 GB)Orin Nano~179Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~129Simulate output on iPhone 1720 GB
fits · 19.6 GB free · up to 4K ctx
1 GPU
RX 7900 XT~1297Simulate output on RX 7900 XT32–40 GB
fits · 31.6 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3148Simulate output on RTX 5090M5 Max36 GB~870Simulate output on Mac Studio M5 Max (36 GB)M532 GB~291Simulate output on MacBook Air 13" M5 (32 GB)Orin~360Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~237Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Specifications
- Publisher
- hauser458b
- Parameters
- 25M
- Architecture
- BananaMind2MiniForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 8,194
- Release Date
- 2026-07-17
Frequently 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 0.3 GB (at full 4K context)
VRAM usage by quantization
Can I run Bananamind2 Mini IT on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Bananamind2 Mini IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 0.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Bananamind2 Mini IT locally?
Yes — Bananamind2 Mini IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Bananamind2 Mini IT?
At BF16, Bananamind2 Mini IT can reach ~12973 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 ÷ 0.37 × 0.65 = ~14054 tok/s
Estimated speed at BF16 (0.4 GB)
Real-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?
52 consumer GPUs can run Bananamind2 Mini IT at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Bananamind2 Mini IT?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.