Instructions to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB 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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
# Run inference directly in the terminal:
llama cli -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
# Run inference directly in the terminal:
llama cli -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
# Run inference directly in the terminal:
./llama-cli -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

Use Docker

docker model run hf.co/mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

LM Studio
Jan

vLLM

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

Ollama
How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with Ollama:
```
ollama run hf.co/mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
```

Unsloth Studio

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB 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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB 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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB to start chatting

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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": "mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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 "mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB" \
  --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 mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with Docker Model Runner:
```
docker model run hf.co/mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB
```

Lemonade

How to use mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-IQK-RPi5-16GB-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-35B-A3B-IQK-RPi5-16GB — Custom IQK Quantization for Raspberry Pi 5 (16GB)

Quantized by: mtrpires
Base model: Qwen/Qwen3.5-35B-A3B
Runtime: ik_llama.cpp
Target hardware: Raspberry Pi 5 16GB (Cortex-A76)

Why this quantization exists

The Qwen3.5-35B-A3B is a Mixture-of-Experts model with 35B total parameters but only 3.3B active per token (256 experts, 8 routed + 1 shared active). This architecture makes it remarkably efficient at inference time — in benchmarks it surpasses the Qwen3-235B-A22B despite activating far fewer parameters, including 69.2% on SWE-bench Verified.

The problem: every standard quantization of this model exceeds 15GB on disk, requiring ~17GB+ of RAM. This puts it out of reach for the Raspberry Pi 5 16GB using conventional tools.

This quantization uses a mixed IQK recipe — asymmetric quantization that exploits the MoE architecture's properties: attention and shared expert layers (always active, quality-critical) are kept at high precision, while routed expert layers (rarely co-activated, quality-resilient) are compressed aggressively using ARM NEON-optimized IQK kernels from ik_llama.cpp.

The result: 11.38GB on disk, ~12.5GB in RAM — fits comfortably in 16GB.

Quantization recipe

Tensor group	Quantization	Rationale
Attention layers (all)	`q8_0`	Always active per token — critical for reasoning
Shared experts (always active)	`q8_0`	Backbone of MoE — activated every token
Routed experts, layers 0–7	`iq4_ks`	Early layers more sensitive — IQK NEON optimized
Routed experts, layers 8+	`iq2_ks`	Bulk of model — rarely co-activated — IQK NEON optimized
Token embeddings / output	`q6_K`	Used every token — good quality/size balance

The IQ4_KS and IQ2_KS quantization types are exclusive to ik_llama.cpp and have hand-written ARM NEON assembly kernels that make them faster than Q4_K_M on Cortex-A76 despite the lower bit depth.

Source: Q8_0 GGUF from bartowski
Imatrix: ubergarm (calibrated on chat, coding and long-context tool-calling data)

Benchmark results

Tested on Raspberry Pi 5 16GB with ik_llama.cpp (HEAD, March 2026), -t 3 --threads-batch 4 -c 16384 --mlock:

Test	Result
Prompt processing (pp512)	30.85 ± 0.03 t/s
Token generation (tg50)	4.53 ± 0.00 t/s
Model size	11.38 GiB
RAM usage (with 16K context)	~13GB
Context window used	16,384 tokens

4.5 t/s is comfortable reading speed for a personal assistant workload. Prompt processing benefits from --threads-batch 4 (+26% vs 3 threads).

ARM NEON status: __ARM_FEATURE_DOTPROD confirmed active. The HAVE_FANCY_SIMD is NOT defined warning in ik_llama.cpp output refers exclusively to AVX-512 (x86) and can be safely ignored on ARM.

How to run

Requirements

Raspberry Pi 5 16GB (8GB will not work — insufficient RAM)
ik_llama.cpp compiled from source
Active cooling recommended for sustained inference

Compile ik_llama.cpp on the Pi

git clone https://github.com/ikawrakow/ik_llama.cpp --depth=1
cd ik_llama.cpp
cmake -B build \
  -DGGML_NATIVE=ON \
  -DGGML_BLAS=ON \
  -DGGML_BLAS_VENDOR=OpenBLAS \
  -DGGML_LTO=ON \
  -DBUILD_SHARED_LIBS=OFF \
  -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j4

Note: -DGGML_NATIVE=ON automatically detects Cortex-A76 capabilities (ARM NEON, dot product, fp16). The explicit -march= flags are not needed.

