TensorRT-LLM

Maximum LLM inference throughput with NVIDIA TensorRT optimization — deployed via Triton Inference Server

TensorRT-LLM is NVIDIA's open-source library for optimizing Large Language Model inference on NVIDIA GPUs. It delivers state-of-the-art performance through kernel fusion, quantization (INT4, INT8, FP8), in-flight batching, and paged KV-caching. Combined with Triton Inference Server, you get a production-grade serving infrastructure.

GitHub: NVIDIA/TensorRT-LLM — 10K+ ⭐

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Why TensorRT-LLM?

Feature	vLLM	TensorRT-LLM
Throughput	Excellent	Best-in-class
Latency	Good	Excellent
INT4/INT8 quantization	Partial	Native
FP8 support	Limited	Full
Multi-GPU tensor parallel	Yes	Yes
Setup complexity	Low	Medium-High

TensorRT-LLM typically delivers 2–4x higher throughput compared to standard HuggingFace transformers inference, and 30–50% better throughput than vLLM for batch serving scenarios.

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Prerequisites

• Clore.ai account with GPU rental

• NVIDIA GPU with Ampere architecture or newer (RTX 3090, A100, RTX 4090, H100)

• Basic Linux and Docker knowledge

• Sufficient VRAM for your chosen model

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VRAM Requirements by Model

Model	FP16	INT8	INT4
Llama-3.1 8B	16GB	8GB	4GB
Llama-3.1 70B	140GB	70GB	35GB
Mistral 7B	14GB	7GB	4GB
Mixtral 8x7B	90GB	45GB	24GB
Qwen2.5 72B	144GB	72GB	36GB

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Step 1 — Choose Your GPU on Clore.ai

1. Log in to clore.ai → Marketplace

2. For single GPU serving (7B–13B models): RTX 4090 24GB or RTX 3090 24GB

3. For large models (70B+): Multiple A100 80GB or H100

Multi-GPU Strategy:

• 2x A100 80GB → Llama 3.1 70B in FP16 or Qwen2.5 72B

• 4x A100 80GB → Llama 3.1 405B in INT8

• Select servers with multiple GPUs listed in the Clore.ai marketplace

---

Step 2 — Deploy Triton Inference Server with TRT-LLM Backend

Docker Image:

nvcr.io/nvidia/tritonserver:24.01-trtllm-python-py3

Use the -trtllm-python-py3 variant — this includes the TensorRT-LLM backend pre-installed. The tag corresponds to the NVIDIA container release (24.01 = January 2024). Check NGC for the latest tag.

Exposed Ports:

22
8000

Environment Variables:

NVIDIA_VISIBLE_DEVICES=all
NVIDIA_DRIVER_CAPABILITIES=compute,utility
TRANSFORMERS_CACHE=/workspace/hf_cache
HF_HOME=/workspace/hf_cache

Volume/Disk: Minimum 100GB recommended

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Step 3 — Connect and Verify Installation

ssh root@<server-ip> -p <ssh-port>

# Check GPU
nvidia-smi

# Check TensorRT version
python3 -c "import tensorrt_llm; print(tensorrt_llm.__version__)"

# Check Triton is available
tritonserver --version

---

Step 4 — Download and Prepare Model

We'll use Llama 3.1 8B as the example. Adjust paths for your chosen model.

Install HuggingFace CLI

pip install huggingface_hub
huggingface-cli login
# Enter your HuggingFace token when prompted

Download Model Weights

mkdir -p /workspace/models/llama-3.1-8b
huggingface-cli download \
    meta-llama/Llama-3.1-8B-Instruct \
    --local-dir /workspace/models/llama-3.1-8b \
    --local-dir-use-symlinks False

# Or use snapshot_download
python3 << 'EOF'
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="meta-llama/Llama-3.1-8B-Instruct",
    local_dir="/workspace/models/llama-3.1-8b",
    local_dir_use_symlinks=False
)
EOF

---

Step 5 — Build TensorRT Engine

This is the key step — compiling the model into an optimized TensorRT engine.

FP16 Engine (Best Quality)

cd /workspace

# Convert HuggingFace weights to TRT-LLM format
python3 /usr/local/lib/python3.10/dist-packages/tensorrt_llm/examples/llama/convert_checkpoint.py \
    --model_dir /workspace/models/llama-3.1-8b \
    --output_dir /workspace/trt_checkpoints/llama-3.1-8b-fp16 \
    --dtype float16 \
    --tp_size 1

# Build TensorRT engine
trtllm-build \
    --checkpoint_dir /workspace/trt_checkpoints/llama-3.1-8b-fp16 \
    --output_dir /workspace/trt_engines/llama-3.1-8b-fp16 \
    --gemm_plugin float16 \
    --max_batch_size 32 \
    --max_input_len 4096 \
    --max_seq_len 8192 \
    --max_num_tokens 16384 \
    --use_paged_context_fmha enable

INT8 SmoothQuant Engine (Higher Throughput)

