updated benchmarks
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+15
-15
@@ -57,9 +57,9 @@ function cacheUI() {
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hipblasModalOpen: document.getElementById("hipblas-modal-open"),
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hipblasModal: document.getElementById("hipblas-modal"),
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hipblasModalClose: document.getElementById("hipblas-modal-close"),
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rpcModalOpen: document.getElementById("rpc-modal-open"),
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rpcModal: document.getElementById("rpc-modal"),
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rpcModalClose: document.getElementById("rpc-modal-close"),
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dualModalOpen: document.getElementById("dual-modal-open"),
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dualModal: document.getElementById("dual-modal"),
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dualModalClose: document.getElementById("dual-modal-close"),
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rocwmmaModalOpen: document.getElementById("rocwmma-modal-open"),
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rocwmmaModal: document.getElementById("rocwmma-modal"),
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rocwmmaModalClose: document.getElementById("rocwmma-modal-close"),
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@@ -77,9 +77,9 @@ function setupModals() {
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close: state.ui.hipblasModalClose,
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},
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{
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open: state.ui.rpcModalOpen,
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modal: state.ui.rpcModal,
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close: state.ui.rpcModalClose,
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open: state.ui.dualModalOpen,
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modal: state.ui.dualModal,
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close: state.ui.dualModalClose,
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},
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{
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open: state.ui.rocwmmaModalOpen,
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@@ -186,7 +186,7 @@ function ensureModel(testEntry, modelName, run) {
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quant: (run.quant || "Unknown").toUpperCase(),
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sizeB: run.name_params_b ?? run.params_b ?? null,
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backends: {},
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isRpc: Boolean(run.rpc),
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isDual: run.gpu_config === "dual",
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search_blob: [modelName, run.quant, run.env, run.test]
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.filter(Boolean)
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.map((s) => s.toString().toLowerCase())
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@@ -202,10 +202,10 @@ function ensureModel(testEntry, modelName, run) {
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state.sizeStats.min = Math.min(state.sizeStats.min, row.sizeB);
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state.sizeStats.max = Math.max(state.sizeStats.max, row.sizeB);
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}
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if (run.rpc) {
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row.isRpc = true;
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if (!row.search_blob.includes("rpc")) {
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row.search_blob = `${row.search_blob} rpc`;
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if (run.gpu_config === "dual") {
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row.isDual = true;
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if (!row.search_blob.includes("dual")) {
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row.search_blob = `${row.search_blob} dual`;
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}
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}
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return row;
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@@ -462,11 +462,11 @@ function buildSingleTable(models, backendList) {
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nameSpan.className = "model-name";
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nameSpan.textContent = model.model;
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head.appendChild(nameSpan);
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if (model.isRpc) {
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if (model.isDual) {
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const pill = document.createElement("span");
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pill.className = "model-pill model-pill-rpc";
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pill.title = "Run executed via llama.cpp RPC across two servers";
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pill.textContent = "RPC · dual server";
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pill.className = "model-pill model-pill-dual";
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pill.title = "Run executed on Dual GPU (2x R9700)";
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pill.textContent = "Dual GPU";
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head.appendChild(pill);
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}
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tdModel.appendChild(head);
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+9
-13
@@ -21,8 +21,8 @@
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<button id="hipblas-modal-open" type="button" class="chip small legend-pill legend-pill-default">
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hipBLASLt vs hblt0
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</button>
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<button id="rpc-modal-open" type="button" class="chip small legend-pill legend-pill-rpc">
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RPC · dual server
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<button id="dual-modal-open" type="button" class="chip small legend-pill legend-pill-dual">
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Dual GPU
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</button>
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<button id="rocwmma-modal-open" type="button" class="chip small legend-pill legend-pill-rocwmma">
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rocWMMA
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@@ -103,18 +103,14 @@
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</div>
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</div>
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<div id="rpc-modal" class="modal hidden" role="dialog" aria-modal="true" aria-labelledby="rpc-title">
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<div id="dual-modal" class="modal hidden" role="dialog" aria-modal="true" aria-labelledby="dual-title">
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<div class="modal-content">
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<button id="rpc-modal-close" class="modal-close" aria-label="Close dialog">×</button>
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<h2 id="rpc-title">RPC · dual server</h2>
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<p>These results were produced with two R9700 systems (each 32 GB)
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connected over 5 Gbps Ethernet. One runs <code>rpc-server</code> from llama.cpp; the other runs
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<code>llama-bench --rpc</code>.
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</p>
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<p>This setup allows distributed inference, splitting large GGUF models across both machines. The metric
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shows what
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you can expect when latency is limited by the network and the workload is balanced between two RPC
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participants.</p>
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<button id="dual-modal-close" class="modal-close" aria-label="Close dialog">×</button>
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<h2 id="dual-title">Dual GPU (2x R9700)</h2>
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<p>These results were produced using two AMD Radeon AI PRO R9700 GPUs (32GB each, 64GB total).</p>
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<p>Models larger than ~30GB are automatically distributed across both GPUs using
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<code>HIP_VISIBLE_DEVICES=0,1</code>. Smaller models run on a single GPU
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(<code>HIP_VISIBLE_DEVICES=0</code>).</p>
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</div>
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</div>
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