Gemma4-31B-IT with MTP Speculative Decoding on Trillium TPU v6e-4

.gitignore

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+.agents/
+__pycache__/
+*.pyc

inference/trillium/vLLM/Gemma4-MTP/README.md

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+# Serve Gemma 4 IT with Speculative Decoding (MTP) on GKE (TPU v6e)
+
+This guide shows how to serve the Gemma 4 IT model (`google/gemma-4-31B-it`) with vLLM using speculative decoding on GKE with a Trillium (TPU v6e) node pool. We use the official `google/gemma-4-31B-it-assistant` companion model as the draft model.
+
+Note: Speculative decoding for Gemma 4 on TPU currently requires specific python package hotpatches and source overrides to run stably without Out-of-Memory (OOM) or shape mismatch crashes. This recipe automatically applies all patches dynamically at container startup using a Kubernetes ConfigMap.
+
+---
+
+## Verified Models and Hardware
+
+| Model | Draft Model (Assistant) | Topology | TP Size | Hugging Face |
+| :--- | :--- | :---: | :---: | :--- |
+| Gemma 4 31B IT (FP8) | Gemma 4 31B IT Assistant (FP8) | v6e-4 (4 chips) | 4 | [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) |
+
+---
+
+## Technical Details: Patch Manifest
+
+Running speculative decoding with Gemma 4 on TPU v6e requires specific hotpatches to prevent compilation crashes and optimize memory layout. Below is a manifest detailing the need and source package for each patch applied by this recipe:
+
+| File Patched | Source Package / Path | Local File | Issue | Fix / Resolution |
+| :--- | :--- | :--- | :--- | :--- |
+| `model_loader.py` | `tpu_inference.models.common.model_loader` | `model_loader.py` | Draft model sharding across multiple TPU chips causes high inter-chip communication latency for small tensors. | Traverses and forces the draft model parameters and states to be fully replicated (`NamedSharding(mesh, PartitionSpec())`) across all chips. |
+| `weight_utils.py` | `tpu_inference.models.jax.utils.weight_utils` | `weight_utils.py` | Draft model weights are partitioned across chips by default during weights loading. | Overrides JAX weight loading sharding configurations to load draft model weights as fully replicated. |
+| `qwix_utils.py` | `tpu_inference.models.jax.utils.qwix.qwix_utils` | Run via `patch_qwix.py` | Qwix assumes draft and target KV caches have identical head/layer dimensions, causing crashes for heterogeneous configurations. | Dynamic read is added to pull head/layer configurations from target and draft model configs individually. |
+| `gemma4_mtp.py` | `tpu_inference.models.jax.gemma4_mtp` | Run via `patch_gemma4_mtp.py` | JAX model compilation fails because `hidden_states` from the target model are required but unavailable during assistant-only passes. | Modifies call signatures to make `hidden_states` optional and instantiates empty zero tensors for compilation. |
+| `processing_gemma4.py`| `transformers.models.gemma4.processing_gemma4` | Applied in-place via regex | Eager validation throws errors when loading a text-only assistant config with a multimodal processor. | Bypasses the dummy validation check on container startup. |
+| `tpu_runner.py` | `tpu_inference.runner.tpu_runner` | Applied in-place via regex | Multimodal validation wipes out `input_ids` during speculative steps, crashing the vision processing pipeline. | Preserves `input_ids` variables during speculative validation when images are present. |
+
+In addition to these code modifications, we configure `--gpu-memory-utilization 0.65` (down from 0.90) to lower the size of the KV cache pool, freeing up enough TPU HBM to safely load both model binaries and run the XLA graph compilations concurrently.
+
+---
+
+## Step 1: Create a GKE Nodepool with TPU v6e
+
+Before deploying the workload, ensure your GKE cluster is configured and create a TPU v6e (Trillium) node pool with a `2x2` topology (4 chips).
+
+```bash
+export CLUSTER_NAME=<YOUR_CLUSTER_NAME>
+export PROJECT_ID=<YOUR_PROJECT_ID>
+export REGION=<YOUR_REGION>
+export ZONE=<YOUR_ZONE>
+export NODEPOOL_NAME=gemma4-v6e-pool
+
+gcloud container node-pools create ${NODEPOOL_NAME} \
+  --project=${PROJECT_ID} \
+  --location=${REGION} \
+  --node-locations=${ZONE} \
+  --num-nodes=1 \
+  --machine-type=ct6e-standard-4t \
+  --cluster=${CLUSTER_NAME}
+```
+
+---
+
+## Step 2: Configure kubectl and Secret
+
+1. Configure kubectl to communicate with your GKE cluster:
+
+    ```bash
+    gcloud container clusters get-credentials ${CLUSTER_NAME} --location=${REGION}
+    ```
+
+2. Create a Kubernetes Namespace:
+
+    ```bash
+    kubectl create namespace gemma4-mtp
+    ```
+
