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Qwen3.8-Flash-Next VRAM Requirements

Developed by Alibaba Cloud (Qwen)

Find out exactly how much VRAM you need to run Qwen3.8-Flash-Next locally. Calculate the memory footprint of different GGUF quantization variants (like Q4_K_M or Q8_0), estimate your context length KV cache VRAM footprint, and determine if your hardware supports a full GPU VRAM offload or if you will need to rely on slow partial CPU offloading to avoid a CUDA Out of Memory (OOM) error.

Hugging Face Repository

Hardware Configuration

Adjust settings to check compatibility with your system in real time.

GB VRAM

Available: 14.50 GB

GB RAM

Available: 29.00 GB

8,192 tokens
CPU Offloading
Multi-Token Prediction (MTP)
Compatibility Verdict
Out of Memory Risk

This configuration exceeds your system's usable memory capacity. Attempting to run it will cause crashes or freeze your machine.

Requires 118.01 GB total memory (weights: 84.18 GB, context overhead: 0.26 GB, activation overhead: 6.76 GB).

Memory Margin-74.51 GB
Hardware Memory Partitioning
GPU Dedicated VRAM
Core Transformer Weights7.49 GB
KV Cache0.26 GB
Activation & Runtime Overhead6.76 GB
Total VRAM Required14.50 GB
System RAM
N-Gram Embedding Table26.82 GB
Offloaded Core Layers76.69 GB
Total RAM Required103.51 GB

Qwen3.8-Flash-Next Quantization Formats & VRAM Compatibility

Select a format to set it as active and calculate your system fit dynamically.

QuantCore WeightsN-Gram EmbeddingKV CacheOverheadTotal MemoryStatusLinks
Base (Unquantized)225.28 GB102.00 GB0.26 GB18.04 GB
345.58 GB
Too Large
HF weights
UD-Q4_K_XL84.18 GB26.82 GB0.26 GB6.76 GB
118.01 GB
Too Large
GGUF
UD-Q2_K_XL52.08 GB26.82 GB0.26 GB4.19 GB
83.34 GB
Too Large
GGUF
UD-IQ1_M47.68 GB26.82 GB0.26 GB3.84 GB
78.59 GB
Too Large
GGUF

Qwen3.8-Flash-Next KV Cache Memory Breakdown

How to Setup and Run Qwen3.8-Flash-Next Locally

1

Method A: Ollama (Recommended)

Ollama is the easiest way to run models in the background. First, download it from ollama.com, then execute this terminal command:

ollama run <model-name>
2

Method B: LM Studio (GUI)

If you prefer a full graphical interface with chat UI and local server hosting:

  • Download and install LM Studio.
  • Search for Qwen3.8-Flash-Next in the home page search tab.
  • Select a quantization level (like Q4_K_M) that fits your VRAM, click download, and load it to chat.

Model Specs

  • DeveloperAlibaba Cloud (Qwen)
  • Parameter Count180B
  • Base File Size335.3 GB
  • AvailabilityLocal-Only
  • Input Modalities
    TextImageVideo

Standard Benchmark Scores

Coding (SWE-bench Pro)62.5%
Reasoning (GPQA Diamond)91.7%

Commercial API Pricing

This model is self-hosted only or official API pricing is not available.

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