Instructions to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark", filename="Qwen3.5-27B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M # Run inference directly in the terminal: llama-cli -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M # Run inference directly in the terminal: llama-cli -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
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 JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
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 JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
Use Docker
docker model run hf.co/JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with Ollama:
ollama run hf.co/JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
- Unsloth Studio new
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark 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 JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark 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 JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark to start chatting
- Pi new
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
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": "JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
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 JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with Docker Model Runner:
docker model run hf.co/JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
- Lemonade
How to use JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JohnTdi/Qwen3.5-Unsloth-GGUF-R9700-Benchmark:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-Unsloth-GGUF-R9700-Benchmark-Q4_K_M
List all available models
lemonade list
Qwen3.5 GGUF Models โ RDNA4 R9700 Benchmark
Exact GGUF files used in the Comprehensive LLM Benchmark on AMD Radeon AI PRO R9700 (RDNA4).
These files are provided so anyone with an R9700 can reproduce the results exactly.
Note: The 35B file was downloaded from unsloth/Qwen3.5-35B-A3B-GGUF on 2025-02-25. Unsloth has since updated the file โ the current version on their repo is larger (20.71 GiB vs 18.34 GiB). This repo preserves the original file used in the benchmark.
Models
| Model | Type | Total Params | Active/Token | File Size | Quantization | Source |
|---|---|---|---|---|---|---|
| Qwen3.5-35B-A3B | MoE | 34.66B | ~3.5B | 18.34 GiB | UD-Q4_K_XL (file_type=Q4_K_M) | unsloth |
| Qwen3.5-27B | Dense | 26.90B | 26.90B | 15.59 GiB | Q4_K_M | unsloth |
System Configuration
| Component | Details |
|---|---|
| GPU | AMD Radeon AI PRO R9700 (gfx1201, RDNA4, 32 GB GDDR6, 64 CUs) |
| Memory bandwidth | 640 GB/s (MCLK 1258 MHz) |
| PCIe | PCIe 5.0 x16, 32 GT/s |
| CPU | AMD Ryzen 9 9900X 12-Core |
| RAM | 64 GB DDR5 |
| OS | Ubuntu 24.04.4 LTS, Kernel 6.19.8 |
| Mesa (RADV) | 25.2.8 |
| llama.cpp | commit dc8d14c58 (build 8554) |
Best Results
Qwen3.5-35B-A3B (MoE)
| Metric | RADV | AMDVLK |
|---|---|---|
| Best decode | 149.5 t/s | 163.7 t/s (gfx+rm_kq=1) |
| Best prefill pp2048 | 3,075 t/s (ub=2048) | 2,170 t/s |
Qwen3.5-27B (Dense)
| Metric | RADV | AMDVLK |
|---|---|---|
| Best decode | 32.5 t/s (ASPM perf) | 33.2 t/s (ASPM perf) |
| Best prefill pp2048 | 993 t/s (Mesa 25.3.6) | 207 t/s |
Key Findings
rm_kq=1is the single most impactful code change: +1% RADV, +2% AMDVLK MoE, +13% AMDVLK dense- PCIe ASPM=performance gives +10.8% dense decode on RADV
- gfx queue helps AMDVLK MoE (+4.7%) but hurts AMDVLK dense (-8%)
- RADV wins overall (best prefill, competitive decode)
- Dense models reach 79-83% bandwidth utilization with optimizations
Full Results
See BENCHMARK_COMPREHENSIVE_R9700.md for the complete benchmark with 50+ configurations tested.
License
Apache 2.0 (following the original model license).
Credits
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