I tried running a 1.56TB MoE model on a 6GB RTX 4050 Laptop, Here’s the result

Reddit r/LocalLLaMA News
mixture-of-experts nvidia-rtx-4050 laptop-inference mxfp4 quantization memory-optimization deep-learning

Summary

Testing a 1.56TB Mixture-of-Experts model on a 6GB RTX 4050 laptop, requiring patched memory streaming with NVMe to achieve 0.106 tokens/s decode speed.

The Test Bench Setup I tested running a massive 1.56TB Mixture-of-Experts (MoE) checkpoint (96 shards, 93 layers, 896 experts/layer, ~4.46 bits/param MXFP4) on a budget gaming laptop. Laptop: HP Victus 15 GPU: NVIDIA RTX 4050 Laptop (6GB GDDR6, 96-bit interface @ 192 GB/s bandwidth, Ada Lovelace AD107) System RAM: 16GB DDR4 (Dual-Channel) Storage: Stock PCIe 4.0 NVMe SSD (Benchmarked at ~3,500 MB/s sequential read) Usable Fast Memory Budget: ~19GB total (~13.5GB available RAM + 5.5GB available VRAM after OS overhead) Hardware Context: The RTX 4050 uses 4th-Gen Tensor Cores. Because it lacks native MXFP4 hardware acceleration (which arrived with the 50-series/Blackwell), the engine dequantizes MXFP4 weights down to FP16/INT8 in memory before Tensor Core execution. Run 1: Standard Engine Architecture (Out-of-Box Attempt) Result: Hard Crash / OOM before Token 1 Under the default engine setup, dense attention weights and shared parameters stay permanently resident in RAM while sparse expert weights stream off the NVMe. Required Resident Memory: ~40GB at 4-bit precision. Available Memory: ~19GB. The engine instantly hit an Out-Of-Memory (OOM) allocation error during the initial memory-map phase and terminated before processing a single prompt token. Run 2: Patched Engine (Streaming Dense Weights off SSD) To force the model to execute on 16GB RAM, I patched the runtime to treat RAM purely as a thin LRU cache, forcing the engine to stream non-cached dense weights and attention layers directly off the NVMe alongside the active experts. Real Benchmark Numbers: Initial Load & Mmap Time: 5 minutes, 42 seconds (dominated by mmap overhead and parsing headers across all 96 shard files). Prefill (7-token prompt): 11.8 seconds (loads each layer's weights once across all positions). Decode Speed: 9.4 seconds / token (~0.106 tok/s). Where the Bottleneck Settled: At 8 active experts per layer across 93 layers, each token required thrashing: Active Experts: 8 × 93 × ~18MB ≈ 13.4GB Uncached Dense/Attention Weights: ≈ 19.6GB Total Disk I/O per token: ~33GB / token At a sustained 3.5 GB/s read limit on the stock NVMe, the drive was pegged at 100% active time throughout the entire generation. used grammarly for formatting since english isn't my native language
Original Article

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