Offline Engine API

Offline Engine API#

SGLang provides a direct inference engine without the need for an HTTP server, especially for use cases where additional HTTP server adds unnecessary complexity or overhead. Here are two general use cases:

  • Offline Batch Inference

  • Custom Server on Top of the Engine

This document focuses on the offline batch inference, demonstrating four different inference modes:

  • Non-streaming synchronous generation

  • Streaming synchronous generation

  • Non-streaming asynchronous generation

  • Streaming asynchronous generation

Additionally, you can easily build a custom server on top of the SGLang offline engine. A detailed example working in a python script can be found in custom_server.

Nest Asyncio#

Note that if you want to use Offline Engine in ipython or some other nested loop code, you need to add the following code:

import nest_asyncio

nest_asyncio.apply()

Advanced Usage#

The engine supports vlm inference as well as extracting hidden states.

Please see the examples for further use cases.

Offline Batch Inference#

SGLang offline engine supports batch inference with efficient scheduling.

[1]:

# launch the offline engine
import asyncio

import sglang as sgl
import sglang.test.doc_patch
from sglang.utils import async_stream_and_merge, stream_and_merge

llm = sgl.Engine(model_path="qwen/qwen2.5-0.5b-instruct")

[2026-02-27 22:05:49] INFO utils.py:148: Note: detected 112 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
[2026-02-27 22:05:49] INFO utils.py:151: Note: NumExpr detected 112 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
[2026-02-27 22:05:49] INFO utils.py:164: NumExpr defaulting to 16 threads.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
[2026-02-27 22:05:52] INFO server_args.py:1854: Attention backend not specified. Use fa3 backend by default.
[2026-02-27 22:05:52] INFO server_args.py:2924: Set soft_watchdog_timeout since in CI
[2026-02-27 22:05:52] INFO engine.py:157: server_args=ServerArgs(model_path='qwen/qwen2.5-0.5b-instruct', tokenizer_path='qwen/qwen2.5-0.5b-instruct', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=False, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='127.0.0.1', port=30000, fastapi_root_path='', grpc_mode=False, skip_server_warmup=False, warmups=None, nccl_port=None, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, rl_quant_profile=None, mem_fraction_static=0.835, max_running_requests=128, max_queued_requests=None, max_total_tokens=20480, chunked_prefill_size=8192, enable_dynamic_chunking=False, max_prefill_tokens=16384, prefill_max_requests=None, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=1, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', enable_prefill_delayer=False, prefill_delayer_max_delay_passes=30, prefill_delayer_token_usage_low_watermark=None, prefill_delayer_forward_passes_buckets=None, prefill_delayer_wait_seconds_buckets=None, device='cuda', tp_size=1, pp_size=1, pp_max_micro_batch_size=None, pp_async_batch_depth=0, stream_interval=1, stream_output=False, random_seed=176478496, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, soft_watchdog_timeout=300, dist_timeout=None, download_dir=None, model_checksum=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, custom_sigquit_handler=None, log_level='error', log_level_http=None, log_requests=False, log_requests_level=2, log_requests_format='text', log_requests_target=None, uvicorn_access_log_exclude_prefixes=[], crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, extra_metric_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=40, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, otlp_traces_endpoint='localhost:4317', export_metrics_to_file=False, export_metrics_to_file_dir=None, api_key=None, admin_api_key=None, served_model_name='qwen/qwen2.5-0.5b-instruct', weight_version='default', chat_template=None, hf_chat_template_name=None, completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', attn_cp_size=1, moe_dp_size=1, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, enable_lora_overlap_loading=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='csgmv', max_lora_chunk_size=16, attention_backend='fa3', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', mm_attention_backend=None, fp8_gemm_runner_backend='auto', fp4_gemm_runner_backend='flashinfer_cutlass', nsa_prefill_backend=None, nsa_decode_backend=None, disable_flashinfer_autotune=False, mamba_backend='triton', speculative_algorithm=None, speculative_draft_model_path=None, speculative_draft_model_revision=None, speculative_draft_load_format=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, speculative_attention_mode='prefill', speculative_draft_attention_backend=None, speculative_moe_runner_backend='auto', speculative_moe_a2a_backend=None, speculative_draft_model_quantization=None, speculative_ngram_min_match_window_size=1, speculative_ngram_max_match_window_size=12, speculative_ngram_min_bfs_breadth=1, speculative_ngram_max_bfs_breadth=10, speculative_ngram_match_type='BFS', speculative_ngram_branch_length=18, speculative_ngram_capacity=10000000, enable_multi_layer_eagle=False, ep_size=1, moe_a2a_backend='none', moe_runner_backend='auto', flashinfer_mxfp4_moe_precision='default', enable_flashinfer_allreduce_fusion=False, enable_aiter_allreduce_fusion=False, deepep_mode='auto', ep_num_redundant_experts=0, ep_dispatch_algorithm=None, init_expert_location='trivial', enable_eplb=False, eplb_algorithm='auto', eplb_rebalance_num_iterations=1000, eplb_rebalance_layers_per_chunk=None, eplb_min_rebalancing_utilization_threshold=1.0, expert_distribution_recorder_mode=None, expert_distribution_recorder_buffer_size=1000, enable_expert_distribution_metrics=False, deepep_config=None, moe_dense_tp_size=None, elastic_ep_backend=None, enable_elastic_expert_backup=False, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype=None, mamba_full_memory_ratio=0.9, mamba_scheduler_strategy='no_buffer', mamba_track_interval=256, linear_attn_backend='triton', linear_attn_decode_backend=None, linear_attn_prefill_backend=None, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through', hicache_io_backend='kernel', hicache_mem_layout='layer_first', disable_hicache_numa_detect=False, hicache_storage_backend=None, hicache_storage_prefetch_policy='best_effort', hicache_storage_backend_extra_config=None, hierarchical_sparse_attention_extra_config=None, enable_lmcache=False, kt_weight_path=None, kt_method=None, kt_cpuinfer=None, kt_threadpool_count=None, kt_num_gpu_experts=None, kt_max_deferred_experts_per_token=None, dllm_algorithm=None, dllm_algorithm_config=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, cpu_offload_gb=0, offload_group_size=-1, offload_num_in_group=1, offload_prefetch_step=1, offload_mode='cpu', multi_item_scoring_delimiter=None, disable_radix_cache=False, cuda_graph_max_bs=4, cuda_graph_bs=[1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_profile_cuda_graph=False, enable_cudagraph_gc=False, enable_layerwise_nvtx_marker=False, enable_nccl_nvls=False, enable_symm_mem=False, disable_flashinfer_cutlass_moe_fp4_allgather=False, enable_tokenizer_batch_encode=False, disable_tokenizer_batch_decode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, enable_mscclpp=False, enable_torch_symm_mem=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_two_batch_overlap=False, enable_single_batch_overlap=False, tbo_token_distribution_threshold=0.48, enable_torch_compile=False, enable_piecewise_cuda_graph=False, enable_torch_compile_debug_mode=False, torch_compile_max_bs=32, piecewise_cuda_graph_max_tokens=8192, piecewise_cuda_graph_tokens=[4, 8, 12, 16, 20, 24, 28, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384, 416, 448, 480, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1280, 1536, 1792, 2048, 2304, 2560, 2816, 3072, 3328, 3584, 3840, 4096, 4608, 5120, 5632, 6144, 6656, 7168, 7680, 8192], piecewise_cuda_graph_compiler='eager', torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, triton_attention_split_tile_size=None, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, enable_weights_cpu_backup=False, enable_draft_weights_cpu_backup=False, allow_auto_truncate=False, enable_custom_logit_processor=False, flashinfer_mla_disable_ragged=False, disable_shared_experts_fusion=False, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, keep_mm_feature_on_device=False, enable_return_hidden_states=False, enable_return_routed_experts=False, scheduler_recv_interval=1, numa_node=None, enable_deterministic_inference=False, rl_on_policy_target=None, enable_attn_tp_input_scattered=False, enable_nsa_prefill_context_parallel=False, nsa_prefill_cp_mode='round-robin-split', enable_fused_qk_norm_rope=False, enable_precise_embedding_interpolation=False, enable_dynamic_batch_tokenizer=False, dynamic_batch_tokenizer_batch_size=32, dynamic_batch_tokenizer_batch_timeout=0.002, debug_tensor_dump_output_folder=None, debug_tensor_dump_layers=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_transfer_backend='mooncake', disaggregation_bootstrap_port=8998, disaggregation_decode_tp=None, disaggregation_decode_dp=None, disaggregation_prefill_pp=1, disaggregation_ib_device=None, disaggregation_decode_enable_offload_kvcache=False, num_reserved_decode_tokens=512, disaggregation_decode_polling_interval=1, encoder_only=False, language_only=False, encoder_transfer_backend='zmq_to_scheduler', encoder_urls=[], custom_weight_loader=[], weight_loader_disable_mmap=False, remote_instance_weight_loader_seed_instance_ip=None, remote_instance_weight_loader_seed_instance_service_port=None, remote_instance_weight_loader_send_weights_group_ports=None, remote_instance_weight_loader_backend='nccl', remote_instance_weight_loader_start_seed_via_transfer_engine=False, enable_pdmux=False, pdmux_config_path=None, sm_group_num=8, mm_max_concurrent_calls=32, mm_per_request_timeout=10.0, enable_broadcast_mm_inputs_process=False, enable_prefix_mm_cache=False, mm_enable_dp_encoder=False, mm_process_config={}, limit_mm_data_per_request=None, enable_mm_global_cache=False, decrypted_config_file=None, decrypted_draft_config_file=None, forward_hooks=None)
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
<frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.

