I recently picked up an AMD Radeon Pro V340L for $93 CAD and successfully got both GPUs running LLM inference under Windows 11 using Vulkan and llama.cpp.
The V340L is a dual-GPU enterprise accelerator with 16 GB of HBM2 memory split between 2 GPUs. While it wasn't designed for Windows 11 desktop use, I've validated a working configuration that allows both GPUs to run inference simultaneously.
Getting It Working
The key was using AMD PRO Edition 22.Q4 with a modified driver INF to support the V340L's PCI device revision.
The V340L-Enablement project provides tooling to prepare the driver. On my Windows 11 installation, I temporarily disabled driver signature enforcement and installed the modified driver manually (you can sign the driver to avoid this.)
Both GPUs were then recognized by Windows and detected by llama.cpp's Vulkan backend.
One important configuration for llama.cpp was required to achieve usable performance:
set GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1
This avoids a slow Vulkan memory allocation path and made a substantial difference in inference performance.
Performance
Using Qwen3-4B Q4_K_M, I ran two independent llama.cpp inference benchmarks simultaneously, one on each GPU.
| Benchmark | Combined Performance |
|---|---|
| Prompt processing | 724.47 tokens/sec |
| Text generation | 88.39 tokens/sec |
Each GPU delivered about 44.2 tokens/sec during simultaneous text generation, with virtually identical performance.
These results represent two concurrent inference workloads, rather than a single model distributed across both GPUs.
What's Next?
For less than $100 CAD, I've now got a working dual-GPU Vulkan inference setup under Windows 11, without modifying any firmware.
Next up is integrating the V340L into LLM Controller Pro, with each GPU operating as an independent inference worker.
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