How to Run Benchmarks Using vLLM on the Dell Pro Max 16 Plus with the Qualcomm Inference Card in Linux
Summary: Learn how to run benchmarks using vLLM on the Dell Pro Max 16 Plus with the Qualcomm Inference Card in Linux.
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Instructions
- Download a Qualcomm Program Container (QPC.)
Download a prebuilt AI model QPC from Zentree's precompiled model catalog - Zentree-Qualcomm Pre-compiled Model Catalog for Cloud AI Accelerators
Note: Ensure that the QPC is compatible with Dell Pro Max by looking forSoCs / Tensor slicing = 2tar -xzvf ~/Downloads/qpc_name.tar.gz - Serve the Large Language Model (LLM) using vLLM.
Open a Terminal and run the following command (replace<QPC_DIR>,<MODEL_ID>,<CTX_LEN>,<BATCH_SIZE>, and<PREFILL_SEQ_LEN>).Note: That<MODEL_ID>,<CTX_LEN>,<BATCH_SIZE>, and<PREFILL_SEQ_LEN>depend on the QPC compilation config. For QPCs built and hosted by Zentree, these values are documented on their website. The mapping is<BATCH_SIZE>with "Full Batch Size,"<CTX_LEN>with "Context Length (CL)," and<PREFILL_SEQ_LEN>with "Chunking Prompt Length" from the Zentree model QPC Configurations table. Also note that some models require that your setup a Huggingface token which you will need to pass into the container to access.docker run --rm -it \ --name qaic-bench --device=/dev/accel/accel1 \ --device=/dev/accel/accel2 \ --network host \ --ulimit nofile=1048576 \ -e OMP_NUM_THREADS=8 \ -e HF_TOKEN=$HF_TOKEN \ -v ~/Downloads/<QPC_DIR>:/root/qpc \ ghcr.io/quic/cloud_ai_inference_ubuntu24:1.20.6.0 \ /opt/vllm-env/bin/vllm serve <MODEL_ID> \ --max-num-seq <BATCH_SIZE> \ --max-model-len <CTX_LEN> \ --max-seq_len-to-capture <PREFILL_SEQ_LEN> \ --device qaic \ --device-group 0,1 \ --quantization mxfp6 \ --kv-cache-dtype mxint8 \ --override-qaic-config "qpc_path=/root/qpc/qpc"Note: All Zentree-hosted QPCs for Dell Pro Max are built withnum_devices=2, but if a model is built withnum_devices=1, then--device-groupshould be set to0or1.
All Zentree-hosted QPCs for Dell Pro Max are built withmxfp6_matmul=true, but if a model is built withmxfp6_matmul=false, then--quantization mxfp6should be dropped.
All Zentree-hosted QPCs for Dell Pro Max are built withmxint8_kv_cache=true, but if a model is built withmxint8_kv_cache=false, then--kv-cache-dtype mxint8should be dropped.
Once you see the message "Application startup complete," the model is ready for inferencing.
To stop vLLM (therefore unloading the model and closing the container), press Ctrl+C in this Terminal window. Larger models may take up to 1 minute to unload.
References: Docker - Qualcomm Cloud AI SDK User Guideand vLLM - Qualcomm Cloud AI SDK User Guide
- Run Benchmarking.
Open a new Terminal and run the following command (replace<MODEL_ID>and<BATCH_SIZE>to match the model served in step 2). The values fornum-prompts,random-input-len, andrandom-output-lencan be adjusted as wanted.docker exec -it qaic-bench \ /opt/vllm-env/bin/vllm bench serve \ --model \ --num-prompts 25 \ --max-concurrency \ --random-input-len 256 \ --random-output-len 256
Once the benchmark completes the vLLM prints the resulting metrics.
Note: To derive throughput (tokens/second or TPS) for only the decode (token generation) stage excluding the prefill (prompt processing) stage, you can calculate TPS using 1000 ÷ Mean TPOT (ms).
For instance, a mean TPOT of 20ms equates to a decode throughput of 50 tokens/second (TPS).
Working Example:- Download the Qwen3-30B-A3B-Instruct-2507 prebuilt QPC.
