FreeToken 4gb vram: Setup Guide & Hardware Limits

FreeToken is a research system for serving large Mixture-of-Experts models on personal hardware. It is not a game, character build, or download-code topic. The central question behind FreeToken 4gb vram is whether a very small GPU memory budget can participate in inference while the complete expert pool stays elsewhere.

The system separates model data into two broad groups. Non-expert weights remain on the GPU, while routed expert weights are stored in a CPU-resident pool. Remaining VRAM becomes an elastic cache for complete layer–expert entries. This design allows the model to operate even when the entire expert pool exceeds GPU capacity, but lower VRAM generally means more cache misses and greater dependence on PCIe transfer or CPU execution.

The source paper documents a cross-hardware evaluation from an 8 GB laptop GPU through workstation-class hardware. It does not confirm a tested 4 GB configuration. Therefore, a 4 GB setup should be viewed as a constrained experiment rather than a guaranteed deployment target.

FreeToken uses a two-level expert-memory hierarchy. The host keeps the complete routed-expert pool, and the GPU maintains a shared LRU cache. Each cache slot stores the tensors needed to evaluate one layer–expert pair. This logical organization allows the cache to follow routing behavior instead of relying on a fixed expert placement chosen at startup.

During decoding, the router identifies active experts. Cached experts execute directly on the GPU. Missing experts are divided into two groups: some are transferred into GPU cache slots, while others execute directly from the CPU-resident pool. The division is based on measured bandwidth rather than a universal hardware specification.

The paper defines the approximate fill ratio as:

q* ≈ m × BP / BH

Here, m is the number of missing experts, BP is measured pinned transfer bandwidth, and BH is measured CPU-side expert-processing bandwidth. The practical meaning is simple: faster PCIe transfer favors more cache fills, while stronger CPU memory bandwidth makes direct CPU execution more useful.

A 4 GB GPU has a much smaller working area than the documented 8 GB laptop tier. That difference affects more than the number of experts that can be cached. The same memory budget must also accommodate GPU-resident model components and context-related state. As conversations or agent sessions grow, KV cache demand can increase while expert-cache capacity becomes harder to preserve.

With less cache space, FreeToken may encounter more cold misses, working-set churn, and host-to-device movement. The system’s CPU execution path can help absorb some misses, but it does not remove the cost of reading expert weights from host memory. Performance will therefore depend on the model format, prompt length, CPU bandwidth, PCIe link, operating-system memory behavior, and concurrent applications.

The source reports that an 8 GB laptop configuration served Qwen3.6 using an official NVFP4 release and sustained measurable decode performance. That result is useful as a reference point, but it should not be extrapolated directly to 4 GB. A smaller GPU may need a more aggressive split between KV cache and expert cache, and some model configurations may not leave enough room for a practical runtime.

A useful evaluation sequence is to begin with a short, single-turn request. Then test a longer prompt, followed by several turns that change the context. This separates cold-start behavior from sustained decode behavior and reveals whether the GPU cache remains effective as memory demand changes.

The most important preparation step is identifying which resource is limiting the run. FreeToken is designed to adapt its expert cache at scheduler safe points without restarting the engine or reloading the host-resident pool. That flexibility is especially relevant on personal computers, where browsers, desktop compositors, and other applications can change available VRAM.

The host-side pool also benefits from an optimized layout. The paper describes the FreeToken Weight format, which stores expert weights in a runtime-friendly bank arrangement. Direct reads into the final host layout can reduce startup work, while delayed memory pinning avoids faulting in empty pages only to overwrite them.

FreeToken’s design changes the meaning of “fit” for an MoE model. A model does not necessarily need every expert resident in VRAM at once, because inactive experts can remain in host memory. However, fitting the model in an addressable memory hierarchy is different from serving it at interactive speed. Every cache miss creates a scheduling decision and a memory-bandwidth cost.

The system combines several mechanisms to reduce that cost:

• Full-layer double buffering overlaps prefill transfers with GPU computation.
• Semantic-aware state caching preserves recurrent states and prefixes around agentic editing boundaries.
• Shared LRU expert caching follows recent routing locality across decode steps.
• Bandwidth-adaptive execution divides misses between GPU cache fills and CPU execution.
• Elastic memory management adjusts the expert-cache and KV-cache split as conditions change.

For a 4 GB system, the safest conclusion is conditional: FreeToken’s architecture offers a path for experimenting with constrained GPU memory, but the available source does not establish 4 GB as an official or generally practical target. If the runtime cannot reserve enough memory for its required GPU components, or if cache churn makes latency unacceptable, the limitation is architectural and hardware-specific rather than a simple configuration error.