windowsxp811203Qwen 3.8Qwen4ExpForConditionalGeneration

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

VisionFunctions

Qwen3.8 Flash Next Abliterated is a 180.0B-parameter open language model from windowsxp811203 in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 108.38 GB of VRAM — see which GPUs and Macs can run it below.

180.0B params · MoE62B active262K contextOtherAug 2026933 downloads6 likes48.9K quant downloads

Based on Qwen3.8 Flash Next

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

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Qwen3.8 Flash Next Abliterated runs on a 128 GB+ Mac or a datacenter GPU

Best local pick → Mac Pro M2 Ultra (128 GB) · Q4_K_M · 109 GB · ~13 tok/s

Macs from 96 GB (Q3_K_M · ~16 tok/s)

Other

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Qwen3.8 Flash Next Abliterated

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Recommended

Ollama

one command · needs Ollama installed

$ollama run hf.co/windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF:Q4_K_M

Q4_K_M runs on one Instinct MI250X 128 GB · 16K context

Download: Qwen3.8-Flash-Next-Abliterated-Q4_K_M.gguf119.15 GBwindowsxp811203

install, OS, other quants

Other ways

each opens step-by-step

Run in cloud

Fits on H200 (141 GB) (32 GB headroom) · Q4_K_M

Generation speed: ~69 tok/s; generation speed
Cost per 1M output tokens: $14.47; per 1M output tokens

Deployon RunPodBest optionfrom $3.59/hr

Compare GPUs →

or

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

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	Runs on
	76.9 GB	78.1 GB	82.2 GB	~76.5 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on H100 SXM 80 GB
	88.1 GB	89.4 GB	93.4 GB	~87.8 GB	3-bit medium — 3-bit medium quantization	Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB
	108.4 GB	109.6 GB	113.7 GB	119.2 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on Instinct MI250X 128 GB
	128.6 GB	129.9 GB	133.9 GB	134.1 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on H200 NVL 141 GB
	148.9 GB	150.1 GB	154.2 GB	167.6 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on B200 192 GB
	180.4 GB	181.6 GB	185.7 GB	188.2 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on B200 192 GB
	360.4 GB	361.6 GB	365.7 GB	354 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 6× H100 SXM 80 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

109.0GBat 16K context

Weights: 108.0 GB
KV cache: 0.7 GB
Overhead: 0.3 GB

Server-class: ~109 GB at 16K context · one Instinct MI250X 128 GB

Or rent H200 from $3.59/hr.

Which Hardware Can Run Qwen3.8 Flash Next Abliterated?

Q4_K_M · 108.4 GB

Qwen3.8 Flash Next Abliterated (Q4_K_M) requires 108.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 141+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 119.0 GB. No consumer GPU has enough memory. 18 devices with unified memory can run Qwen3.8 Flash Next Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (128 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 77.5 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

Alternatives to Qwen3.8 Flash Next Abliterated

Qwen 3.8 family · 12 models →

Similar models

1

Model	Score	VRAM	tok/s	Why it is similar
BloomBigScience	—	122.4 GB	—needs > 24 GB	same size

Specifications

Publisher: windowsxp811203
Family: Qwen 3.8
Parameters: 180.0B
Active parameters: 61.6B per token
Architecture: Qwen4ExpForConditionalGeneration
Context Length: 262,144 tokens
Vocabulary Size: 248,320
Release Date: 2026-08-26
License: Other

Resources

Frequently Asked Questions

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

Qwen3.8 Flash Next Abliterated requires 108.4 GB of VRAM at Q4_K_M, or 360.4 GB at BF16. Full 262K context adds up to 10.7 GB (119.0 GB total).

VRAM = Weights + KV Cache + Overhead

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

KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 11 GB (at full 262K context)

VRAM usage by quantization

Q4_K_M

108.4 GB

Q4_K_M + full context

119.0 GB

Learn more about VRAM estimation →

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

No — Qwen3.8 Flash Next Abliterated requires at least 76.9 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 Abliterated?

For Qwen3.8 Flash Next Abliterated, Q4_K_M (108.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (128.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 76.9 GB.

VRAM requirement by quantization

Q2_K

76.9 GB~75%

Q4_K_M ★

108.4 GB~89%

Q5_K_M

128.6 GB~92%

Q6_K

148.9 GB~95%

Q8_0

180.4 GB~99%

BF16

360.4 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

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

Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run Qwen3.8 Flash Next Abliterated, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 108.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (128 GB).

Can I run Qwen3.8 Flash Next Abliterated locally?

Yes — Qwen3.8 Flash Next Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3.8 Flash Next Abliterated?

At Q4_K_M, Qwen3.8 Flash Next Abliterated can reach ~58 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)

Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.

Example: NVIDIA B200 → 37.1 GB active ÷ (8000 × 0.65) = 7.13 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 9.77 ms = ~102 tok/s

Estimated speed at Q4_K_M (108.4 GB)

NVIDIA B200

~102 tok/s

NVIDIA B300

~102 tok/s

NVIDIA GH200 Grace Hopper Superchip

~70 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 Abliterated?

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 Abliterated?

No single consumer GPU has enough VRAM to run Qwen3.8 Flash Next Abliterated at Q4_K_M (108.4 GB). Multi-GPU or professional hardware is required.

Which devices can run Qwen3.8 Flash Next Abliterated?

18 devices with unified memory can run Qwen3.8 Flash Next Abliterated at Q4_K_M (108.4 GB), including ASUS Ascent GX10, Mac Pro M2 Ultra (128 GB), Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Run Qwen3.8 Flash Next Abliterated

109.0 GB VRAM at 16K context · runs on one Instinct MI250X 128 GB

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1. Splits the model over 8 GPUs of one server (tensor parallel).
$vllm serve windowsxp811203/Qwen3.8-Flash-Next-Abliterated --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~396 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$windowsxp811203/Qwen3.8-Flash-Next-Abliterated-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
H200 SXM (141 GB)CheapestCheapest32 GB free	~69 tok/s	$14.47	Deploy on RunPodfrom $3.59/hr (opens in new tab)	141 GB · 32 GB free · ~69 tok/s · $14.47 / 1M tokens
H200 NVL (141 GB)32 GB free	~69 tok/s	$17.75	Deploy on Vast.aifrom $4.40/hr (opens in new tab)	141 GB · 32 GB free · ~69 tok/s · $17.75 / 1M tokens
B200 (192 GB)83 GB free	~102 tok/s	$16.22	Deploy on RunPodfrom $5.98/hr (opens in new tab)	192 GB · 83 GB free · ~102 tok/s · $16.22 / 1M tokens
B300 (288 GB)179 GB free	~102 tok/s	$18.83	Deploy on RunPodfrom $6.94/hr (opens in new tab)	288 GB · 179 GB free · ~102 tok/s · $18.83 / 1M tokens

Compare GPUs →