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| <title>The Memory Wall — Exeaon</title> | |
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| </head> | |
| <body> | |
| <div class="wrap"> | |
| <h1>The Memory Wall</h1> | |
| <p class="sub">Why your GPU's TFLOPs do not decide your tokens per second.</p> | |
| <div class="eq">tokens/second ≤ memory bandwidth ÷ model bytes</div> | |
| <p>At batch one, a language model reads <strong>every weight from memory to produce | |
| one token</strong>, and uses each weight exactly once. Arithmetic intensity is about | |
| <strong>0.5 FLOP per byte</strong>. The accelerator's FLOP rating never enters the | |
| equation — the only lever on single-stream speed is how many bytes the model is.</p> | |
| <div class="controls"> | |
| <div> | |
| <label for="p">Parameters <span class="val" id="pv">7B</span></label> | |
| <input type="range" id="p" min="0.5" max="120" step="0.1" value="7"> | |
| </div> | |
| <div> | |
| <label for="hw">Hardware</label> | |
| <select id="hw"></select> | |
| </div> | |
| <div> | |
| <label for="m">Available memory <span class="val" id="mv">16 GB</span></label> | |
| <input type="range" id="m" min="4" max="192" step="1" value="16"> | |
| </div> | |
| </div> | |
| <div class="verdict"><h2 id="vh"></h2><p id="vp"></p></div> | |
| <div class="tblwrap"><table> | |
| <thead><tr><th>Format</th><th>Bits/weight</th><th>Size</th><th>Fits</th><th>Decode ceiling</th></tr></thead> | |
| <tbody id="rows"></tbody> | |
| </table></div> | |
| <p class="note"> | |
| <strong>This is a ceiling, not a promise.</strong> Attention, the KV cache, kernel | |
| overhead and real bandwidth utilisation (typically 70–85% of the spec sheet) all | |
| put the achievable number below it. Larger batches raise arithmetic intensity and | |
| change the picture entirely — this models the single-stream case, which is what | |
| most people run locally. Bandwidth figures are vendor peak. | |
| </p> | |
| <h2 style="margin-top:34px">Models built against this limit</h2> | |
| <p class="note" style="border:none;padding-top:0"> | |
| Bytes are the only lever at batch one, so these are what the lever is worth in | |
| practice. Accuracy retained is the mean of ARC-Easy, ARC-Challenge, HellaSwag and | |
| PIQA against the uncompressed model, scored on the same GPU and harness. The | |
| speech model has no such benchmark, so it is measured by word error rate instead. | |
| </p> | |
| <div class="tblwrap"><table> | |
| <thead><tr><th>Model</th><th>Base</th><th>Size</th><th>Smaller by</th><th>Accuracy retained</th></tr></thead> | |
| <tbody> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Nunya-14B">Exeaon1-Nunya-14B</a></td><td>Qwen3-14B</td><td>7.38 GB</td><td>3.73x</td><td>100.3%</td></tr> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Kese-30B-A3B">Exeaon1-Kese-30B-A3B</a></td><td>Qwen3-30B-A3B</td><td>16.27 GB</td><td>3.50x</td><td>98.1%</td></tr> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Nunya-8B">Exeaon1-Nunya-8B</a></td><td>Qwen3-8B</td><td>4.11 GB</td><td>3.80x</td><td>99.6%</td></tr> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Dzo-4B">Exeaon1-Dzo-4B</a></td><td>Qwen3-4B</td><td>2.04 GB</td><td>3.67x</td><td>98.1%</td></tr> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Dzo-0.6B">Exeaon1-Dzo-0.6B</a></td><td>Qwen3-0.6B</td><td>0.31 GB</td><td>3.50x</td><td>96.3%</td></tr> | |
| <tr><td><a href="https://huggingface.co/Exeaon/Exeaon1-Voice-0.8B">Exeaon1-Voice-0.8B</a></td><td>Whisper v3-turbo</td><td>0.43 GB</td><td>3.47x</td><td>WER unchanged</td></tr> | |
