Add SPEED-Bench (within repo)

docs/evaluation/index.md

@@ -12,6 +12,7 @@ We support many popular benchmarks and it's easy to add new in the future. The f
 - [**Multilingual**](./multilingual.md): e.g. [mmlu-prox](./multilingual.md#mmlu-prox), [flores-200](./multilingual.md#flores-200), [wmt24pp](./multilingual.md#wmt24pp)
 - [**Speech & Audio**](./speech-audio.md): e.g. [asr-leaderboard](./speech-audio.md#asr-leaderboard), [mmau-pro](./speech-audio.md#mmau-pro)
 - [**Vision-Language Models (VLM)**](./vlm.md): e.g. [mmmu-pro](./vlm.md#mmmu-pro)
+- [**Speculative Decoding (SD)**](./speculative-decoding.md): e.g. [SPEED-Bench](./speculative-decoding.md#SPEED-Bench)
 
 See [nemo_skills/dataset](https://github.com/NVIDIA-NeMo/Skills/blob/main/nemo_skills/dataset) where each folder is a benchmark we support.
 

docs/evaluation/speculative-decoding.md

@@ -0,0 +1,96 @@
+# Speculative Decoding
+
+This section details how to evaluate speculative decoding (SD) benchmarks.
+SD has emerged as a leading technique for accelerating LLM inference. By allowing a smaller draft model to propose multiple future tokens that are verified in a single forward pass by a larger target model, SD can significantly increase system throughput.
+
+In all SD benchmarks we want to measure two qualitative metrics for draft accuracy/quality: acceptance length (AL), acceptance rate (AR).
+Other metric in this group is conditional acceptance rate (or per-position acceptance rate), which measures the acceptance rate in a given position conditioned that all previous tokens were accepted.
+
+For more advanced evaluation of SD, including throughput and per-category metrics, please use the evaluation framework [here](https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/specdec_bench).
+
+
+## How we evaluate?
+
+!!! note
+    The current evaluation supports only SGLang and VLLM servers.
+
+The evaluation is executed by the following process:
+
+1. Get SD metrics from `/metrics` endpoint of the server.
+2. Send the benchmark's prompts to the server.
+3. Get metrics from `/metrics` endpoint, and calculate the difference from step (1), to get the average SD metrics (AL, AR, etc.).
+
+!!! note
+    For `local` executor and SGLang server, we also support a flow which writes a metrics file per request to a local path, and then we calculate the SD metrics based on this file. This way, we can have a per-request metric, which can be relevant in some cases. More information on this feature can be found in [SGLang Documentation](https://docs.sglang.io/advanced_features/server_arguments.html#requestmetricsexporter-configuration).
+
+
+## Supported Benchmarks
+
+### SPEED-Bench
+
+- Benchmark is defined in [`nemo_skills/dataset/speed-bench/__init__.py`](https://github.com/NVIDIA-NeMo/Skills/blob/main/nemo_skills/dataset/speed-bench/__init__.py)
+- Original benchmark source, is [here](https://huggingface.co/datasets/nvidia/SPEED-Bench).
+- NOTICE: This dataset is governed by the [NVIDIA Evaluation Dataset License Agreement](https://huggingface.co/datasets/nvidia/SPEED-Bench/blob/main/License.pdf). For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose. The `prepare_data` script automatically fetches data from all the source datasets.
+
+#### Data preparation
+
+See example of data preparation command in [main evaluation docs](../evaluation/index.md#using-data-on-cluster).
+
+```shell
+ns prepare_data speed-bench --data_dir=<output directory for data files> --cluster=<cluster config>
+```
+
+Other supported options:
+
+  * **config**: select which config to prepare, can be one of the splits in the dataset (e.g., `qualitative`, `throughput_2k`) or `all` to prepare all of the configs.
+
+
+#### Evaluation command
+
+An example of running Llama 3.3 70B with external draft Llama 3.2 1B using SGLang and a draft length of 3:
+
+```bash
+ns eval \
+    --cluster=<cluster config> \
+    --data_dir=<must match prepare_data parameter> \
+    --output_dir=<any mounted output location> \
+    --benchmarks=speed-bench \
+    --model=meta-llama/Llama-3.3-70B-Instruct \
+    --server_args="--speculative-algorithm STANDALONE --speculative-draft-model-path meta-llama/Llama-3.2-1B-Instruct --speculative-num-steps 3 --speculative-eagle-topk 1 --torch-compile-max-bs 32 --max-running-requests 32 --cuda-graph-max-bs 32 --mem-fraction-static 0.8" \
+    --server_nodes=1 \
+    --server_gpus=8 \
+    --server_type=sglang \
+    ++inference.tokens_to_generate=1024
+```
+
+Example evaluation metrics:
+
+```
+--------------------------------------------- speed-bench ----------------------------------------------
+evaluation_mode | num_entries | avg_tokens | gen_seconds | spec_acceptance_length | spec_acceptance_rate
+pass@1          | 880         | 464        | 139         | 2.78                   | 69.38
+```
+
+An example of running Llama 3.3 70B with EAGLE3 using vLLM and a draft length of 3:
+
+```bash
+ns eval \
+    --cluster=<cluster config> \
+    --data_dir=<must match prepare_data parameter> \
+    --output_dir=<any mounted output location> \
+    --benchmarks=speed-bench \
+    --model=meta-llama/Llama-3.3-70B-Instruct \
+    --server_args="--speculative-config '{\"method\": \"eagle3\", \"num_speculative_tokens\": 3, \"model\": \"nvidia/Llama-3.3-70B-Instruct-Eagle3\"}'" \
+    --server_nodes=1 \
+    --server_gpus=8 \
+    --server_type=vllm \
+    ++inference.tokens_to_generate=1024
+```
+
+Example evaluation metrics:
+
+```
+--------------------------------------------- speed-bench ----------------------------------------------
+evaluation_mode | num_entries | avg_tokens | gen_seconds | spec_acceptance_length | spec_acceptance_rate
+pass@1          | 880         | 463        | 104         | 2.37                   | 45.52
+```

mkdocs.yml

@@ -89,6 +89,7 @@ nav:
     - evaluation/vlm.md
     - evaluation/other-benchmarks.md
     - evaluation/robustness.md
+    - evaluation/speculative-decoding.md
     - External benchmarks: evaluation/external-benchmarks.md
   - Agentic Inference:
     - agentic_inference/parallel_thinking.md

nemo_skills/dataset/speed-bench/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) 2026, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# settings that define how evaluation should be done by default (all can be changed from cmdline)
+REQUIRES_DATA_DIR = True
+METRICS_TYPE = "specdec"
+EVAL_SPLIT = "qualitative"
+GENERATION_ARGS = "++prompt_format=openai ++eval_type=specdec ++inference.include_response=true"
+GENERATION_MODULE = "nemo_skills.inference.eval.specdec"