For this playbook we use the prebuilt QPC for the Qwen3 30B A3B Instruct 2507
There are 2 prebuilt models available that work on the AI100 inferencing card that supports 2 SoCs - one with a 4K and another with an 8K context length. For this playbook the 2 SoC, 4K context length model is used.
Follow the steps below to download and extract the QPC from the archive. Note that if you do not have curl already installed you can install it in the terminal using the command (sudo apt install curl).# Use existing Downloads folder for model downloads cd ~/Downloads # Download QPC archive from Zentree as qwen3-30b-a3b-instruct-2507.tar.gz curl -fSL https://dc00tk1pxen80.cloudfront.net/SDK1.20.4/Qwen/Qwen3-30B-A3B-Instruct-2507/ Qwen3-30B-A3B-Instruct-2507_qpc_16cores_1pl_4096cl_1bs_2devices_mxfp6_mxint8.tar.gz -o qwen3-30b-a3b-instruct-2507.tar.gz # Extract QPC archive tar -xzvf qwen3-30b-a3b-instruct-2507.tar.gz # Rename extracted directory to qwen3-30b-a3b-instruct-2507-qpc mv $(tar -tzf qwen3-30b-a3b-instruct-2507.tar.gz | head -1 | cut -f1 -d"/") qwen3-30b-a3b-instruct-2507-qpc - Serve the Qwen3:30B model using vLLM.
Open a Terminal and run the following command. Note from the general recipe above that<QPC_DIR>is mapped using the "-v ~/Downloads/qwen3-30b-a3b-instruct-2507-qpc:/root/qpc" line to the QPC directory that the model is downloaded to,<MODEL_ID>is mapped to "Qwen/Qwen3-30B-A3B-Instruct-2507,"<CTX_LEN>is mapped to 4096,<BATCH_SIZE>is mapped to 1, and<PREFILL_SEQ_LEN>is mapped to 1 (all from the Zentree model page for the Qwen3:30B model using a 4K context length).
Note that if you have not previously used this version of the AI100 Docker container it fetches ~14GB before opening the Docker container.docker run --rm -it \ --name qaic-bench \ --device=/dev/accel/accel1 \ --device=/dev/accel/accel2 \ --network host \ --ulimit nofile=1048576 \ -e OMP_NUM_THREADS=8 \ -v ~/Downloads/qwen3-30b-a3b-instruct-2507-qpc:/root/qpc \ ghcr.io/quic/cloud_ai_inference_ubuntu24:1.20.6.0 \ /opt/vllm-env/bin/vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 \ --max-num-seq 1 \ --max-model-len 4096 \ --max-seq_len-to-capture 1 \ --device qaic \ --device-group 0,1 \ --quantization mxfp6 \ --kv-cache-dtype mxint8 \ --override-qaic-config "qpc_path=/root/qpc/qpc"
The model is ready for inferencing once you see the message "Application startup complete."
Note: To stop vLLM (unloading the model and closing the container), press Ctrl+C in this terminal window. Larger models may take up to 1 minute to unload.
References: Docker - Qualcomm Cloud AI SDK User Guide
- Run Benchmarking.
Open a new Terminal and run the following command (replace<MODEL_ID>for--modeland<BATCH_SIZE>for--max-concurrencyto match the model served in step 2). The values fornum-prompts,random-input-len, andrandom-output-lencan be adjusted as wanted.docker exec -it qaic-bench \ /opt/vllm-env/bin/vllm bench serve \ --model Qwen/Qwen3-30B-A3B-Instruct-2507 \ --num-prompts 25 \ --max-concurrency 1 \ --random-input-len 256 \ --random-output-len 256 - Sample Output Capture.
Sample output for serving Qwen3:30B using vLLM.
