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Framework Comparison & Selection Guide

Detailed Comparison Matrix

Feature vLLM TensorRT DeepSpeed Ollama PyTorch
─────────────────────────────────────────────────────────────────
Setup Complexity Easy Hard Hard Easy Easy
Performance 

Latency (ms) 70 40 70 150 200
Throughput (tok/s) 1200 1500 1200 300 100

Multi-GPU 
Distributed 
LoRA Support 
Quantization 

API Style Python Config Python CLI Python
OpenAI Compatible 
Docker 

Community Large Medium Large Growing Medium
Maturity High High High Medium High

Cost (for 100k tok) $0.30 $0.25 $0.30 $0.50 $1.00
(estimated GPU cost)

Scenario-Based Selection

Scenario 1: Fast Prototyping (Local Development)

Requirements:
 - Quick setup (< 5 minutes)
 - Single GPU
 - Local only
 - Not production

Best choice: Ollama

Why:
 - One-liner: ollama run llama2
 - Models auto-downloaded
 - Great for testing
 - Works on consumer GPU

Runner-up: PyTorch with Hugging Face
 - More control
 - Python API
 - Familiar to ML engineers

Code:
```bash
# Ollama
ollama run llama2

# PyTorch
from transformers import pipeline
pipe = pipeline("text-generation", model="llama2")
pipe("Hello world")

### Scenario 2: Production Web Service (Single GPU)

Requirements:

  • High throughput (1000+ tok/s)
  • Low latency (<100ms)
  • OpenAI API compatible
  • Easy deployment

Best choice: vLLM

Why:

  • Simple Python setup
  • OpenAI-compatible (drop-in)
  • Great performance
  • Docker-ready

Code:

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-2-7b-hf")
outputs = llm.generate(prompts, SamplingParams())

# Or via OpenAI API:
# python -m vllm.entrypoints.openai.api_server

Docker:

FROM nvidia/cuda:12.1-runtime
RUN pip install vllm
CMD ["python", "-m", "vllm.entrypoints.openai.api_server", \
 "--model", "meta-llama/Llama-2-7b-hf"]

### Scenario 3: Production, Multi-GPU Cluster

Requirements:

  • Maximum throughput
  • Distribute across GPUs
  • LoRA support
  • Production monitoring

Best choice: vLLM with multi-GPU

Why:

  • Tensor parallelism built-in
  • LoRA support
  • Mature, widely tested
  • Great community support

Code:

from vllm import LLM, SamplingParams

# 8 GPUs
llm = LLM(
 model="meta-llama/Llama-2-70b-hf",
 tensor_parallel_size=8,
)

outputs = llm.generate(prompts, SamplingParams())

Performance:

  • Throughput: 10K+ tokens/sec
  • Latency: 50-100ms
  • Concurrent users: 100-200
### Scenario 4: Ultra-Low Latency Required (<50ms)

Requirements:

  • Absolute lowest latency
  • Interactive experience critical
  • Willing to optimize more
  • Production grade

Best choice: TensorRT-LLM

Why:

  • Compiled kernels (fastest)
  • 30-50ms latency achievable
  • Proven in high-traffic systems
  • Best for real-time

Trade-offs: More complex setup Requires model compilation Less flexible than vLLM

Code:

# Compile model for TensorRT
trtllm-build \
 --checkpoint_dir./checkpoint/llama-7b \
 --output_dir./trt_engines/llama-7b

# Run inference
python run.py --model_path./trt_engines/llama-7b

### Scenario 5: CPU-Only or Edge Deployment

Requirements:

  • No GPU available
  • Mobile/edge device
  • Moderate latency OK
  • Memory constrained

Best choice: llama.cpp

Why:

  • CPU-optimized C++
  • Quantization (4-bit, 8-bit)
  • Tiny binary (small download)
  • No GPU needed

Alternative: ONNX Runtime

  • Cross-platform support
  • CPU/GPU flexible
  • Production-ready

Code:

# llama.cpp
./main -m model.gguf -n 128 -p "Hello world"

# Or Python:
from llama_cpp import Llama
llm = Llama(model_path="./model.gguf")
output = llm("Hello world", max_tokens=128)

---

## Decision Tree: Final Choice

Start: Which inference framework? │

  • Do you have GPUs? │ │
  • No → llama.cpp or ONNX (CPU-optimized) │ │
  • Yes → Continue below │
  • Is this production critical? │ │
  • No → Ollama (quick prototyping) │ │
  • Yes → Continue below │
  • Do you need <50ms latency? │ │
  • Yes → TensorRT-LLM (if you can handle complexity) │ │
  • No → Continue below │
  • Do you need multiple GPUs? │ │
  • Yes → vLLM (easiest distributed) or DeepSpeed │ │
  • No → vLLM (single GPU works great) │
  • Do you need LoRA support? │
  • Yes → vLLM (best support) │
  • No → Any of the above
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## Performance Benchmarks: Real Numbers

Benchmark Setup:

  • Model: LLaMA-7B
  • GPU: A100 (80GB)
  • Batch Size: 32
  • Max Sequence: 2048

Results (tokens/sec):

Framework FP16 INT8 INT4 ──────────────────────────────── PyTorch 100 150 200 Ollama 150 200 250 vLLM 1200 1500 1800 TensorRT-LLM 1500 2000 2200 DeepSpeed 1000 1300 1600

Latency (first token, empty queue):

Framework Latency ────────────────────── PyTorch 200ms Ollama 150ms vLLM 75ms TensorRT-LLM 40ms DeepSpeed 80ms

Memory Usage (MB):

Framework Batch 1 Batch 32 ──────────────────────────── PyTorch 20GB 23GB vLLM 14GB 18GB TensorRT-LLM 14GB 16GB DeepSpeed 14GB 19GB

Key insight: vLLM is sweet spot (great perf, easy setup)

---

## Migration Paths

### From PyTorch to vLLM

```python
# BEFORE
from transformers import pipeline
pipe = pipeline("text-generation", model="llama2")
output = pipe("Hello", max_new_tokens=50)

# AFTER
from vllm import LLM, SamplingParams
llm = LLM(model="llama2")
output = llm.generate(["Hello"], SamplingParams(max_tokens=50))

# Minimal code changes needed!

From vLLM to TensorRT (if needed)

# Export vLLM model to TensorRT
# 1. Export to ONNX
# 2. Compile with TensorRT
# 3. Run with TensorRT runtime

# Tool:
trtllm-build --checkpoint_dir <vllm_model>

-

Cost Analysis

Scenario: Serve 1M tokens per day

Framework GPU Cost Time per day Cost/day
───────────────────────────────────────────────
PyTorch $1000 40 hours $1.66
Ollama $1000 10 hours $0.42
vLLM $1000 1 hour $0.04
TensorRT $1000 45 min $0.03

Annual cost:
PyTorch: $606
Ollama: $153
vLLM: $15
TensorRT: $11

Lesson: Framework choice >> Hardware choice for costs!

Key Takeaways

vLLM: Best all-around (performance + simplicity) TensorRT: Best latency if setup complexity acceptable Ollama: Best for prototyping and learning llama.cpp: Best for CPU and edge Framework choice affects cost more than hardware!

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