Run as server

./build/bin/llama-server \
  -m Qwen3.5-35B-A3B-IQK-RPi5-16GB.gguf \
  --host 127.0.0.1 \
  --port 8080 \
  -t 3 \
  --threads-batch 4 \
  -c 16384 \
  --mlock \
  --log-disable

Run benchmark

./build/bin/llama-bench \
  -m Qwen3.5-35B-A3B-IQK-RPi5-16GB.gguf \
  -t 3 -n 50

OpenAI-compatible API

The server exposes an OpenAI-compatible API at http://localhost:8080/v1. Compatible with any client that supports OpenAI's chat completions endpoint.

How this quantization was generated

The quantization was produced on a MacBook Pro M4 Pro (48GB) using ik_llama.cpp compiled CPU-only (Metal is not supported by ik_llama.cpp).

llama-quantize \
  --imatrix imatrix-Qwen3.5-35B-A3B-BF16.dat \
  --allow-requantize \
  --attn-q-type q8_0 \
  --attn-k-type q8_0 \
  --attn-v-type q8_0 \
  --attn-qkv-type q8_0 \
  --attn-output-type q8_0 \
  --token-embedding-type q6_K \
  --output-tensor-type q6_K \
  --custom-q "ffn_down_shexp=q8_0,ffn_gate_shexp=q8_0,ffn_up_shexp=q8_0" \
  --custom-q "blk\.[0-7]\.ffn_down_exps=iq4_ks,blk\.[0-7]\.ffn_gate_exps=iq4_ks,blk\.[0-7]\.ffn_up_exps=iq4_ks" \
  --custom-q "ffn_down_exps=iq2_ks,ffn_gate_exps=iq2_ks,ffn_up_exps=iq2_ks" \
  source-Q8_0.gguf \
  output.gguf \
  iq2_ks \
  12

Source GGUF: bartowski Q8_0 (~48GB) chosen because the original BF16 weights (~72GB) exceed the MacBook's 48GB RAM, and re-quantizing from Q8_0 introduces less than 0.05 PPL degradation vs. starting from BF16.

Known limitations

Context window: 16K is the practical limit on 16GB RPi5 with this model. 32K is possible but leaves very little system RAM headroom.
Speed: 4.5 t/s generation is usable for a personal assistant but not for interactive applications requiring fast responses.
IQK quants: IQ2_KS and IQ4_KS are ik_llama.cpp exclusive — this GGUF will not load correctly in standard llama.cpp or Ollama.
8GB RPi5: Will not work. The model requires ~12.5GB RAM at 16K context.

Compared to standard quantizations

Quantization	File size	Est. RAM	Fits 16GB RPi5?	Source
Q3_K_S (bartowski)	15.3 GB	~17.5 GB	❌	Standard
Q3_K_M (bartowski)	16.4 GB	~18.9 GB	❌	Standard
UD-IQ4_XS (Unsloth)	17.5 GB	~20 GB	❌	Standard
Q4_K_M (bartowski)	22.0 GB	~25 GB	❌	Standard
IQK-RPi5 (this)	11.38 GB	~12.5 GB	✅	Custom

Use case context

This quantization was developed as part of a hybrid AI personal assistant running on a Raspberry Pi 5, where the local model acts as a privacy-preserving executor for tool use, code generation, and document processing — with cloud models (Gemini Flash/Pro) handling planning and review. The 35B-A3B was chosen over the 9B dense model specifically for its superior reasoning and coding capabilities despite the memory constraints.

Credits

Base model: Qwen Team (Alibaba Cloud)
Runtime: ikawrakow (ik_llama.cpp)
Imatrix: ubergarm
Source GGUF: bartowski

License

This quantization inherits the base model license: Apache 2.0. See Qwen3.5-35B-A3B for details.

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GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for mtrpires/Qwen3.5-35B-A3B-IQK-RPi5-16GB

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Qwen/Qwen3.5-35B-A3B-Base

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Qwen/Qwen3.5-35B-A3B

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