# Convert with SmoothQuant quantization
python3 /usr/local/lib/python3.10/dist-packages/tensorrt_llm/examples/llama/convert_checkpoint.py \
    --model_dir /workspace/models/llama-3.1-8b \
    --output_dir /workspace/trt_checkpoints/llama-3.1-8b-int8 \
    --dtype float16 \
    --smoothquant 0.5 \
    --per_channel \
    --per_token

trtllm-build \
    --checkpoint_dir /workspace/trt_checkpoints/llama-3.1-8b-int8 \
    --output_dir /workspace/trt_engines/llama-3.1-8b-int8 \
    --gemm_plugin float16 \
    --smoothquant_plugin float16 \
    --max_batch_size 64 \
    --max_input_len 4096 \
    --max_seq_len 8192

INT4 AWQ Engine (Maximum Throughput / Minimum Memory)

# Install auto-gptq for quantization
pip install autoawq

# Quantize to INT4 AWQ
python3 << 'EOF'
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = "/workspace/models/llama-3.1-8b"
quant_path = "/workspace/models/llama-3.1-8b-awq-int4"

model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

quant_config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}
model.quantize(tokenizer, quant_config=quant_config)
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)
EOF

# Convert AWQ to TRT-LLM
python3 /usr/local/lib/python3.10/dist-packages/tensorrt_llm/examples/llama/convert_checkpoint.py \
    --model_dir /workspace/models/llama-3.1-8b-awq-int4 \
    --output_dir /workspace/trt_checkpoints/llama-3.1-8b-int4 \
    --dtype float16 \
    --quant_ckpt_path /workspace/models/llama-3.1-8b-awq-int4 \
    --use_weight_only \
    --weight_only_precision int4_awq \
    --per_group

trtllm-build \
    --checkpoint_dir /workspace/trt_checkpoints/llama-3.1-8b-int4 \
    --output_dir /workspace/trt_engines/llama-3.1-8b-int4 \
    --gemm_plugin float16 \
    --max_batch_size 128 \
    --max_input_len 4096 \
    --max_seq_len 8192

Engine build time: 10–30 minutes depending on GPU and model size. This is a one-time operation — once built, the engine loads in seconds.

---

Step 6 — Quick Test with TRT-LLM Python API

Before setting up Triton, verify the engine works:

python3 << 'EOF'
import tensorrt_llm
from tensorrt_llm.runtime import ModelRunner
from transformers import AutoTokenizer

engine_dir = "/workspace/trt_engines/llama-3.1-8b-fp16"
tokenizer_dir = "/workspace/models/llama-3.1-8b"

tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
runner = ModelRunner.from_dir(
    engine_dir=engine_dir,
    rank=0
)

prompt = "What is the capital of France?"
input_ids = tokenizer.encode(prompt, return_tensors="pt")

output = runner.generate(
    batch_input_ids=[input_ids[0].tolist()],
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9
)

output_ids = output[0][0][len(input_ids[0]):]
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(f"Response: {response}")
EOF

---

Step 7 — Set Up Triton Inference Server

Create Model Repository Structure

mkdir -p /workspace/triton_model_repo/llama/1

# Create model configuration
cat > /workspace/triton_model_repo/llama/config.pbtxt << 'EOF'
backend: "tensorrtllm"
name: "llama"
max_batch_size: 64
model_transaction_policy {
  decoupled: true
}

dynamic_batching {
  preferred_batch_size: [1, 2, 4, 8, 16, 32, 64]
  max_queue_delay_microseconds: 1000
}

input [
  {
    name: "input_ids"
    data_type: TYPE_INT32
    dims: [-1]
  },
  {
    name: "input_lengths"
    data_type: TYPE_INT32
    dims: [1]
    reshape: { shape: [] }
  },
  {
    name: "request_output_len"
    data_type: TYPE_INT32
    dims: [1]
    reshape: { shape: [] }
  },
  {
    name: "temperature"
    data_type: TYPE_FP32
    dims: [1]
    reshape: { shape: [] }
    optional: true
  }
]

output [
  {
    name: "output_ids"
    data_type: TYPE_INT32
    dims: [-1, -1]
  },
  {
    name: "sequence_length"
    data_type: TYPE_INT32
    dims: [1]
  }
]

instance_group [
  {
    count: 1
    kind: KIND_GPU
    gpus: [0]
  }
]

parameters: {
  key: "gpt_model_type"
  value: { string_value: "inflight_fused_batching" }
}

parameters: {
  key: "gpt_model_path"
  value: { string_value: "/workspace/trt_engines/llama-3.1-8b-fp16" }
}

parameters: {
  key: "max_tokens_in_paged_kv_cache"
  value: { string_value: "8192" }
}

parameters: {
  key: "batch_scheduler_policy"
  value: { string_value: "guaranteed_no_evict" }
}
EOF

Create Engine Symlink

ln -s /workspace/trt_engines/llama-3.1-8b-fp16 \
    /workspace/triton_model_repo/llama/1/