| </tbody> | |
| </table></div> | |
| <p class="note"> | |
| <strong>Retention is not a free lunch, and neither is speed.</strong> On a large | |
| GPU these run slower than dense fp16 — a vendor tensor-core GEMM is heavily | |
| tuned and there is bandwidth to spare, so trading compute for memory loses. The | |
| win is on the other side of this page: fitting where the dense model does not. | |
| </p> | |
| <p class="note" style="border:none;padding-top:0"> | |
| Compressed models: <a href="https://huggingface.co/Exeaon">huggingface.co/Exeaon</a> · | |
| Runtime: <a href="https://github.com/ExeaonLM">github.com/ExeaonLM</a> · | |
| Zenux Plimver Technologies LTD, Ghana | |
| </p> | |
| </div> | |
| <script> | |
| const HW = { | |
| "Laptop CPU (DDR5-5600, dual channel)":90, | |
| "Desktop CPU (DDR5-6000, dual channel)":96, | |
| "Server CPU (DDR5, 12 channel)":460, | |
| "Apple M4 Pro":273, "Apple M4 Max":546, | |
| "NVIDIA T4":320, "NVIDIA RTX 4090":1008, "NVIDIA L40S":864, | |
| "NVIDIA A100 80GB":2039, "NVIDIA H100 SXM":3350, "NVIDIA B200":8000, | |
| }; | |
| const FMT = [ | |
| ["FP16 / BF16",16,false],["FP8",8,false],["INT8",8,false],["INT4",4,false], | |
| ["ℰ-PURE 4-bit",4,true],["ℰ-PURE 3-bit",3,true], | |
| ]; | |
| const $=id=>document.getElementById(id); | |
| const sel=$("hw"); | |
| for(const k in HW){const o=document.createElement("option");o.textContent=k;sel.appendChild(o);} | |
| sel.value="NVIDIA T4"; | |
| // Weight bytes only. KV cache and activations are extra, which is part of why | |
| // the printed number is a ceiling rather than an estimate. | |
| const gb=(p,bits)=>p*1e9*bits/8/2**30; | |
| function render(){ | |
| const p=+$("p").value, mem=+$("m").value, bw=HW[sel.value]; | |
| $("pv").textContent=p.toFixed(1).replace(/\.0$/,"")+"B"; | |
| $("mv").textContent=mem+" GB"; | |
| const rows=FMT.map(([name,bits,ours])=>{ | |
| const size=gb(p,bits); | |
| return {name,bits,ours,size,fits:size<=mem*0.9,tps:bw/size}; | |
| }); | |
| $("rows").innerHTML=rows.map(r=>`<tr class="${r.ours?"ours":""}"> | |
| <td>${r.name}</td><td class="num">${r.bits}</td> | |
| <td class="num">${r.size.toFixed(2)} GB</td> | |
| <td class="${r.fits?"yes":"no"}">${r.fits?"yes":"no"}</td> | |
| <td class="num">${r.tps.toFixed(1)} tok/s</td></tr>`).join(""); | |
| const base=rows[0], fitting=rows.filter(r=>r.fits), best=rows[rows.length-1]; | |
| let h,t; | |
| if(!fitting.length){ | |
| h=`Nothing fits in ${mem} GB`; | |
| t=`A ${p.toFixed(1).replace(/\.0$/,"")}B model exceeds this device's memory at every | |
| format listed. Smaller model, or more memory.`; | |
| }else if(!base.fits){ | |
| const f=fitting[fitting.length-1]; | |
| h=`${sel.value} cannot run this model in FP16 at all`; | |
| t=`FP16 needs ${base.size.toFixed(1)} GB against ${mem} GB available. At | |
| ${f.name} it is ${f.size.toFixed(1)} GB and fits — a step function, not a | |
| speed-up. The model goes from impossible to running at roughly | |
| ${f.tps.toFixed(0)} tok/s.`; | |
| }else{ | |
| h=`${best.tps.toFixed(0)} vs ${base.tps.toFixed(0)} tok/s`; | |
| t=`Same model, same hardware, same ${bw} GB/s of bandwidth. | |
| ${(best.tps/base.tps).toFixed(1)}× more tokens purely from moving fewer bytes.`; | |
| } | |
| $("vh").textContent=h; $("vp").textContent=t; | |
| } | |
| ["p","m","hw"].forEach(id=>$(id).addEventListener("input",render)); | |
| render(); | |
| </script> | |
| </body> | |
| </html> | |