CyberPal2.0 20B — Hardware Requirements & GPU Compatibility
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CyberPal2.0 20B is a 20.9B-parameter open language model from cyber-pal-security. It supports a context window of up to 131,072 tokens. At BF16 it needs about 42.20 GB of VRAM — see which GPUs and Macs can run it below.
403 downloads
8 likesSpecifications
- Publisher
- cyber-pal-security
- Parameters
- 20.9B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-05-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does CyberPal2.0 20B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 42.2 GB | 46.7 GB | 41.83 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run CyberPal2.0 20B?
BF16 · 42.2 GBCyberPal2.0 20B (BF16) requires 42.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 55+ GB is recommended. Using the full 131K context window can add up to 4.5 GB, bringing total usage to 46.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run CyberPal2.0 20B?
BF16 · 42.2 GB11 devices with unified memory can run CyberPal2.0 20B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does CyberPal2.0 20B need?
CyberPal2.0 20B requires 42.2 GB of VRAM at BF16. Full 131K context adds up to 4.5 GB (46.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20.9B × 16 bits ÷ 8 = 41.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.9 GB (at full 131K context)
VRAM usage by quantization
BF1642.2 GBBF16 + full context46.7 GB- Can NVIDIA GeForce RTX 5090 run CyberPal2.0 20B?
No — CyberPal2.0 20B requires at least 42.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run CyberPal2.0 20B on a Mac?
CyberPal2.0 20B requires at least 42.2 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 CyberPal2.0 20B locally?
Yes — CyberPal2.0 20B can run locally on consumer hardware. At BF16 quantization it needs 42.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CyberPal2.0 20B?
At BF16, CyberPal2.0 20B can reach ~69 tok/s on AMD Instinct MI300X. 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 ÷ 42.2 × 0.55 = ~69 tok/s
Estimated speed at BF16 (42.2 GB)
~69 tok/s~52 tok/s~43 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of CyberPal2.0 20B?
At BF16, the download is about 41.83 GB.
- Which GPUs can run CyberPal2.0 20B?
No single consumer GPU has enough VRAM to run CyberPal2.0 20B at BF16 (42.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run CyberPal2.0 20B?
11 devices with unified memory can run CyberPal2.0 20B at BF16 (42.2 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.