The Tool Desk
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What “3 GiB of RAM” means in this result
Edge0’s approximately 2.9 GiB figure is the peak reported by the MLX allocator for a short-context workload. It is not the full process memory, the Mac’s total unified memory, or the disk space required to install the model. The test machine had 24 GB of unified memory, and the project places about 23 GB of model weights on SSD. Those numbers describe different things: the active working set in memory, the computer’s available memory, and the complete weight storage.
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So the result supports a narrow claim: a particular 35B MoE inference pipeline can keep its measured active memory below 3 GiB under the reported test conditions. It does not show that a 35B model can be downloaded, stored, or run entirely using only 3 GiB of total memory.
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An MoE model routes each input through selected experts, rather than using every expert for every computation. That reduces the parameters involved in a given computation, but the complete pool of expert weights still has to be stored somewhere.
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Edge0’s approach stores those weights on SSD and loads needed pieces into memory on demand. Its trained Prerouter predicts which experts will be needed soon, allowing some SSD reads to overlap with computation. The project also describes an unmerged recovery LoRA trained for its quantized, predicted-routing path. This is a particular inference pipeline—not an automatic benefit of choosing any MoE model.
What Edge0 reports for its 35B setup
These are project-reported results from Edge0’s 2026 documentation, not independent measurements. The project describes a roughly 3.3k-token prompt, ten warm-up sampling steps, a timed 200-token decode, and two runs per model tier.
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| Measure | Reported result | How to interpret it |
|---|---|---|
| Weight footprint | About 23 GB | Storage for the 35B tier’s weights; this is not the active-memory figure. |
| Peak active memory | About 2.9 GiB | MLX allocator peak for a short-context workload, not total process memory. |
| Decode speed | 14.9–17.7 tokens per second | Reported for the 35B tier on a Mac mini M4 Pro with 24 GB unified memory. |
| Average benchmark score | 79.2 versus 83.2 | Edge0 pipeline versus its fp16 base model across five public benchmark evaluations. The comparison includes quantization, adapters, and predicted routing; changes vary by task. |
| Prerouter decoding speedup | Up to 59% | Maximum reported in alternating Prerouter and native-routing tests using the same model, adapter, and workload; not a general expected gain. |
The project distinguishes cold prefill—the first request after startup—from warm prefill when pages are already in the file cache. Cache state matters because the SSD supplies weights. Edge0’s detailed project table gives 14.9–17.7 tokens per second for the 35B tier, while its September 2026 preprint abstract summarizes a 20-token-per-second result. Those are different source summaries, so the detailed table’s range is the more specific figure to use for the stated tier.
Edge0 describes the practical tradeoff this way: “The SSD holds the complete model while memory carries the immediate working set, so available memory, cache state, and SSD speed all affect generation speed.” Edge0 project documentation, September 10, 2026.
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What you need to reproduce the idea
The published Edge0 page identifies MLX on Apple Silicon as its available backend. The reported memory, speed, and quality figures are therefore specific to that setup; they do not establish equivalent results on Windows, Linux, discrete GPUs, or every SSD. A 3 GiB allocator peak should not be treated as a minimum whole-machine RAM specification.
- Storage: allow space for roughly 23 GB of weights for the cited 35B tier, plus whatever space the surrounding software and files require.
- Memory: distinguish allocator peak from total process and system memory. The reported test ran on a 24 GB unified-memory Mac.
- Disk performance and cache: expect generation behavior to depend in part on how quickly needed weights can be read and whether relevant pages are already cached.
- Backend: the published Edge0 results apply to its MLX-on-Apple-Silicon setup, not automatically to other hardware or operating systems.
Does quantizing a model file guarantee low RAM use?
No. Quantization can reduce the amount of memory needed to represent weights, but the runtime’s loading path matters. Hugging Face’s Transformers documentation explains that GGUF files can contain model metadata and tensors in quantized data types. It also notes that its packed path for Qwen3.5 MoE depends on relevant kernels to keep weights packed; without those kernels, a loader may dequantize weights at load. Architectures other than Qwen3.5 and Qwen3.5 MoE use the legacy loader, which dequantizes.
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In other words, downloading a quantized file alone does not guarantee that weights remain packed in memory or produce a particular RAM footprint. Check the model architecture and the inference software’s supported kernels and loading behavior. The documentation describes the general GGUF path, while a model card’s instructions may apply only to its specific conversion or variant.
Quick Recap
How to read the result without overgeneralizing
- It is evidence that a 35B MoE can be served with under 3 GiB of measured active memory in a particular SSD-streaming setup.
- It is not evidence that the entire 35B model fits in 3 GiB: the cited weights alone occupy about 23 GB on SSD.
- The reported speed and benchmark figures belong to Edge0’s pipeline and stated Apple Silicon test configuration; they are not universal expectations for other models or computers.
- Memory comparisons are meaningful only when the measurement type, workload, cache state, hardware, and backend are comparable.
Sources
- Edge0: 35B Models in 3 GB of Memory, September 10, 2026.
- The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction, September 16, 2026.
- Hugging Face Transformers GGUF quantization documentation.
- Smoffyy Qwen3.6 35B A3B Pure GGUF model card, a community conversion with variant-specific guidance.
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