Qwen3.8 27B Kearuga — Hardware Requirements & GPU Compatibility
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Qwen3.8 27B Kearuga is a 19.8B-parameter open language model from 0xWhiteMage in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 40.27 GB of VRAM — see which GPUs and Macs can run it below.
290 downloads
2 likesSpecifications
- Publisher
- 0xWhiteMage
- Family
- Qwen 3.8
- Parameters
- 19.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.8 27B Kearuga Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 40.3 GB | 97.1 GB | 39.52 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 Qwen3.8 27B Kearuga?
BF16 · 40.3 GBQwen3.8 27B Kearuga (BF16) requires 40.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 53+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 97.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen3.8 27B Kearuga?
BF16 · 40.3 GB27 devices with unified memory can run Qwen3.8 27B Kearuga, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3.8 27B Kearuga need?
Qwen3.8 27B Kearuga requires 40.3 GB of VRAM at BF16. Full 262K context adds up to 56.8 GB (97.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 19.8B × 16 bits ÷ 8 = 39.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 262K context)
VRAM usage by quantization
BF1640.3 GBBF16 + full context97.1 GB- Can NVIDIA GeForce RTX 5090 run Qwen3.8 27B Kearuga?
No — Qwen3.8 27B Kearuga requires at least 40.3 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Qwen3.8 27B Kearuga on a Mac?
Qwen3.8 27B Kearuga requires at least 40.3 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 Qwen3.8 27B Kearuga locally?
Yes — Qwen3.8 27B Kearuga can run locally on consumer hardware. At BF16 quantization it needs 40.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.8 27B Kearuga?
At BF16, Qwen3.8 27B Kearuga can reach ~119 tok/s on AMD Instinct MI350X. 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 ÷ 40.3 × 0.65 = ~129 tok/s
Estimated speed at BF16 (40.3 GB)
~129 tok/s~129 tok/s~119 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.8 27B Kearuga?
At BF16, the download is about 39.52 GB.
- Which GPUs can run Qwen3.8 27B Kearuga?
No single consumer GPU has enough VRAM to run Qwen3.8 27B Kearuga at BF16 (40.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen3.8 27B Kearuga?
27 devices with unified memory can run Qwen3.8 27B Kearuga at BF16 (40.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.