diff --git a/Package.swift b/Package.swift
index 1c34907..e677e77 100644
--- a/Package.swift
+++ b/Package.swift
@@ -7,6 +7,7 @@ let package = Package(
     products: [
         .library(name: "MarkBase", targets: ["MarkBase"]),
         .executable(name: "MarkBaseServer", targets: ["MarkBaseServer"]),
+        .executable(name: "EmbeddingServer", targets: ["EmbeddingServer"]),
         .executable(name: "CLITest", targets: ["CLITest"]),
     ],
     dependencies: [
@@ -34,6 +35,17 @@ let package = Package(
                 .linkedFramework("Foundation"),
             ]
         ),
+        .executableTarget(
+            name: "EmbeddingServer",
+            dependencies: [
+                "MarkBase",
+                .product(name: "Hummingbird", package: "hummingbird"),
+            ],
+            linkerSettings: [
+                .linkedFramework("Metal"),
+                .linkedFramework("Foundation"),
+            ]
+        ),
         .executableTarget(
             name: "CLITest",
             dependencies: ["MarkBase"],
diff --git a/Sources/EmbeddingServer/EmbeddingServerMain.swift b/Sources/EmbeddingServer/EmbeddingServerMain.swift
new file mode 100644
index 0000000..1339457
--- /dev/null
+++ b/Sources/EmbeddingServer/EmbeddingServerMain.swift
@@ -0,0 +1,77 @@
+import Foundation
+import MarkBase
+import Hummingbird
+
+@main
+struct EmbeddingServerMain {
+    static func main() async throws {
+        let args = CommandLine.arguments
+        let modelName = args.count > 1 ? args[1] : "embeddinggemma-300m"
+        let port = args.count > 2 ? Int(args[2]) ?? 8084 : 8084
+        
+        let modelPath = NSString(string: "~/MarkBaseEngine/models/\(modelName)").expandingTildeInPath
+
+        print("MarkBaseEngine Embedding Server")
+        print("  Model: \(modelName)")
+        print("  Port: \(port)")
+        print("  Path: \(modelPath)")
+
+        let engine = try MarkBaseEngine(autoCompile: true)
+        let embedModel = try EmbeddingGemmaModel(modelDir: modelPath, engine: engine)
+        let layers = embedModel.config.numHiddenLayers
+        let hiddenSize = embedModel.config.hiddenSize
+
+        print("EmbeddingGemma loaded (\(layers) layers, hidden=\(hiddenSize))")
+
+        let router = Router()
+
+        router.get("/") { _, _ in
+            return "{\"server\":\"MarkBaseEngine Embedding\",\"model\":\"embeddinggemma-300m\",\"layers\":\(layers),\"hidden_size\":\(hiddenSize)}"
+        }
+
+        router.get("/health") { _, _ in
+            return "{\"status\":\"healthy\",\"model\":\"embeddinggemma-300m\",\"layers\":\(layers)}"
+        }
+
+        router.post("/v1/embeddings") { request, _ in
+            let buffer = try await request.body.collect(upTo: .max)
+            let data = Data(buffer: buffer)
+
+            guard let json = try JSONSerialization.jsonObject(with: data) as? [String: Any],
+                  let input = json["input"] else {
+                return "{\"error\":\"missing 'input' field\"}"
+            }
+
+            let inputs: [String]
+            if let str = input as? String { inputs = [str] }
+            else if let arr = input as? [String] { inputs = arr }
+            else { return "{\"error\":\"'input' must be string or array\"}" }
+
+            var embeddings: [[String: Any]] = []
+            for (i, text) in inputs.enumerated() {
+                let t0 = Date()
+                let embedding = try embedModel.embed(text: text)
+                let ms = Int(Date().timeIntervalSince(t0) * 1000)
+                embeddings.append(["object": "embedding", "index": i, "embedding": embedding, "usage_ms": ms])
+            }
+
+            let id = UUID().uuidString
+            let ts = Int(Date().timeIntervalSince1970)
+            let response: [String: Any] = [
+                "id": id, "object": "list", "created": ts, "model": "embeddinggemma-300m",
+                "data": embeddings
+            ]
+
+            let jsonData = try JSONSerialization.data(withJSONObject: response)
+            return String(data: jsonData, encoding: .utf8) ?? "{}"
+        }
+
+        let app = Application(
+            router: router,
+            configuration: .init(address: .hostname("0.0.0.0", port: port))
+        )
+
+        print("Server starting on port \(port)...")
+        try await app.run()
+    }
+}
diff --git a/Sources/MarkBase/Embedding/EmbeddingGemmaModel.swift b/Sources/MarkBase/Embedding/EmbeddingGemmaModel.swift
index 895aacc..9e55d4f 100644
--- a/Sources/MarkBase/Embedding/EmbeddingGemmaModel.swift
+++ b/Sources/MarkBase/Embedding/EmbeddingGemmaModel.swift
@@ -284,15 +284,21 @@ public final class EmbeddingGemmaModel: @unchecked Sendable {
         let enc = cmdBuf.makeComputeCommandEncoder()!
         let pso = try engine.pipeline(named: "matmul_f32")
         enc.setComputePipelineState(pso)
-        enc.setBuffer(input, offset: 0, index: 0)
-        enc.setBuffer(weight, offset: 0, index: 1)
-        enc.setBuffer(output, offset: 0, index: 2)
-        var mm = UInt32(m), kk = UInt32(k), nn = UInt32(n)
-        enc.setBytes(&mm, length: 4, index: 3)
-        enc.setBytes(&kk, length: 4, index: 4)
-        enc.setBytes(&nn, length: 4, index: 5)
-        enc.dispatchThreads(MTLSize(width: m * n, height: 1, depth: 1),
-                           threadsPerThreadgroup: MTLSize(width: min(256, m * n), height: 1, depth: 1))
+        // Note: matmul_f32 only handles M=1, so we loop over positions
+        // For production: implement a proper batch matmul kernel
+        for i in 0..<m {
+            let inputOffset = i * k * 4
+            let outputOffset = i * n * 4
+            enc.setBuffer(input, offset: inputOffset, index: 0)
+            enc.setBuffer(weight, offset: 0, index: 1)
+            enc.setBuffer(output, offset: outputOffset, index: 2)
+            var mm: UInt32 = 1, kk = UInt32(k), nn = UInt32(n)
+            enc.setBytes(&mm, length: 4, index: 3)
+            enc.setBytes(&kk, length: 4, index: 4)
+            enc.setBytes(&nn, length: 4, index: 5)
+            enc.dispatchThreads(MTLSize(width: n, height: 1, depth: 1),
+                               threadsPerThreadgroup: MTLSize(width: min(256, n), height: 1, depth: 1))
+        }
         enc.endEncoding()
     }