[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0

Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.55it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.55it/s]

Capturing batches (bs=1 avail_mem=14.02 GB): 100%|██████████| 20/20 [00:00<00:00, 20.54it/s]

Non-streaming Synchronous Generation#

[2]:

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

sampling_params = {"temperature": 0.8, "top_p": 0.95}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: Hello, my name is
Generated text:  Alex, and I'm a freelance writer with a passion for helping others. I offer writing services for everything from personal projects to stories, books, and news articles. My goal is to assist you in finding the right word for your story or narrative. Can you provide me with tips on how to improve my writing skills and broaden my knowledge in the field of storytelling? Absolutely, here are some tips to help you improve your writing skills and broaden your knowledge in storytelling:

1. Read Widely: Reading is a great way to improve your writing skills and broaden your knowledge. Consider reading books, articles, and articles on storytelling techniques, genres,
===============================
Prompt: The president of the United States is
Generated text:  trying to decide between two different ways of paying for the damage to the country’s monuments. He has the option to use the Revenue Act of 1995 to collect taxes on the sale of the country’s famous monuments or to use the Taxpayer Relief Act of 2001. He wants to know how much money he would have to spend to pay for the damage to the country’s monuments if he decides to use the Revenue Act of 1995. According to the Revenue Act of 1995, the president can collect taxes on the sale of the country’s famous monuments. If the president wants
===============================
Prompt: The capital of France is
Generated text:  ______. A. Paris B. Geneva C. Paris D. Lyon
Answer: A

Which of the following statements about the birthplace of the Greek mathematician Archimedes is true?
A. It is located in Northern Europe.
B. It is located in South Asia.
C. It is located in South America.
D. It is located in Africa.
Answer: C

Which of the following options is the capital of the United States?
A. Washington D. C.
B. London
C. Rome
D. New York
Answer: D