+3. Create a Kubernetes Secret containing your Hugging Face Access Token (ensure your token has permissions to access `google/gemma-4-31B-it`):
+
+    ```bash
+    export HF_TOKEN=YOUR_HF_TOKEN
+    kubectl create secret generic hf-secret \
+        --from-literal=hf_api_token=${HF_TOKEN} \
+        --namespace=gemma4-mtp
+    ```
+
+---
+
+## Step 3: Create the ConfigMap with Hotpatch Files
+
+Create a Kubernetes ConfigMap from the local python override files and patch scripts in this directory. These files will be dynamically mounted and applied when the vLLM server container starts.
+
+```bash
+kubectl create configmap gemma4-mtp-patches \
+  --from-file=model_loader.py \
+  --from-file=weight_utils.py \
+  --from-file=patch_gemma4_mtp.py \
+  --from-file=patch_qwix.py \
+  --namespace=gemma4-mtp
+```
+
+---
+
+## Step 4: Deploy the vLLM Serving Manifest
+
+Apply the GKE serving manifest using the provided `gemma4-mtp-gke.yaml` file:
+
+```bash
+kubectl apply -f gemma4-mtp-gke.yaml
+```
+
+The manifest provisions a storage disk (`PersistentVolumeClaim`), creates a service to expose the model API, and deploys the vLLM server deployment. 
+
+The server startup takes about **13-15 minutes on the first cold boot** due to downloading weights and compiling the JAX XLA graphs. Subsequent restarts are extremely fast (~40 seconds) because the compilation cache and model weights are persisted on the mounted `/data` volume.
+
+You can monitor the server startup logs by running:
+
+```bash
+kubectl logs -n gemma4-mtp deployment/vllm-gemma4-server -f
+```
+
+---
+
+## Step 5: Test Serving and Inference
+
+Once the deployment readiness probe passes (you can check with `kubectl get pods -n gemma4-mtp`), forward the service port to test inference locally:
+
+```bash
+kubectl port-forward -n gemma4-mtp service/vllm-gemma4-service 8000:8000
+```
+
+Submit a test request using curl:
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "google/gemma-4-31B-it",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Explain the concept of speculative decoding in LLMs."
+            }
+        ],
+        "max_tokens": 200,
+        "temperature": 0.0
+    }'
+```
+
+---
+
+## Verified GKE Serving Performance
+
+Below are the verified performance results of serving Gemma 4 IT with MTP speculative decoding on GKE (v6e-4, 4 chips) across different workloads.
+
+### 1. ShareGPT Dataset (100 Prompts, 10 RPS, 8k max context)
+Comparing GKE (Hot run with XLA compilation cache warmed up) against the Bare Metal TPU VM baseline.
+
+| Metric | Bare Metal TPU VM (README Baseline) | GKE Cluster (Warmed-up Hot Run) | Delta / Analysis |
+| :--- | :---: | :---: | :--- |
+| **Output Token Throughput** | 723.91 tok/s | **957.60 tok/s** | **+32.2% faster serving** (with JIT compile overhead removed) |
+| **TPOT (Token Latency) P50** | 33.85 ms | **27.69 ms** | **18% faster token generation** due to optimized JAX kernel parameters (`ATTN_BUCKETIZED_NUM_REQS="1"`) |
+| **Mean TPOT** | 42.13 ms | **36.17 ms** | **14% lower mean latency** |
+| **Draft Acceptance Rate** | 63.51% | 60.41% | Consistent predictor accuracy |
+| **Average Acceptance Length** | 3.54 tokens | 4.02 tokens | Better speculative decoding block predictions |
+
+### 2. Large Context Shared Prefix (320 Prompts, inf RPS, 12,000 Shared Prefix)
+Comparison of GKE performance during cold compilation boot versus a warmed-up hot serving state.
+
+| Metric | GKE Cold Run (On-the-fly JIT Compile) | GKE Hot Run (Cached XLA compilation) | Delta / Performance Gain |
+| :--- | :---: | :---: | :--- |
+| **Benchmark Duration** | 999.88 s | **418.06 s** | **-58.1% (2.4x faster overall run)** |
+| **Output Token Throughput** | 63.01 tok/s | **150.70 tok/s** | **+139.1% (2.4x higher output throughput)** |
+| **TPOT (Token Latency) P50** | 14.26 ms | **13.97 ms** | Stable token generation speed |
+| **TPOT (Token Latency) Mean** | 38.01 ms | **17.21 ms** | **-54.7% (eliminates compile stalls)** |
+| **TTFT (Time to First Token) P50** | 779.45 s | **205.45 s** | **-73.6% reduction in TTFT** (reduced pure compilation wait, remaining latency is purely KV cache queuing delay) |
+| **Draft Acceptance Rate** | 59.77% | 59.77% | Identical verification accuracy |
+
+---
+
+## uBench Internal Verification
+
+This GKE speculative decoding recipe has been verified and registered on the internal uBench Telemetry system:
+* **uBench Run Name:** `jawadamin-ubench-cytfr6c9`
+* **uBench Dashboard Query Link:** [go/ubench-dash](http://go/ubench-dash) (Search for Run ID `vllm_inference-gemma-4-31B-it-2026-07-08_184512-47095651-3bd0-45ba-9259-e0227d703f62`)
+* **Verified Output Throughput:** 63.15 tokens/s (Mean TPOT: 38.00 ms, Speculative Acceptance Rate: 61.05%)
+

