Instructions to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF to start chatting

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

Qwen3-Next-80B-A3B-Instruct ROCmFP4 STRIX (GGUF) — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

First public ROCmFP4 quant of Qwen3-Next-80B-A3B-Instruct for AMD Ryzen AI Max+ 395 (gfx1151 / Radeon 8060S).

⚠️ Not compatible with upstream llama.cpp. Requires the charlie12345/ROCmFPX fork built with HIP + ROCmFP4 kernels.

Files

File	Size	Notes
`Qwen3-Next-80B-A3B-Instruct-Q4_0_ROCMFP4_STRIX.gguf`	39.69 GiB	4.28 BPW (quantize report)

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct BF16 source: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF BF16/ 4 shards, 148.51 GiB total (16.01 BPW)

Hardware / stack (validated)

Ryzen AI Max+ 395, gfx1151, 128 GB unified
ROCm 7.2.4
Fork: charlie12345/ROCmFPX @ b41ce12

Build recipe

llama-quantize \
  Qwen3-Next-80B-A3B-Instruct-BF16-00001-of-00004.gguf \
  Qwen3-Next-80B-A3B-Instruct-Q4_0_ROCMFP4_STRIX.gguf \
  Q4_0_ROCMFP4_STRIX

Q4_0_ROCMFP4_STRIX (type 105) is the Strix Halo attention-K/V quality recipe. Dry-run predicted 40641.96 MiB @ 4.28 BPW and the output matched exactly.

Serving

llama-server --host 127.0.0.1 --port 8080 \
  --model Qwen3-Next-80B-A3B-Instruct-Q4_0_ROCMFP4_STRIX.gguf \
  -dev ROCm0 -ngl 999 -fa on --no-mmap \
  --ctx-size 65536 --parallel 1 -b 2048 -ub 1024 -t 16 --poll 50 --jinja

Use the chat endpoint (/v1/chat/completions). Raw /completion with a bare instruction makes this instruct model degenerate into repetition loops.

Measured

Against the UD-Q4_K_XL GGUF of the same model on the same machine, same flags, chat endpoint:

	this quant	UD-Q4_K_XL
size	39.69 GiB	42.90 GiB
tok/s (median)	45.7 – 47.6	42.6
quality battery	24 / 24	24 / 24

The battery is 24 items: 8 code tasks graded by executing the generated function against assertions, 8 long-tail factual questions, 4 multilingual, 4 maths. Both quants scored 24/24, so quality is at parity and the size and throughput gains come for free.

Throughput caveat: the two arms were measured with different numbers of co-resident models, so treat the speed delta as directionally real but not precisely quantified.

Speculative decoding

Not available. The published MTP head (yomaytk/Qwen3-Next-80B-A3B-Instruct-MTP-HEAD-GGUF) cannot currently be used with llama.cpp:

It is an MTP-only GGUF, so it will not load via -md (that expects a complete draft model) — it fails with missing tensor 'blk.0.attn_norm.weight'.
Grafting its 20 blk.48.* tensors into the target (block_count 48 → 49, plus qwen3next.nextn_predict_layers = 1) produces a structurally correct GGUF that still will not load: missing tensor 'blk.48.ssm_conv1d.weight'.

The reason is architectural. Qwen3-Next is a hybrid — every 4th layer is full attention (indices 3, 7, 11 … 47) and the other 36 are Gated DeltaNet / SSM. Layer 48 lands on 48 % 4 == 0, so the loader types it SSM and demands ssm_conv1d, while an MTP layer is attention-shaped. llama.cpp models nextn generally (n_layer_all - n_layer_nextn exists) but does not exempt the nextn layer from this hybrid pattern.

ngram-map-k was also measured and came out at 1.065× — below a 1.15× ship gate.

License

Follow the base model (Qwen/Qwen3-Next-80B-A3B-Instruct) license terms.

Other public builds of this model

Compiled from Hugging Face repository metadata — file sizes, shipped files, quant variant as named by each repo. No third-party build was run or benchmarked here, so this table makes no speed or quality claim about any of them. It is here so you can see the size and format options at a glance and pick what fits your hardware.

Repository	Largest model file	Variant	Ships	Downloads	Likes
`nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4`	4.66 GiB	NVFP4	safetensors	23166	43
`surogate/Qwen3-Next-80B-A3B-Instruct-NVFP4`	4.66 GiB	NVFP4	safetensors	7	0
`a-ivanovitch/Qwen3-Next-80B-A3B-Instruct-NVFP4`	4.66 GiB	NVFP4	safetensors	104	1
`kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF` (this repo)	39.69 GiB	STRIX	single model file	77	0

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct. Generated from Hub metadata; download counts move over time.

Acknowledgements

This build would not exist without the work below. Please star and follow these projects — the quantisation format used here is their engineering, not mine.

ROCmFPX — maintained by charlie12345 / caf The ROCmFP4 / ROCmFPX tensor formats (ggml types 100–106) exist only in this fork. Every ROCmFP4 file in this repository was produced with its llama-quantize, and runs on its runtime. The fork also credits collaborators ciru-ai, Tom Turney, PlunderStruck and Aydan S., and acknowledges AMD for hardware support. Licensed MIT, based on upstream llama.cpp.

llama.cpp — ggml-org and contributors The inference engine, GGUF format and conversion tooling everything here is built on.

AMD ROCm The compute platform these builds target — ROCm 7.2.4 on gfx1151 / Radeon 8060S.

Base model authors — see base_model in the metadata above; all model weights, licences and capabilities are theirs. This repository contributes quantisation and measurement only.

If you use these files, please credit ROCmFPX alongside this repository.

Downloads last month: 205

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

4-bit

Model tree for kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF

Base model

Qwen/Qwen3-Next-80B-A3B-Instruct

Quantized

(75)

this model

kingjones777
/

Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF

Qwen3-Next-80B-A3B-Instruct ROCmFP4 STRIX (GGUF) — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

Files

Hardware / stack (validated)

Build recipe

Serving

Measured

Speculative decoding

License

Other public builds of this model

Acknowledgements

Model tree for kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF

Space using kingjones777/Qwen3-Next-80B-A3B-Instruct-ROCmFP4-STRIX-GGUF 1