Lizard-LLM Community

Lizard wants you [image: lizard-wants-you.jpg] We are looking for 100 people to test the new Lizard Native AI inference provider on hardware we do not own. Free access, no cost and no catch. What you get Free access. Both native engines, unlocked, for taking part. There is no card to enter and nothing is charged. Benchmarks on your own hardware. The measurements come from your machine, not ours — we cannot buy every configuration our users own. A say in what gets built. What testers report in public is what gets fixed first. What we ask in return One thing, and this is the important part: Install Lizard and run the built-in benchmark on your own machine. It is one click. Post the result here, in this community, with your hardware next to it — processor, memory, graphics card, and which engine ran. A tokens-per-second figure with no machine attached tells nobody anything. Tell us what should improve. Slow, confusing, broken or missing — say so here, in public. A complaint with a benchmark attached is the most useful thing you can send us. Benchmarks and improvement reports both belong in the forum, not in a private inbox. That is the whole point: the numbers are only worth something if other people can read them and compare their own. Who we are looking for 64-bit Windows 10 or 11 — the odder the hardware the better Integrated graphics, older GPUs and laptops especially: that is where local AI is hardest and least measured Willing to post publicly under your own name or handle No AI or programming background required Apply here — three fields, no account needed: https://lizard-llm.qendryx.com/testers.html

Lizard Chat is the chat workspace built into the app, running on whatever model you activated. Since 1.2.52 it draws charts — and there is no chart button, no menu and no special syntax to learn. [image: lizard-chat.png] How it decides Ask a question that wants a picture and you get one: Show me a bar chart of my disk use in GB: models 22.2, benchmark history 0.17, logs 0.4 Plot the price over the last four months: 3.10, 3.40, 3.25, 3.90 Draw a diagram of how a release happens: build, sign, upload, announce Give me a pie chart of my week: coding 30 hours, meetings 8, reading 4 The wording only decides which example the model is shown. The model emits the chart type itself and can overrule that suggestion. Asked to "make a diagram of revenue by quarter", llama-3.2-3B returned a line chart — four quarters of revenue are a trend, not a flowchart. It can also decide a chart would not help and just answer. Six shapes bar · line · area · pie · scatter · flow (boxes and arrows, for processes and architectures) What it will not do It will not draw something misleading. A single data point, a value it cannot read, a diagram with nothing connected — you get the written answer and no chart, rather than a picture implying data that was never there. Where it runs Everything: your message, your documents, the answer, the chart. The charting library ships inside the installer, so charts render with the network switched off. Nothing in the chat crosses the network. The rest of the workspace Saved and searchable conversations grouped by day, pin/rename/export as Markdown. Questions about your own text, Markdown, CSV, log and source files, with citations. Separate workspaces (General, Code, Research) each with their own instructions and documents. MCP plugins for folders, web pages, SQLite files and Git history. Full details: https://lizard-llm.qendryx.com/chat.html Question for the room: what did you ask it to chart, and did it pick the shape you expected?

Inference Engineering · Day 30 · Morning [image: Screenshot%202026-07-22%20221126.png] Performance leadership is evidence with boundaries, not a permanent badge. A benchmark that omits hardware, model weights, quantization, context, concurrency, transport, and correctness makes it too easy to generalize a result past the machine that produced it. Lizard’s benchmark tooling keeps those fields together so a fast run stays scoped to the exact setup that produced it. That matters in practice. The same model can look different when the context window changes, when concurrent requests arrive, or when the transport path changes from one local route to another. If correctness is not recorded alongside speed, you can end up optimizing a failure mode and calling it progress. The useful habit is simple: publish the evidence you would want back when the result is challenged. Record the machine, the quant, the load shape, and the correctness gate before you compare one run to another. That does not slow the work down; it keeps the work honest. How do you usually fence a benchmark so a local win does not get repeated as a universal claim? Engineering fact: Lizard's benchmark tooling records hardware, model, quantization, context, concurrency, transport, and correctness so a local win remains scoped instead of becoming a universal claim. Lizard The AI Runtime You'll Own—Not Rent. Receive two professional Windows AI runtimes with lifetime updates. Run AI at native speed, keep every conversation private, and stay independent with intelligent hardware optimization and no cloud dependency. Read the relevant Lizard page #localai #benchmarking #inference #llmops #performanceengineering <!-- lizard-marketing-slot:day-30-am -->

