Reddit users this week ran a comparison test over at r/LocalLLaMA. What we find worth attention: when running large models locally, which is faster depends on whether the model fits in GPU VRAM — when it fits, llama.cpp is 2-3x faster; when it doesn't (e.g., the 63GB gpt-oss-120b), at 32-user concurrency FreeToken hits first-token response in 19 seconds while llama.cpp takes 139 seconds — roughly a 7x gap.
Our judgment: this isn't a simple replacement relationship, but two different approaches to the same problem. llama.cpp tries to compress model weights into VRAM as much as possible; FreeToken accepts overflow and focuses on optimizing data transfer between system memory and GPU over PCIe (the data channel between GPU and motherboard).
What this is
FreeToken is a relatively new local inference engine targeting scenarios where "model weights > VRAM capacity." It works by loading parts of MoE model parameters (Mixture of Experts — models with multiple internal parameter groups, only some of which activate during generation) on demand to relieve the bottleneck. llama.cpp is the most mature inference engine in the open-source community, with strengths in single-card high throughput and broad hardware compatibility.
The benchmark hardware: a single RTX 3090 (24GB VRAM) + PCIe 3.0 motherboard, running both a 26GB Gemma-4-26B-A4B (4-bit quantization, compressing model parameters to 4 bits to save space) and the 63GB gpt-oss-120b. Metrics were time to first token (TTFT) and sustained generation throughput, with concurrent users ranging from 1 to 32.
Industry view
Supporters argue that running 120B-class models locally is moving from "theoretically feasible" to "engineering practical" — good news for those who need enterprise data to stay on-premises and want affordable local AI. But three caveats are worth noting: First, the tester explicitly stated using a PCIe 3.0 motherboard, with FreeToken's PCIe link already maxed out — results may change on a 4.0 board. Second, the test only covers two MoE models and cannot be generalized to dense models. Third, FreeToken 0.1.2 is still in early release — direct crashes (OOM, out-of-memory) in 4-bit expert scenarios mean maturity is far below llama.cpp, and deploying it in production carries elevated risk.
Our judgment: competition among open-source inference engines has shifted from "can it run" to "in what scenarios does it run better." Local AI deployment is no longer "install one piece of software and you're done" — it's "assemble the right combination based on model size, concurrency, and hardware conditions."
Impact on regular people
Enterprise IT: When considering local deployment to handle sensitive data (contracts, internal documents, customer information), hardware procurement needs reassessment — 24GB VRAM on a single card is no longer enough for 120B models, but tools like FreeToken give "one consumer-grade GPU + large memory" setups engineering credibility.
Individual professionals: Running large models locally has moved one step closer from "geek toy," but it's still not at "download and use" stage — early adopters still need to be familiar with the command line and quantization parameters.
Consumer market: Hardware requirements are diverging — the configuration gap between laptops running 7B models and desktops running 120B models is widening, and the "AI PC" concept will further fragment.