# Looking for Windows users with 2–8 GB GPUs to test a low-VRAM local LLM runtime > Source: > Published: 2026-09-04 02:30:52+00:00 Hi everyone, I’ve been developing a Windows local-LLM application called **StreamAI**, specifically focused on running useful language models on systems with limited GPU memory. My development machine is deliberately modest: **AMD Radeon RX 570 — 4 GB VRAM** **32 GB system RAM** On that system, the current beta has qualified two modes: **Fast mode** Qwen2.5 1.5B Instruct About **1.1 tokens/sec** **Quality mode** Qwen1.5 7B Chat About **0.45 tokens/sec** The goal of the project is not to compete with high-end GPUs. I’m trying to determine how useful local LLM inference can remain on older or limited-VRAM Windows hardware. StreamAI uses a memory-bounded CPU/GPU streaming approach so that model execution does not depend on keeping the entire working model resident in GPU memory. I have reached the point where testing only on my own RX 570 is no longer useful. I am looking for a small number of Windows testers with different hardware. I am especially interested in: I would initially like to test on roughly **6–10 different machines** rather than distribute the beta widely. The beta includes an automated hardware and inference qualification process. After testing, it creates a small results ZIP that the tester can inspect and manually send back to me. **There is no automatic telemetry or automatic uploading of results.** The beta is currently a compiled Windows application and uses a short-lived machine-bound tester license while I keep the test group controlled. If you are interested, please reply with: `GPU / VRAM / System RAM / Windows version` For example: `GTX 1650 / 4 GB / 16 GB RAM / Windows 11` I am interested in failures just as much as successes. My goal is to eventually publish the hardware results, performance measurements, and practical limits so the information is useful to other people working with constrained hardware. The main question I am trying to answer is: **How low can the hardware requirements go before local LLM inference stops being genuinely usable?** If there is interest, I’ll share the results from the different machines here as the testing progresses