Sample Output for vLLM:dell@dell:~/Downloads/qwen3-30b-a3b-instruct-2507-qpc$ docker run --rm -it \ --name qaic-bench \ --device=/dev/accel/accel1 \ --device=/dev/accel/accel2 \ --network host \ --ulimit nofile=1048576 \ -e OMP_NUM_THREADS=8 \ -v ~/Downloads/qwen3-30b-a3b-instruct-2507-qpc:/root/qpc \ ghcr.io/quic/cloud_ai_inference_ubuntu24:1.20.6.0 \ /opt/vllm-env/bin/vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 \ --max-num-seq 1 \ --max-model-len 4096 \ --max-seq_len-to-capture 1 \ --device qaic \ --device-group 0,1 \ --quantization mxfp6 \ --kv-cache-dtype mxint8 \ --override-qaic-config "qpc_path=/root/qpc/qpc" loading /opt/qti-aic/dev/lib/x86_64/libQAic.so INFO 01-15 17:46:28 [__init__.py:269] Automatically detected platform qaic. INFO 01-15 17:46:30 [api_server.py:1043] vLLM API server version 0.8.6.dev0+gba41cc90e.d20251229 INFO 01-15 17:46:30 [api_server.py:1044] args: Namespace(subparser='serve', model_tag='Qwen/Qwen3-30B-A3B-Instruct-2507', config='', host=None, port=8000, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='Qwen/Qwen3-30B-A3B-Instruct-2507', task='auto', tokenizer=None, hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config={}, use_tqdm_on_load=True, config_format=<ConfigFormat.AUTO: 'auto'>, dtype='auto', max_model_len=4096, guided_decoding_backend='auto', reasoning_parser=None, logits_processor_pattern=None, model_impl='auto', distributed_executor_backend=None, pipeline_parallel_size=1, tensor_parallel_size=1, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, gpu_memory_utilization=0.9, swap_space=4, kv_cache_dtype='mxint8', num_gpu_blocks_override=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', cpu_offload_gb=0, calculate_kv_scales=False, disable_sliding_window=False, use_v2_block_manager=True, seed=None, max_logprobs=20, disable_log_stats=False, quantization='mxfp6', rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=1, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config={}, limit_mm_per_prompt={}, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=None, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=None, max_prompt_adapters=1, max_prompt_adapter_token=0, device='qaic', speculative_config=None, ignore_patterns=[], served_model_name=None, qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, max_num_batched_tokens=None, max_num_seqs=1, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, num_lookahead_slots=0, scheduler_delay_factor=0.0, preemption_mode=None, num_scheduler_steps=1, multi_step_stream_outputs=True, scheduling_policy='fcfs', enable_chunked_prefill=None, disable_chunked_mm_input=False, scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, additional_config=None, enable_reasoning=False, disable_cascade_attn=False, override_qaic_config={'qpc_path': '/root/qpc/qpc'}, device_group=[0, 1], disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False, enable_server_load_tracking=False, dispatch_function=<function ServeSubcommand.cmd at 0x7416f976a710>) config.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 963/963 [00:00<00:00, 6.38MB/s] INFO 01-15 17:46:34 [config.py:728] This model supports multiple tasks: {'score', 'generate', 'classify', 'embed', 'reward'}. Defaulting to 'generate'. WARNING 01-15 17:46:34 [arg_utils.py:1711] --kv-cache-dtype is not supported by the V1 Engine. Falling back to V0. INFO 01-15 17:46:34 [config.py:1415] Using fp8 data type to store kv cache. It reduces the GPU memory footprint and boosts the performance. Meanwhile, it may cause accuracy drop without a proper scaling factor INFO 01-15 17:46:34 [config.py:1820] Disabled the custom all-reduce kernel because it is not supported on current platform. config_1m.json: 77.3kB [00:00, 163MB/s] generation_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████| 239/239 [00:00<00:00, 2.47MB/s] model.safetensors.index.json: 1.70MB [00:00, 57.5MB/s] tokenizer.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 11.4M/11.4M [00:02<00:00, 5.23MB/s] tokenizer_config.json: 9.38kB [00:00, 41.9MB/s] vocab.json: 2.78MB [00:00, 25.4MB/s] INFO 01-15 17:46:38 [api_server.py:246] Started engine process with PID 51 merges.txt: 1.67MB [00:00, 11.8MB/s] loading /opt/qti-aic/dev/lib/x86_64/libQAic.so INFO 01-15 17:46:40 [__init__.py:269] Automatically detected platform qaic. INFO 01-15 17:46:41 [llm_engine.py:247] Initializing a V0 LLM engine (v0.8.6.dev0+gba41cc90e.d20251229) with config: model='Qwen/Qwen3-30B-A3B-Instruct-2507', speculative_config=None, tokenizer='Qwen/Qwen3-30B-A3B-Instruct-2507', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=True, quantization=mxfp6, enforce_eager=False, kv_cache_dtype=mxint8, device_config=cpu, decoding_config=DecodingConfig(guided_decoding_backend='auto', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=Qwen/Qwen3-30B-A3B-Instruct-2507, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=False, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[1],"max_capture_size":1}, use_cached_outputs=True, WARNING 