Vulkan vs OpenCL vs CPU: Which Backend and When

Understand GPU acceleration options and how TokForge auto-detects the fastest path for your device.

Five Acceleration Paths

TokForge doesn't ask you to choose a backend. Instead, ForgeLab auto-detects your chipset's capabilities and selects the fastest path automatically. Here are the five acceleration paths available:

• • CPU: Universal fallback, works on all devices
• • OpenCL: Broad GPU compute across various manufacturers
• • Vulkan: Dedicated GPU path with tuned kernels
• • QNN (Qualcomm Neural Network): Snapdragon NPU/DSP optimization
• • Vulkan CoopMat: Newer cooperative matrices for the latest Mali GPUs (Dimensity 9400/9300)

These are not backends you manually switch between. TokForge's inference engine handles the selection, and ForgeLab shows you what's running under the hood.

ForgeLab detects your device's GPU capabilities automatically

Two Inference Backends

It's important to distinguish acceleration paths from inference backends. TokForge uses two backends:

• MNN: GPU-accelerated by default, uses any available path (OpenCL, Vulkan, CoopMat, QNN)
• GGUF/llama.cpp: CPU-focused with KleidiAI ARM acceleration for supported chips

The backend you use determines whether the model loads via MNN (GPU optimized) or llama.cpp (CPU optimized). The acceleration path is chosen within that backend.

GPU Path Comparison

Here's how the five paths compare in real-world scenarios:

Which Chipset Uses What

Device manufacturers use different SoCs (System-on-Chip). Here's how TokForge handles each major category:

Snapdragon 8 Gen 3 / 8 Elite

Latest flagship from Qualcomm. TokForge prioritizes:

• Vulkan CoopMat if drivers support it (newest phones with latest updates)
• OpenCL as primary fallback
• QNN for prefill experimentation (optional)

Snapdragon 8 Gen 2

Still prevalent in 2024-2025 flagships. Uses:

• OpenCL or Vulkan depending on driver availability
• CPU fallback stable and reasonable on this tier

MediaTek Dimensity 9400 / 9300

Premium MediaTek. Excellent Vulkan support:

• Vulkan is the primary path (Mali-G925/G925 GPU)
• GGUF Vulkan with cooperative matrix is the one Vulkan path that ships on by default here

MediaTek Dimensity 8000 Series

Mid-range MediaTek. Best performance:

• OpenCL if 8GB+ RAM available
• CPU with KleidiAI for budget memory configurations

Exynos 2400 / Samsung Flagship

Samsung's in-house flagship chip (Galaxy S24 series):

• Vulkan (Mali GPU, excellent support)
• Performance tier similar to Dimensity 9400

Budget & Mid-Range Phones

Snapdragon 6-7 series, Dimensity 6000-7000:

• CPU with KleidiAI (GGUF backend) if 4GB RAM
• OpenCL if 8GB+ RAM available
• GPU paths less optimized on budget hardware

KleidiAI: ARM CPU Acceleration

Not all devices have strong GPU support. That's where KleidiAI comes in. It's ARM's CPU-side acceleration for mobile inference.

What Does KleidiAI Do?

KleidiAI accelerates the i8mm (integer 8-bit matrix multiply) instruction set available on ARM Cortex-A78 and newer. When available, it provides:

• Faster prefill on supported chips compared to vanilla CPU; gains vary by model and quantization
• Compatible with GGUF models using Q4_0 quantization
• No GPU power draw: good for sustained inference

When to Use KleidiAI

KleidiAI is the default path when:

• GPU paths aren't available or reliable
• Running on budget phones (sub-$300)
• You need power efficiency over absolute speed
• Models are quantized to Q4_0 or lower

In the GGUF backend, just load a Q4_0 quantized model and KleidiAI activates automatically on supporting chips.

How to Check Your Acceleration Path

Curious what your device is running? ForgeLab shows it:

1. Open TokForge app → ForgeLab
2. Go to Device Capabilities tab
3. View all detected acceleration paths in order of priority
4. Run a test benchmark to see which was actually used
5. Check the Benchmark Report: it shows the exact backend and path

MNN Backend Controls: choose between Auto, CPU, OpenCL, or Vulkan

Real-World Performance Numbers

Theory is fine, but what do real phones actually achieve? Here's concrete data:

Example: MediaTek Dimensity 9400 with Vulkan CoopMat

Running GGUF models on the cooperative-matrix Vulkan path. For the actual numbers on hardware like yours, see the live leaderboard:

• Small models (around 3B): this is where cooperative matrix helps most
• Larger models (around 8B): the advantage over a well-threaded CPU run narrows considerably

Results vary widely by chipset; on many mid-range and budget devices the CPU path wins, which is why TokForge benchmarks instead of assuming.

First message compiles GPU kernels, subsequent messages are instant

Prefill vs Decode Speed

GPU acceleration helps both, but especially prefill (the first token):

• GPU Prefill: Much faster (entire input processed in parallel)
• GPU Decode: faster than CPU on supported devices; varies by model size
• KleidiAI Prefill: faster than vanilla CPU on supported chips

Does GGUF Work on Android?

Yes. GGUF is the model format for the llama.cpp engine, one of the two inference backends in TokForge, and it runs on every supported Android device on the CPU path, with the Vulkan GPU path available where the driver is solid. The MNN engine uses its own bundle format instead; both live side by side in the app, so a GGUF file downloaded from Hugging Face works without conversion.

Don't Overthink It

Here's the reality: TokForge auto-selects for you. You don't need to:

• Memorize which path is best for your chipset
• Install special drivers
• Tweak low-level settings
• Recompile anything

Just open the app, load a model, and inference begins on the fastest available path. If you're curious about performance tuning, run AutoForge once. It benchmarks your device and shows you the detailed results. That's it.

Most users never manually change backends. TokForge's auto-detection works well because it's been tuned across thousands of real-world devices.

Learn More

Interested in deeper optimization? Check out these related guides:

• AutoForge Guide: Automate device profiling and optimization
• TurboQuant Guide: Quantization techniques for faster inference
• Full Documentation: Deep dives into MNN and llama.cpp backends