--- title: "What Is the Clover Diffusion LLM and How Fast Is It?" url: "https://goremy.ai/blog/articles/clover-diffusion-llm-35b-speed-test" author: Luis Chavez-Mattos editor: Luis Chavez-Mattos editor_title: Director, Product published: "2026-11-07T12:00:10.755Z" updated: "2026-11-10T17:09:40.222Z" topic: AI Models & Industry description: Clover from Diffuse Space AI is a 35B diffusion language model with 3B active parameters. See how it works and how it fared against Qwen 3.6 Flash. --- # What Is the Clover Diffusion LLM and How Fast Is It? > Clover from Diffuse Space AI is a 35B diffusion language model with 3B active parameters. See how it works and how it fared against Qwen 3.6 Flash. Edited by Luis Chavez-Mattos, Director, Product · Published 2026-11-07 · Updated 2026-11-10 ## What is the Clover diffusion LLM? Clover is a diffusion-based language model from Diffuse Space AI. It has 35 billion total parameters and 3 billion active parameters, which makes it a mixture-of-experts (MoE) model. Instead of writing text one token at a time like most LLMs, a diffusion model refines many tokens in parallel, which is why it should run fast. In one computer-use demo, Clover finished a task in 169 seconds while Qwen 3.6 Flash hit its step limit at 578 seconds. ## TL;DR - Clover is a **35B-parameter MoE diffusion language model** with 3B parameters active per token, built by Diffuse Space AI. - Diffusion text models generate by **refining many tokens at once** rather than emitting them strictly left to right, which is where the speed advantage comes from. - In a demo computer-use task, Clover **finished in 169 seconds**. - In the same demo, Qwen 3.6 Flash **ran to its 30-step limit at 578 seconds**, so Clover finished in under a third of that time. - Clover is reported to score **strongly against models of similar size**, but the source gives no specific benchmark numbers I can verify here. - This is one demo on one prompt. Treat it as a signal, not a controlled benchmark. ## How is a diffusion language model different from an autoregressive one? Most chat models you use today are autoregressive. They predict the next token, append it, then predict the next one. Each token depends on every token before it, so generation is sequential. A 1,000-token answer needs roughly 1,000 forward passes. A diffusion language model works differently. It starts from a sequence that is masked or noised and runs a number of denoising passes. Each pass can update many positions at the same time. The model revises its draft across passes until the whole output settles. That has a few practical consequences: - **Parallelism.** Many tokens get committed per pass, so the number of passes can be far smaller than the number of tokens. - **Different latency profile.** Speed depends on how many refinement passes the model needs, not just output length. - **Different tradeoffs.** Sequential generation is a natural fit for text, so diffusion models have to work to match autoregressive quality. That makes "scores strongly against similar-size models" the claim worth checking. Image and video generators have used diffusion for years. Applying it to text is the newer part, and Clover is one of several recent attempts. ## What does "35B with 3B active" mean? Clover is a mixture-of-experts model. The network holds 35 billion parameters in total, but a routing mechanism sends each token through only a subset of them. Around 3 billion are active at any moment. This matters for two reasons. Compute per token tracks the active count, so a 35B MoE can run closer to the cost of a much smaller dense model. Memory is different. You generally still need to hold all the weights somewhere, so the footprint follows the total size, not the active size. Combine that with diffusion's parallel decoding and you get two separate efficiency levers stacked together. That is the design logic behind expecting very high speed from Clover. ## How did Clover compare with Qwen 3.6 Flash in the demo? The demo used a computer-use agent task. The prompt was roughly: "I am at CMU Pittsburgh. How long does it take to drive to the nearest Wendy's?" Each model had to operate a computer to find the answer, working through a loop of observing the screen and taking actions. Here is what was shown: | Model | Result in the demo | |---|---| | Clover (Diffuse Space AI) | Completed the task after 169 seconds | | Qwen 3.6 Flash | Kept going and reached its 30-step limit at 578 seconds | Two details are worth noting. First, the "steps" here are agent actions, not diffusion denoising passes. Second, Qwen 3.6 Flash did not finish within its limit in the footage described, so 578 seconds is a floor for its completion time, not a measured finish. Clover completing in 169 seconds means it was at least about 3.4 times faster on wall-clock time for this run, and the real gap could be larger. ## Is the speed gain real or just a demo? Both things can be true. The mechanism is sound: parallel token refinement and a low active-parameter count should cut latency. But a single side-by-side run has limits. Things a single demo does not tell you: - Whether Clover completes tasks **correctly** across many runs, not just quickly once. - How much of the gap comes from raw generation speed versus the agent taking fewer or better actions. - How the comparison changes with different hardware, serving stacks, or batch sizes. - Whether Qwen 3.6 Flash was running in an optimal configuration. Agent tasks are especially sensitive to this. A faster model that picks worse actions can waste steps, and a slower model that plans well can still win on total time. The demo is encouraging because Clover was both fast and finished. It is not proof of general superiority. ## Why does speed matter for agents? Computer-use agents run a loop: look at the screen, reason, act, repeat. Every iteration pays the model's latency. If generation is the bottleneck, cutting it compounds across dozens of steps. That is why a diffusion model is an interesting fit for agentic work, where total task time is the sum of many short model calls. Lower latency also changes what you can build. Interactive tools, real-time assistants, and agents that need to retry often all benefit when each call is cheap in time. ## What should builders check before adopting Clover? The source material covers the headline specs and the demo, not deployment details. Before planning around it, verify the following from Diffuse Space AI directly: 1. **Availability and license.** Whether weights are open, and under what terms. 2. **Hardware requirements.** A 35B MoE still needs memory for all its weights. 3. **Benchmark results.** The model is described as scoring strongly against similar-size models, so look at the actual tables and which models they used. 4. **Serving support.** Diffusion models often need different inference code than autoregressive ones, so check whether your usual serving stack handles them. 5. **Task accuracy.** Run your own workload and measure success rate, not just time. ## Frequently Asked Questions ### Who made Clover? Clover comes from Diffuse Space AI. It is a diffusion text model with 35 billion total parameters and 3 billion active, using a mixture-of-experts design. ### Is Clover faster than Qwen 3.6 Flash? In the demonstrated computer-use task, yes. Clover completed the task in 169 seconds, while Qwen 3.6 Flash reached its 30-step limit at 578 seconds. That is a single run, so it shows direction, not a general speed ratio. ### Do diffusion language models replace autoregressive models? Not yet, based on what is shown here. Diffusion models offer a different speed profile through parallel decoding, and Clover is reported to be competitive with similar-size models. Autoregressive models remain the default, and broader testing is needed before anyone calls it a replacement. ### Does the 3B active parameter count mean Clover runs on small hardware? Not necessarily. Active parameters determine compute per token, but a MoE model generally needs memory for all 35 billion parameters. Check the published hardware guidance before assuming it fits your setup.