01-15 17:46:42 [qaic.py:100] Pin memory is not supported on Qaic. WARNING 01-15 17:46:42 [registry.py:217] Model class <class 'vllm.model_executor.models.mllama.MllamaForConditionalGeneration'> already has a multi-modal processor registered to <vllm.multimodal.registry.MultiModalRegistry object at 0x7f2ffb2ddc90>. It is overwritten by the new one. WARNING 01-15 17:46:42 [registry.py:217] Model class <class 'vllm.model_executor.models.mllama4.Llama4ForConditionalGeneration'> already has a multi-modal processor registered to <vllm.multimodal.registry.MultiModalRegistry object at 0x7f2ffb2ddc90>. It is overwritten by the new one. WARNING 01-15 17:46:42 [qaic_worker.py:102] Reducing Torch parallelism from 8 threads to 8 to avoid unnecessary CPU contention. Set VLLM_QAIC_MAX_CPU_THREADS to tune this value as needed. INFO 01-15 17:46:42 [qaic_worker.py:87] Setting RLIMIT_STACK to 16.0MB. INFO 01-15 17:46:44 [parallel_state.py:1004] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0 loading /opt/qti-aic/dev/lib/x86_64/libQAic.so INFO 01-15 17:46:44 [executor_base.py:112] # qaic blocks: 1, # CPU blocks: 0 INFO 01-15 17:46:44 [executor_base.py:117] Maximum concurrency for 4096 tokens per request: 1.00x {'prefill_seq_len': 1, 'ctx_len': 4096, 'batch_size': 1, 'full_batch_size': 1, 'device_group': [0, 1], 'num_devices': 2, 'num_cores': 16, 'mxfp6_matmul': True, 'mxint8_kv_cache': True, 'aic_enable_depth_first': True, 'prefill_only': None, 'compile_only': False} INFO 01-15 17:46:44 [qaic.py:959] Using qpc:-/root/qpc/qpc Stages: 1 INFO 01-15 17:46:44 [qserve_model_runner.py:66] Loading QPC... INFO 01-15 17:46:44 [qserve_model_runner.py:67] This may take some time, please don't press CTRL-C during this phase... INFO 01-15 17:46:52 [qserve_model_runner.py:88] Successfully loaded QPC in 7.791425864999837 secs WARNING 01-15 17:46:52 [qserve_model_runner.py:122] User entered `include_sampler`=True. But the provided QPC is not compiled to run sampling on the QAIC device. Falling back to the PyTorch backend. INFO 01-15 17:46:52 [qserve_model_runner.py:162] Warming-up with Dummy run... INFO 01-15 17:46:52 [qserve_model_runner.py:178] Successfully finished Dummy run in 0.04556767500025671 secs INFO 01-15 17:46:52 [llm_engine.py:444] init engine (profile, create kv cache, warmup model) took 7.86 seconds WARNING 01-15 17:46:53 [config.py:1251] Default sampling parameters have been overridden by the model's Hugging Face generation config recommended from the model creator. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`. INFO 01-15 17:46:53 [serving_chat.py:124] Using default chat sampling params from model: {'temperature': 0.7, 'top_k': 20, 'top_p': 0.8} INFO 01-15 17:46:53 [serving_completion.py:61] Using default completion sampling params from model: {'temperature': 0.7, 'top_k': 20, 'top_p': 0.8} INFO 01-15 17:46:53 [api_server.py:1090] Starting vLLM API server on http://0.0.0.0:8000 INFO 01-15 17:46:53 [launcher.py:28] Available routes are: INFO 01-15 17:46:53 [launcher.py:36] Route: /openapi.json, Methods: GET, HEAD INFO 01-15 17:46:53 [launcher.py:36] Route: /docs, Methods: GET, HEAD INFO 01-15 17:46:53 [launcher.py:36] Route: /docs/oauth2-redirect, Methods: GET, HEAD INFO 01-15 17:46:53 [launcher.py:36] Route: /redoc, Methods: GET, HEAD INFO 01-15 17:46:53 [launcher.py:36] Route: /health, Methods: GET INFO 01-15 17:46:53 [launcher.py:36] Route: /load, Methods: GET INFO 01-15 17:46:53 [launcher.py:36] Route: /ping, Methods: POST, GET INFO 01-15 17:46:53 [launcher.py:36] Route: /tokenize, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /detokenize, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/models, Methods: GET INFO 01-15 17:46:53 [launcher.py:36] Route: /version, Methods: GET INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/chat/completions, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/completions, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/embeddings, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /pooling, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /score, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/score, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/audio/transcriptions, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /rerank, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v1/rerank, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /v2/rerank, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /invocations, Methods: POST INFO 01-15 17:46:53 [launcher.py:36] Route: /metrics, Methods: GET INFO: Started server process [1] INFO: Waiting for application startup. INFO: Application startup complete.