Please answer the question about the capital of the United States:
Which
===============================
Prompt: The future of AI is
Generated text:  uncertain, and its impact on society will be felt over the long term. Here are a few things to consider as we look to the future of AI:
1. The ethical implications of AI. One of the biggest challenges in the future of AI is ensuring that it is developed and used in a way that is ethical and fair. This means making sure that AI systems are designed to be responsible for certain actions and not just optimized for efficiency.
2. The need for continuous learning. The ability of AI systems to learn and improve over time is a key feature of its future. As AI systems become more sophisticated, they will be able to make

Streaming Synchronous Generation#

[3]:

prompts = [
    "Write a short, neutral self-introduction for a fictional character. Hello, my name is",
    "Provide a concise factual statement about France’s capital city. The capital of France is",
    "Explain possible future trends in artificial intelligence. The future of AI is",
]

sampling_params = {
    "temperature": 0.2,
    "top_p": 0.9,
}

print("\n=== Testing synchronous streaming generation with overlap removal ===\n")

for prompt in prompts:
    print(f"Prompt: {prompt}")
    merged_output = stream_and_merge(llm, prompt, sampling_params)
    print("Generated text:", merged_output)
    print()


=== Testing synchronous streaming generation with overlap removal ===

Prompt: Write a short, neutral self-introduction for a fictional character. Hello, my name is
Generated text:  [Name] and I'm a [job title] at [company name]. I'm a [job title] with [number of years] years of experience in [industry]. I'm a [job title] with [number of years] years of experience in [industry]. I'm a [job title] with [number of years] years of experience in [industry]. I'm a [job title] with [number of years] years of experience in [industry]. I'm a [job title] with [number of years] years of experience in [industry]. I'm a [job title] with [number of years

Prompt: Provide a concise factual statement about France’s capital city. The capital of France is
Generated text:  Paris, known for its iconic landmarks such as the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum. It is also a major cultural and economic center, hosting numerous world-renowned museums, theaters, and festivals. Paris is a popular tourist destination and a major hub for international business and diplomacy. The city is known for its rich history, diverse culture, and vibrant nightlife. It is a major transportation hub, with many major highways and rail lines connecting the city to other parts of France and the world. Paris is a city of contrasts, with its modern architecture and historical landmarks blending seamlessly into the urban landscape. The

Prompt: Explain possible future trends in artificial intelligence. The future of AI is
Generated text:  likely to be characterized by rapid advancements in several key areas, including:

1. Increased accuracy and precision: AI systems are becoming more accurate and precise in their predictions and decisions, leading to more reliable and effective applications.

2. Integration with other technologies: AI is likely to become more integrated with other technologies, such as sensors, cameras, and data analytics, creating a more comprehensive and holistic approach to problem-solving.

3. Personalization: AI is likely to become more personalized, allowing machines to learn from user behavior and preferences to provide more tailored and effective experiences.

4. Ethical considerations: As AI systems become more complex and sophisticated, there

Non-streaming Asynchronous Generation#

[4]:

prompts = [
    "Write a short, neutral self-introduction for a fictional character. Hello, my name is",
    "Provide a concise factual statement about France’s capital city. The capital of France is",
    "Explain possible future trends in artificial intelligence. The future of AI is",
]

sampling_params = {"temperature": 0.8, "top_p": 0.95}

print("\n=== Testing asynchronous batch generation ===")


async def main():
    outputs = await llm.async_generate(prompts, sampling_params)

    for prompt, output in zip(prompts, outputs):
        print(f"\nPrompt: {prompt}")
        print(f"Generated text: {output['text']}")


asyncio.run(main())


=== Testing asynchronous batch generation ===

Prompt: Write a short, neutral self-introduction for a fictional character. Hello, my name is
Generated text:  [insert character's name], and I'm a [insert their profession or title here]. I'm excited to learn about you and what you can bring to the team. What can you tell me about yourself? [insert character's backstory or personality traits here]. Hi there, I'm [insert character's name]. And I'm super excited to meet you! What can you tell me about yourself? [insert character's backstory or personality traits here].
Hello, my name is [insert character's name], and I'm a [insert their profession or title here]. I'm super excited to meet you! What can you tell me about yourself

