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Run 125B+ MoE on consumer GPUs

A project called Strata, from developer Niko1221, claims to run the 125-billion-parameter Qwen3.8-Flash-Next mixture-of-experts model on a single 12GB NVIDIA GPU plus 64GB of system RAM by streaming only active expert weights over PCIe. Strata's own benchmark table reports 21.4 tokens/sec on an RTX 4070 with 64GB DDR5 and 52GB RAM allocation, versus out-of-memory failure for standard Ollama and 3.8 tokens/sec for vLLM with CPU offload. The engine uses predictive sparse routing that anticipates which of the 64 experts fire 1-2 tokens ahead, a tiered VRAM-RAM cache, and zero-copy pinned memory over asynchronous CUDA streams, with the code distributed via the GitHub repository Niko1221/Strata.

by read2 min views2 publishedNov 12, 2026
Run 125B+ MoE on consumer GPUs
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Powered by Niko1221's dynamic sparse router off. Run server-grade frontier models like Qwen3.8-Flash-Next 125B on a single 12GB NVIDIA graphics card and 64GB system RAM—with zero cloud dependencies.

git clone https://github.com/Niko1221/Strata.git
cd Strata
.\START-HERE.bat

How Strata Solves the VRAM Wall #

Traditional inference engines crash when a 125B model exceeds GPU memory. Strata utilizes the sparse nature of Mixture-of-Experts architectures to stream only active weights:

Predictive Sparse Routing

In MoE models like Qwen 125B, only 2-8 out of 64 experts fire per token. Strata predicts which expert layers will be called 1-2 tokens in advance, queuing them for instant DMA PCIe transfer.

Tiered VRAM-RAM Cache

Critical attention heads and frequently triggered experts reside permanently inside your 12GB GPU VRAM. The remaining 90GB of dormant weights stream through high-speed 64GB DDR5 memory.

Zero-Copy Pinned Memory

Weights are transferred over asynchronous CUDA streams via PCIe 4.0/5.0 directly into tensor cores, completely bypassing CPU compute cycles and preventing stutter.

System Requirements Matrix #

NVIDIA RTX 3060 / 4060 / 3070 12 GB VRAM

64 GB DDR4/DDR5 (Dual Channel)

PCIe 3.0/4.0 NVMe SSD (50GB+ Free)

12 - 18 Tokens/sec (MoE Offloaded)

Recommended Models:

  • Qwen3.8-Flash-Next 125B (4-bit Q_K)
  • DeepSeek-V2-Lite 16B

NVIDIA RTX 4070 Ti / 4080 / 3090 16 GB - 24 GB VRAM

64 GB - 128 GB DDR5

PCIe 4.0 NVMe SSD (7000 MB/s read)

25 - 38 Tokens/sec

Recommended Models:

  • Qwen3.8-Flash-Next 125B (5-bit Q_M)
  • Mixtral 8x22B Instruct

NVIDIA RTX 4090 (24 GB) or Dual RTX 3090 24 GB - 48 GB VRAM

128 GB DDR5 Quad Channel

PCIe 5.0 / Gen4 NVMe DirectStorage

45 - 65 Tokens/sec

Recommended Models:

  • Full-context 125B-236B MoE Models
  • Continuous Batching Serving

Engine Performance Comparison #

Testing Qwen3.8-Flash-Next (125 Billion Parameters) on a standard single RTX 4070 (12GB VRAM) paired with 64GB DDR5:

Inference Engine Hardware Target Tokens / Sec RAM Allocation Status & Stability
Strata LLM (Niko1221) RTX 4070 (12GB) + 64GB RAM 21.4 tok/s 52 GB Native Support (Zero OOM Crash)
Ollama (Standard) RTX 4070 (12GB) + 64GB RAM Fail / OOM Exceeds limit Out of Memory / Requires Quantization Drop
vLLM (CPU-Offload) RTX 4070 (12GB) + 64GB RAM 3.8 tok/s 58 GB Severe PCIe Bus Bottleneck

Frequently Asked Questions #

Everything you need to know about setting up Strata and configuring MoE quantization.

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