Sample benchmarking output for qaic-bench.
Sample output for qaic-benchdell@dell:~$ docker exec -it qaic-bench \ /opt/vllm-env/bin/vllm bench serve \ --model Qwen/Qwen3-30B-A3B-Instruct-2507 \ --num-prompts 25 \ --max-concurrency 1 \ --random-input-len 256 \ --random-output-len 256 loading /opt/qti-aic/dev/lib/x86_64/libQAic.so INFO 01-15 17:47:44 [__init__.py:269] Automatically detected platform qaic. Namespace(subparser='bench', bench_type='serve', dispatch_function=<function BenchmarkServingSubcommand.cmd at 0x71abbd45e9e0>, endpoint_type='openai-comp', label=None, base_url=None, host='127.0.0.1', port=8000, endpoint='/v1/completions', dataset_name='random', max_concurrency=1, model='Qwen/Qwen3-30B-A3B-Instruct-2507', tokenizer=None, best_of=1, use_beam_search=False, num_prompts=25, logprobs=None, request_rate=inf, burstiness=1.0, seed=0, trust_remote_code=False, disable_tqdm=False, profile=False, save_result=False, metadata=None, result_dir=None, result_filename=None, ignore_eos=False, percentile_metrics='ttft,tpot,itl', metric_percentiles='99', goodput=None, random_input_len=256, random_output_len=256, random_range_ratio=1.0, random_prefix_len=0, tokenizer_mode='auto', served_model_name=None, lora_modules=None) Starting initial single prompt test run... Initial test run completed. Starting main benchmark run... Traffic request rate: inf Burstiness factor: 1.0 (Poisson process) Maximum request concurrency: 1 100%|████████████████████████████████████████████████████████████████████████████████████| 25/25 [05:00<00:00, 12.02s/it] ============ Serving Benchmark Result ============ Successful requests: 25 Benchmark duration (s): 300.53 Total input tokens: 6400 Total generated tokens: 6400 Request throughput (req/s): 0.08 Output token throughput (tok/s): 21.30 Total Token throughput (tok/s): 42.59 ---------------Time to First Token---------------- Mean TTFT (ms): 5915.88 Median TTFT (ms): 5803.64 P99 TTFT (ms): 7153.97 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 23.94 Median TPOT (ms): 23.82 P99 TPOT (ms): 25.18 ---------------Inter-token Latency---------------- Mean ITL (ms): 23.94 Median ITL (ms): 23.78 P99 ITL (ms): 25.77 ================================================== dell@dell:~$
To calculate the TPS (tokens/second) results for the output-generated tokens, use 1000 ÷ mean TPOT (23.94 from above) = 41.8 TPS.
- Download the Qwen3-30B-A3B-Instruct-2507 prebuilt QPC.
Affected Products
Dell Pro Max 16 Plus MB16250Article Properties
Article Number: 000446730
Article Type: How To
Last Modified: 24 May 2026
Version: 5
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