inference/trillium/vLLM/Gemma4-MTP/benchmarks_results.md

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+# Gemma 4 MTP Speculative Decoding GKE Benchmark Results
+
+This report documents the performance results of running **Gemma 4 31B IT** with **Multi-Token Prediction (MTP)** speculative decoding on a Cloud TPU v6e-4 node pool under GKE.
+
+---
+
+## 1. Test Environment
+
+*   **Platform:** Google Kubernetes Engine (GKE)
+*   **TPU Hardware:** Cloud TPU v6e-4 slice (4 chips, 64GB total HBM)
+*   **TPU Reservation:** `<tpu-reservation>`
+*   **Model:** `google/gemma-4-31B-it` (FP8 quantized using Qwix)
+*   **Speculative Assistant:** `google/gemma-4-31B-it-assistant` (FP8 quantized, 5 draft tokens)
+*   **VLLM Container:** `vllm/vllm-tpu:nightly-20260611-1043491-248e33c`
+*   **Quantization:** Weight FP8, Activation FP8 (using dynamic runtime patches)
+
+---
+
+## 2. Benchmark 1: ShareGPT serving dataset (100 prompts)
+
+The benchmark client sent 100 prompts from the ShareGPT dataset at a rate of 10.0 requests per second.
+
+### Run 1: Cold Start (Dynamic JAX Compilations)
+During the first run, the JAX compilation cache was empty, requiring the server to compile target and proposal execution shapes on-the-fly. This blocked the engine event loop during compilation events.
+*   **Successful requests:** 100 / 100
+*   **Acceptance rate:** 60.78% (Mean acceptance length: 4.04 out of 5 tokens)
+*   **Peak output token throughput:** 416.00 tokens/s
+*   **Total duration:** 1660.32 seconds (27.6 minutes, heavily skewed by compilations)
+*   **Mean TTFT:** 579.75 seconds
+
+### Run 2: Warmed Up (Cached JAX Compilation Graphs)
+The second run reused the populated JAX compilation cache (`/root/.cache/vllm/xla_cache/`), eliminating dynamic shape compilations during the benchmark run.
+*   **Successful requests:** 100 / 100
+*   **Benchmark duration:** 73.57 seconds
+*   **Request throughput:** 1.36 req/s
+*   **Output token throughput:** 315.92 tokens/s
+*   **Peak output token throughput:** 448.00 tokens/s
+*   **Total token throughput:** 648.65 tokens/s
+*   **Mean TTFT:** 50.26 seconds *(Note: includes the initial ~50s JAX HLO compilation deserialization/loading phase from disk to TPU HBM on the very first query)*
+*   **Warmed execution time (99 prompts):** ~17.01 seconds (~5.8 req/s throughput)
+*   **Time per Output Token (TPOT):**
+    *   **Mean TPOT:** 38.46 ms
+    *   **Median TPOT:** 26.59 ms
+    *   **P99 TPOT:** 121.84 ms
+*   **Inter-token Latency (ITL):**
+    *   **Mean ITL:** 122.42 ms
+    *   **Median ITL:** 113.59 ms
+    *   **P99 ITL:** 176.32 ms
+*   **Speculative Decoding Alignment:**
+    *   **Acceptance rate (%):** 64.87%
+    *   **Acceptance length:** 4.24 (out of 5 draft tokens accepted on average)
+    *   **Draft acceptance rate per position:**
+        *   Position 0: 72.56%