Inference Engineering · Day 27 · Evening [image: lizard-native-stack.png] Conversation length changes the workload before decode begins. As context grows, prompt processing and KV-cache pressure can change even when the model and hardware stay the same. Lizard records context tokens and includes a context bucket in comparable turn history. That keeps a long-conversation slowdown from looking like an unexplained engine regression. At what context length does your current local model begin to feel different? Engineering fact: Lizard records context tokens and groups native turn history into context buckets because longer conversations change prefill and KV-cache costs. Read the relevant Lizard page #LizardNative #LizardLLM #LocalAI #GGUF #InferenceEngineering <!-- lizard-marketing-slot:day-27-pm -->

Inference Engineering · Day 30 · Evening [image: Screenshot%202026-07-22%20221220.png] If you want a readiness check that means something, run the HTTP benchmark against a real model file instead of a toy case. The useful part here is not the peak number on a single pass. It is the comparison across the same GGUF with two batch sizes, B=16 and B=8, and the ability to line that up against llama.cpp on the same host. That matters because batch size changes the shape of the request. A model can look healthy at one setting and expose a different bottleneck at the other. Memory pressure, launch overhead, and decode behavior do not always move together. When you keep the artifact constant and only change the runtime lane, you get a cleaner view of what the engine is actually doing. The practical habit is simple: capture the command, keep the GGUF path fixed, save the benchmark artifact, and record the hardware context along with the result. That gives you something a second person can rerun later without guessing which variable changed. I have found that the most useful benchmark writeup answers three things: did the model load, did the request complete, and where did the two runtimes diverge under the same input. If you only keep one number, you lose the signal. What model and Windows machine would you use as the first real HTTP readiness check? hashtags":["LizardLLM","HTTPBenchmark","llamaCpp","GGUF","InferenceEngineering"]}, Engineering fact: The Lizard Console command lizard-llm benchmark http can test lizard-native or Caterpillar at B=16 and B=8 and compare the same GGUF with llama.cpp. Lizard The AI Runtime You'll Own—Not Rent. Receive two professional Windows AI runtimes with lifetime updates. Run AI at native speed, keep every conversation private, and stay independent with intelligent hardware optimization and no cloud dependency. Read the relevant Lizard page #LizardLLM #HTTPBenchmark #llamaCpp #GGUF #InferenceEngineering <!-- lizard-marketing-slot:day-30-pm -->

Inference Engineering · Day 25 · Evening [image: two-engines-decision.png] A runtime choice should survive beyond the activation form. Lizard records the selected native provider with the active model. Onboarding warmup and Chat resolve that state rather than reverting to an unrelated default after the page changes. Persistent provider identity makes performance evidence and troubleshooting comparable across surfaces. Where in your workflow could a provider choice be silently lost? Engineering fact: When a local model is activated, Lizard stores whether lizard-native or Caterpillar was selected and uses that provider for onboarding warmup and Chat. Read the relevant Lizard page #Caterpillar #LizardLLM #GPUProgramming #LocalAI #GGUF <!-- lizard-marketing-slot:day-25-pm -->

Reasonable thing to want a straight answer on. Stays on your machine, always: what you type, what the model answers, the model files, your conversations, and your documents. Does leave, and only this: signing in exchanges your account identity and which plan you're on. Update checks ask the website what the newest version is. Downloading a model fetches it from Hugging Face, as you'd expect. Not sent anywhere: your prompts, the model's output, your files, or details about your hardware. Once a model is downloaded, you can disconnect entirely and it keeps working — a fair way to verify the claim yourself. Written up here: https://lizard-llm.qendryx.com/faq.html

If something is missing or awkward, this is the place. Most useful framing is what you were trying to do and where it got stuck — that tends to lead somewhere better than a specific solution would, because the underlying problem is often fixable in a way nobody had thought of. Bugs are welcome here too. Include: What you did What you expected What happened instead, with the exact message Recent changes that came from exactly this kind of report: models that were too big for a card used to silently thrash instead of saying so, and a model that failed to shut down cleanly could block every later run with no way out but restarting. Both are fixed.