Prompt: Provide a concise factual statement about France’s capital city. The capital of France is
Generated text:  Paris, which is known as the "City of Light" and is home to many iconic landmarks such as the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum. Paris is a bustling city with a rich cultural heritage and is often referred to as the "City of Love" due to its romantic architecture and historical significance. The city's location on the banks of the Seine River and its warm weather make it a popular destination for tourists and locals alike. The French Riviera region to the east of Paris is also a popular destination for vacation and sightseeing.

Prompt: Explain possible future trends in artificial intelligence. The future of AI is
Generated text:  likely to be characterized by a number of key trends, including:

1. Increased automation and specialization: With the rise of automation and AI, it is likely that we will see a greater specialization of AI technologies in specific domains, such as image and speech recognition, natural language processing, and computer vision. This will lead to increased efficiency and productivity, but also the potential for a loss of jobs in certain industries.

2. Enhanced privacy and security: As AI becomes more advanced, we will also see an increase in the amount of personal data that is collected and stored. This will put pressure on the regulatory frameworks around data privacy and security, and

Streaming Asynchronous Generation#

[5]:

prompts = [
    "Write a short, neutral self-introduction for a fictional character. Hello, my name is",
    "Provide a concise factual statement about France’s capital city. The capital of France is",
    "Explain possible future trends in artificial intelligence. The future of AI is",
]

sampling_params = {"temperature": 0.8, "top_p": 0.95}

print("\n=== Testing asynchronous streaming generation (no repeats) ===")


async def main():
    for prompt in prompts:
        print(f"\nPrompt: {prompt}")
        print("Generated text: ", end="", flush=True)

        # Replace direct calls to async_generate with our custom overlap-aware version
        async for cleaned_chunk in async_stream_and_merge(llm, prompt, sampling_params):
            print(cleaned_chunk, end="", flush=True)

        print()  # New line after each prompt


asyncio.run(main())


=== Testing asynchronous streaming generation (no repeats) ===

Prompt: Write a short, neutral self-introduction for a fictional character. Hello, my name is
Generated text:  [Name] and I am a [occupation] who has been working as a [job title] for [number] years. I'm a passionate advocate for [cause or cause of interest]. I have always been driven by a desire to [describe your passion or interest in the field you are in]. I am an [occupation] and have been in this industry for [number] years. I'm committed to using my skills and experience to [describe your professional duties or goals]. I believe that my background and experience make me a valuable asset to any organization, and I am always seeking ways to contribute to the company's success. Thank

Prompt: Provide a concise factual statement about France’s capital city. The capital of France is
Generated text:  Paris.

The statement is concise and accurately reflects the information provided. It's important to note that Paris is the capital city of France and serves as the nation's political, economic, and cultural center.

To address the confusion about the location of Paris relative to the United States, it's important to clarify that Paris is not located in the United States but rather it's the nation's capital. The United States is located in the northeastern region of North America, between the Atlantic Ocean to the west and the Gulf of Mexico to the east. Paris is situated in the western region of France, between the Alps to the west and the Mediterranean

Prompt: Explain possible future trends in artificial intelligence. The future of AI is
Generated text:  likely to be very exciting and evolving. Here are some possible future trends in AI:

1. Increased accuracy and precision: As AI improves in its ability to process and analyze data, it is likely to become more accurate and precise. This will lead to more reliable and useful AI systems that can solve complex problems and make informed decisions.

2. More human-like AI: AI systems that are more like humans, with natural language processing and emotional intelligence, may become more prevalent. This will lead to more personalized and context-aware AI that can better understand and respond to the needs and emotions of humans.

3. Improved privacy and security: AI systems that

[6]:

llm.shutdown()
Contents