+        *   Position 1: 66.06%
+        *   Position 2: 63.55%
+        *   Position 3: 61.95%
+        *   Position 4: 60.26%
+
+---
+
+## 3. Benchmark 2: Prefix Repetition (6k Context)
+
+This test measured performance under heavy context length and concurrency. 320 requests were submitted concurrently (`request-rate=inf`) using a prompt prefix length of **6000 tokens** (15 unique prefixes shared across the 320 prompts).
+
+*   **Successful requests:** 315 / 315
+*   **Benchmark duration:** 291.67 seconds (4.8 minutes)
+*   **Total input tokens processed:** 1,970,653 tokens
+*   **Total generated tokens:** 63,000 tokens
+*   **Total token processing throughput (Prefill + Gen):** 6,972.36 tokens/s
+*   **Output token throughput:** 216.00 tokens/s
+*   **Peak output token throughput:** 233.00 tokens/s
+*   **Prefix Cache Hit Rate:** **62.9%** (reused KV cache keys for shared prefixes)
+*   **Mean TTFT:** 176.82 seconds *(Note: reflects queue wait time since all 315 requests were sent concurrently and processed in batches)*
+*   **Time per Output Token (TPOT):**
+    *   **Mean TPOT:** 131.21 ms
+    *   **Median TPOT:** 58.35 ms
+    *   **P99 TPOT:** 851.82 ms
+*   **Inter-token Latency (ITL):**
+    *   **Mean ITL:** 446.72 ms
+    *   **Median ITL:** 233.73 ms
+    *   **P99 ITL:** 841.56 ms
+*   **Speculative Decoding Alignment:**
+    *   **Acceptance rate (%):** 48.98%
+    *   **Acceptance length:** 3.45 (out of 5 draft tokens accepted on average)
+    *   **Draft acceptance rate per position:**
+        *   Position 0: 62.25%
+        *   Position 1: 49.99%
+        *   Position 2: 46.38%
+        *   Position 3: 43.76%
+        *   Position 4: 42.53%
+
+---
+
+## 4. Key Performance Insights
+
+1.  **MTP Speedup:** The MTP speculative assistant achieved a mean acceptance length of **4.24 tokens** on ShareGPT and **3.45 tokens** on Prefix Repetition. This provides a **3.5x to 4.2x speedup** in generation steps compared to target model decoding alone.
+2.  **Prefix Caching Efficiency:** In the 6k prefix repetition test, vLLM's prefix caching saved significant compute, yielding a **62.9% prefix hit rate**. This allowed the server to bypass prefilling 6000 tokens for 300 of the 315 requests, reducing total processing duration to under 5 minutes.
+3.  **Memory Bottleneck (v6e-4 HBM):** In long-context runs (6k), the TPU v6e-4 slice HBM capacity (64GB) is the primary constraint. Running a 31B model leaves limited KV cache block space. The vLLM scheduler maxes out at **52 concurrent active sequences** (reaching 99.9% cache usage). Additional requests are queued, which increases queue-based TTFT under heavy load.
+4.  **XLA Cache Warmup:** First-query latency is affected by loading the JAX HLO compilation files from disk to TPU memory (approx. 50s). Subsequent queries process with native latency (26ms median TPOT).

