I tried running a 1.56TB MoE model on a 6GB RTX 4050 Laptop, Here’s the result
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.
Similar Articles
EXPERIMENT: Qwen3.8-2.4T-A95B running locally on an RTX 5090 + RTX 5060 Ti at ~0.80 tok/s
An experiment running the Qwen3.8-2.4T-A95B MoE model locally on dual consumer GPUs (RTX 5090 + 5060 Ti) with llama.cpp, achieving ~0.8 tok/s with MTP speculative decoding enabled.
24+ tok/s from ~30B MoE models on an old GTX 1080 (8 GB VRAM, 128k context)
A developer demonstrates running MoE models like Qwen 3.6 35B-A3B and Gemma 4 26B-A4B at 24+ tok/s on an old GTX 1080 (8GB VRAM) with 128k context using llama.cpp with MoE offloading and TurboQuant KV cache quantization, revealing optimization tricks for Gemma's MTP speculative decoding.
Running Qwen3.6-35B-A3B on a laptop RTX 4060 (8GB) — what worked, what didn't, and a surprising speculative-decoding result
A detailed account of running the Qwen3.6-35B-A3B MoE model on an 8GB laptop GPU, covering effective optimizations like --no-mmap and VRAM headroom, unexpected findings where speculative decoding improved speed by 26% contrary to benchmarks, and pitfalls with Windows and CPU bottlenecks.
@analogalok: my 8 GB VRAM gaming laptop is absolutely going to hate me for this. but I still did it. ran a 31b dense model (Gemma 4 …
User runs Gemma 4 31B dense model on 8GB VRAM gaming laptop at ~3 tokens/sec using llama.cpp with MTP speculative decoding, demonstrating feasibility of running a 31B dense model on consumer hardware and proposing agentic workflows where a fast MoE model routes to this slower dense model for hard tasks.
Auto-fit vs tuned MoE offload: 564 → 1330 pp tok/s, unchanged decode (Qwen3.6-35B-A3B Q6 / RTX 3090)
A developer benchmarks Qwen3.6-35B-A3B Q6 on an RTX 3090, showing that offloading eight MoE expert layers to CPU and increasing batch sizes improves prompt processing by 2.36× (564→1330 tok/s) with no decode speed regression, using evolutionary search to find the tuning config.