Emerging Hardware Configurations for Local LLM Inference

Recent technical discussions on community forums, including Reddit, have highlighted a growing interest in multi-GPU configurations using AMD hardware for local Large Language Model (LLM) inference. Specifically, users are reporting performance metrics for setups utilizing dual AMD Radeon AI PRO R9700 cards, which offer a combined 64 GB of VRAM.

According to unverified benchmark data shared by a user on Reddit, a system equipped with two R9700 GPUs, a Ryzen 7500F processor, and 64 GB of DDR5 RAM was tested using the vLLM Radiance framework. The benchmarks focused on the Qwen 3.8 model series, providing a glimpse into the potential price-to-performance ratio of non-NVIDIA hardware for high-end local computing.

Benchmark Results for Qwen 3.8

The reported benchmarks utilized the BetterBench 0.2.2 tool with a 8k benchmark context. The results for the Qwen 3.8 27B model showed significant variance based on the weight format and quantization methods used:

  • Quark AWQ MXFP4: Achieved a weighted decode median of 111.4 tokens per second (tok/s), with a 1% low of 77.9 tok/s.
  • Native block FP8: Produced a weighted decode median of 87.6 tok/s, with a 1% low of 61.9 tok/s.

When testing the Qwen 3.8 Flash Next model using a tiered expert offload method, the median decode speed was recorded at 35.4 tok/s. The user noted that while the current setup utilizes a SATA SSD, which appeared sufficient for the Flash Next model, upgrading to a Gen 5 NVMe drive is expected to improve decode performance.

Cost-Efficiency and Thermal Management

The total build cost for the reported configuration was approximately €4,000. The user noted this is roughly €1,000 less than the cost of a single NVIDIA RTX 5090 with 32 GB of VRAM. This price differential has led some community members to characterize the dual R9700 setup as a high-value alternative for users prioritizing VRAM capacity over single-card peak performance.

However, the configuration presents specific engineering challenges. The user reported significant thermal variance between the two cards, noting that one unit runs 10–15 °C hotter than the other. To mitigate this, the user indicated plans to implement power limiting to 210 W and apply undervolting to manage heat within the chassis.

Hardware Evolution and Industry Context

The shift toward specialized hardware for AI tasks continues to drive discussions regarding efficiency. While some users debate the efficacy of AI-driven frame generation in gaming, the focus for local LLM enthusiasts remains on memory bandwidth and VRAM capacity.

The development of these local setups occurs alongside broader industry projections. For instance, OpenAI has stated that its AI agents are currently performing at the level of an “automated research intern,” with expectations to reach “automated AI researcher” levels by March 2028. Such advancements in software capability continue to place increasing pressure on the availability of high-performance hardware capable of running complex models locally.