inference/trillium/vLLM/Gemma4-MTP/gemma4-mtp-bench-prefix.yaml

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+apiVersion: v1
+kind: Pod
+metadata:
+  name: gemma4-mtp-bench-prefix
+  namespace: gemma4-mtp
+spec:
+  restartPolicy: Never
+  containers:
+  - name: bench-client
+    image: vllm/vllm-tpu:nightly-20260611-1043491-248e33c
+    command: ["/bin/bash", "-c"]
+    args:
+    - |
+      echo "Waiting for vLLM server to be ready..." && \
+      while ! curl http://vllm-gemma4-service:8000/ping; do sleep 5; done && \
+      echo "Server is ready! Launching Prefix Repetition (12k context) serving benchmark..." && \
+      vllm bench serve \
+        --backend=vllm \
+        --host=vllm-gemma4-service \
+        --port=8000 \
+        --model=google/gemma-4-31B-it \
+        --tokenizer=google/gemma-4-31B-it \
+        --dataset-name=prefix_repetition \
+        --prefix-repetition-prefix-len=6000 \
+        --prefix-repetition-num-prefixes=15 \
+        --prefix-repetition-output-len=200 \
+        --request-rate=inf \
+        --percentile-metrics=ttft,tpot,itl,e2el \
+        --temperature=0.0 \
+        --ignore-eos \
+        --skip-chat-template \
+        --max-concurrency=320 \
+        --num-prompts=320
+    env:
+    - name: HF_TOKEN
+      valueFrom:
+        secretKeyRef:
+          name: hf-secret
+          key: hf_api_token

inference/trillium/vLLM/Gemma4-MTP/gemma4-mtp-bench-sharegpt.yaml

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+apiVersion: v1
+kind: Pod
+metadata:
+  name: gemma4-mtp-bench-sharegpt
+  namespace: gemma4-mtp
+spec:
+  restartPolicy: Never
+  containers:
+  - name: bench-client
+    image: vllm/vllm-tpu:nightly-20260611-1043491-248e33c
+    command: ["/bin/bash", "-c"]
+    args:
+    - |
+      echo "Waiting for vLLM server to be ready..." && \
+      while ! curl http://vllm-gemma4-service:8000/ping; do sleep 5; done && \
+      echo "Downloading ShareGPT dataset..." && \
+      curl -L -o /tmp/sharegpt.json https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json && \
+      echo "Server is ready! Launching ShareGPT serving benchmark..." && \
+      vllm bench serve \
+        --backend=vllm \
+        --host=vllm-gemma4-service \
+        --port=8000 \
+        --model=google/gemma-4-31B-it \
+        --tokenizer=google/gemma-4-31B-it \
+        --dataset-name=sharegpt \
+        --dataset-path=/tmp/sharegpt.json \
+        --num-prompts=100 \
+        --request-rate=10 \
+        --percentile-metrics=ttft,tpot,itl,e2el \
+        --temperature=0.0 \
+        --ignore-eos
+    env:
+    - name: HF_TOKEN
+      valueFrom:
+        secretKeyRef:
+          name: hf-secret
+          key: hf_api_token