RTX 3060 Tests: How GPU Memory Allocation Shifts LLM Execution Strategy
Abhishekcur · x · 2026-08-11
While testing inference performance with sglang and qwen3-4b on an RTX 3060, the author discovered that GPU memory availability directly alters how the inference engine executes the model.
Launching with 75% static memory fraction and requesting 8192 KV tokens, the engine profiled the actual headroom and allocated only 731 tokens for the KV cache. With only 1.93 GiB left, the engine hit a safety gate disabling prefill CUDA graph capture (requiring 4.00 GiB) and fell back to eager mode, though decode CUDA graph capture succeeded.
The author notes this fallback is a heuristic gate on the auto-selected backend, not a hard limit. The investigation will continue bottom-up—from memory budget and KV cache capacity to execution strategies and CUDA graphs—to understand how these underlying decisions impact latency and throughput.
Related event: RTX 3060 Test Shows GPU VRAM Alters LLM Inference Strategy(2 posts)→
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