Kappa 20B 131k GGUF — Hardware Requirements & GPU Compatibility
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565 downloads
4 likesSpecifications
- Publisher
- eousphoros
- Parameters
- 20B
- Release Date
- 2026-03-01
- License
- Other
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HuggingFace
How Much VRAM Does Kappa 20B 131k GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 44 GB | — | 40.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Kappa 20B 131k GGUF?
BF16 · 44 GBKappa 20B 131k GGUF (BF16) requires 44 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 58+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kappa 20B 131k GGUF?
BF16 · 44 GB11 devices with unified memory can run Kappa 20B 131k GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Kappa 20B 131k GGUF need?
Kappa 20B 131k GGUF requires 44 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 16 bits ÷ 8 = 40 GB
KV Cache + Overhead ≈ 4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1644.0 GB- Can NVIDIA GeForce RTX 5090 run Kappa 20B 131k GGUF?
No — Kappa 20B 131k GGUF requires at least 44 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Kappa 20B 131k GGUF on a Mac?
Kappa 20B 131k GGUF requires at least 44 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 Kappa 20B 131k GGUF locally?
Yes — Kappa 20B 131k GGUF can run locally on consumer hardware. At BF16 quantization it needs 44 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Kappa 20B 131k GGUF?
At BF16, Kappa 20B 131k GGUF can reach ~66 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 ÷ 44.0 × 0.55 = ~66 tok/s
Estimated speed at BF16 (44 GB)
AMD Instinct MI300X~66 tok/sNVIDIA H100 SXM~50 tok/sAMD Instinct MI250X~41 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Kappa 20B 131k GGUF?
At BF16, the download is about 40.00 GB.
- Which GPUs can run Kappa 20B 131k GGUF?
No single consumer GPU has enough VRAM to run Kappa 20B 131k GGUF at BF16 (44 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kappa 20B 131k GGUF?
11 devices with unified memory can run Kappa 20B 131k GGUF at BF16 (44 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.