orcarouter·Qwen 3.8

Qwen3.8 Flash Next Uncensored — Hardware Requirements & GPU Compatibility

VisionFunctionsReasoning

Qwen3.8 Flash Next Uncensored is a 180.0B-parameter open language model from orcarouter in the Qwen 3.8 family. At Q4_K_M it needs about 118.80 GB of VRAM — see which GPUs and Macs can run it below.

1.0K downloads 34 likes

Specifications

Publisher
orcarouter
Family
Qwen 3.8
Parameters
180.0B
Release Date
2026-08-26
License
Apache 2.0

Get Started

How Much VRAM Does Qwen3.8 Flash Next Uncensored Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.4084.2 GB
Q3_K_Mest.3.9096.5 GB
Q4_K_Mest.4.80118.8 GB
Q5_K_Mest.5.70141.1 GB
Q6_Kest.6.60163.3 GB
Q8_0est.8.00198 GB
BF16est.16.00396 GB

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 Flash Next Uncensored?

Q4_K_M · 118.8 GB

Qwen3.8 Flash Next Uncensored (Q4_K_M) requires 118.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 155+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Qwen3.8 Flash Next Uncensored?

Q4_K_M · 118.8 GB

10 devices with unified memory can run Qwen3.8 Flash Next Uncensored, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M5 Max (128 GB).

Related Models

Frequently Asked Questions

How much VRAM does Qwen3.8 Flash Next Uncensored need?

Qwen3.8 Flash Next Uncensored requires 118.8 GB of VRAM at Q4_K_M, or 396 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 180.0B × 4.8 bits ÷ 8 = 108 GB

KV Cache + Overhead 10.8 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

118.8 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Qwen3.8 Flash Next Uncensored?

No — Qwen3.8 Flash Next Uncensored requires at least 84.2 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Qwen3.8 Flash Next Uncensored?

For Qwen3.8 Flash Next Uncensored, Q4_K_M (118.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (141.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 84.2 GB.

VRAM requirement by quantization

Q2_K
84.2 GB
Q4_K_M
118.8 GB
Q5_K_M
141.1 GB
Q6_K
163.3 GB
Q8_0
198.0 GB
BF16
396.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen3.8 Flash Next Uncensored on a Mac?

Qwen3.8 Flash Next Uncensored requires at least 84.2 GB at Q2_K, 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 Flash Next Uncensored locally?

Yes — Qwen3.8 Flash Next Uncensored can run locally on consumer hardware. At Q4_K_M quantization it needs 118.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3.8 Flash Next Uncensored?

At Q4_K_M, Qwen3.8 Flash Next Uncensored can reach ~40 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 B2008000 ÷ 118.8 × 0.65 = ~44 tok/s

Estimated speed at Q4_K_M (118.8 GB)

~44 tok/s
~44 tok/s
~40 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Qwen3.8 Flash Next Uncensored?

At Q4_K_M, the download is about 108.00 GB. The full-precision BF16 version is 360.00 GB. The smallest option (Q2_K) is 76.50 GB.

Which GPUs can run Qwen3.8 Flash Next Uncensored?

No single consumer GPU has enough VRAM to run Qwen3.8 Flash Next Uncensored at Q4_K_M (118.8 GB). Multi-GPU or professional hardware is required.

Which devices can run Qwen3.8 Flash Next Uncensored?

18 devices with unified memory can run Qwen3.8 Flash Next Uncensored at Q4_K_M (118.8 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.