Start Triton Server

tritonserver \
    --model-repository=/workspace/triton_model_repo \
    --http-port=8000 \
    --grpc-port=8001 \
    --metrics-port=8002 \
    --log-verbose=0 &

# Wait for server to start
sleep 30

# Check server health
curl -s http://localhost:8000/v2/health/ready

---

Step 8 — Query the API

OpenAI-Compatible Client

import requests
import json

def generate(prompt: str, max_tokens: int = 200) -> str:
    url = "http://localhost:8000/v2/models/llama/generate"
    
    payload = {
        "text_input": prompt,
        "parameters": {
            "max_tokens": max_tokens,
            "temperature": 0.7,
            "top_p": 0.9
        }
    }
    
    response = requests.post(url, json=payload)
    result = response.json()
    return result.get("text_output", "")

# Test
print(generate("Explain quantum computing in simple terms:"))

Benchmark Throughput

# Install tritonclient
pip install tritonclient[all]

# Run performance benchmark
perf_analyzer \
    -m llama \
    -u localhost:8001 \
    --protocol grpc \
    --input-data /workspace/sample_inputs.json \
    --concurrency-range 1:32:2 \
    --measurement-interval 10000 \
    --shape input_ids:512 \
    --shape input_lengths:1 \
    --shape request_output_len:1

---

Step 9 — Add OpenAI-Compatible API Wrapper

For easier integration, add a FastAPI wrapper:

pip install fastapi uvicorn tritonclient[all]

cat > /workspace/openai_server.py << 'EOF'
from fastapi import FastAPI
from pydantic import BaseModel
import tritonclient.http as httpclient
import numpy as np
from transformers import AutoTokenizer

app = FastAPI()
tokenizer = AutoTokenizer.from_pretrained("/workspace/models/llama-3.1-8b")
client = httpclient.InferenceServerClient("localhost:8000")

class ChatRequest(BaseModel):
    model: str = "llama"
    messages: list
    max_tokens: int = 512
    temperature: float = 0.7

@app.post("/v1/chat/completions")
async def chat(req: ChatRequest):
    prompt = tokenizer.apply_chat_template(
        req.messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    input_ids = tokenizer.encode(prompt)
    
    inputs = [
        httpclient.InferInput("input_ids", [len(input_ids)], "INT32"),
        httpclient.InferInput("input_lengths", [1], "INT32"),
        httpclient.InferInput("request_output_len", [1], "INT32"),
    ]
    inputs[0].set_data_from_numpy(np.array(input_ids, dtype=np.int32))
    inputs[1].set_data_from_numpy(np.array([len(input_ids)], dtype=np.int32))
    inputs[2].set_data_from_numpy(np.array([req.max_tokens], dtype=np.int32))
    
    result = client.infer("llama", inputs)
    output_ids = result.as_numpy("output_ids")[0][len(input_ids):]
    text = tokenizer.decode(output_ids, skip_special_tokens=True)
    
    return {
        "choices": [{"message": {"role": "assistant", "content": text}}]
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8080)
EOF

python3 /workspace/openai_server.py &

---

Troubleshooting

Engine Build OOM

# Reduce max_batch_size and max_num_tokens
trtllm-build \
    --checkpoint_dir /workspace/trt_checkpoints/llama-3.1-8b-fp16 \
    --output_dir /workspace/trt_engines/llama-3.1-8b-fp16 \
    --gemm_plugin float16 \
    --max_batch_size 8 \        # Reduce from 32
    --max_input_len 2048 \      # Reduce from 4096
    --max_seq_len 4096          # Reduce from 8192

Triton Server Not Starting

# Check logs
cat /workspace/triton.log

# Verify engine files exist
ls -la /workspace/trt_engines/llama-3.1-8b-fp16/

# Check GPU memory
nvidia-smi

Low Throughput

# Enable in-flight batching and increase concurrency
# Tune max_tokens_in_paged_kv_cache based on available VRAM

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Performance Benchmarks on Clore.ai GPUs

Model	GPU	Quantization	Throughput (tokens/sec)
Llama 3.1 8B	RTX 4090	FP16	~3,500
Llama 3.1 8B	RTX 4090	INT4 AWQ	~6,200
Llama 3.1 70B	2x A100 80G	FP16	~1,800
Mixtral 8x7B	2x RTX 4090	INT8	~2,400

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Additional Resources

• TensorRT-LLM GitHub

• Triton Inference Server

• NGC Container Registry

• TRT-LLM Documentation

• AWQ Quantization

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TensorRT-LLM on Clore.ai is the optimal choice for production LLM serving where throughput and latency are critical. For simpler setups, consider the vLLM guide.

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Clore.ai GPU Recommendations

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	$0.07–0.21/gpu/hr
Production Inference	RTX 4090 (24GB)	$0.14–0.42/gpu/hr
Large Models (70B+)	A100 80GB	bare metal

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.