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refactor: remove outdated skills and references from MLOps
- Deleted the `huggingface-accelerate` skill documentation, which included details on distributed training and common workflows. - Removed `custom-plugins.md`, `megatron-integration.md`, `performance.md`, and other related reference documents that were no longer relevant or necessary. - This cleanup aims to streamline the MLOps skills repository and improve maintainability.
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# TensorRT-LLM Optimization Guide
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Comprehensive guide to optimizing LLM inference with TensorRT-LLM.
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## Quantization
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### FP8 Quantization (Recommended for H100)
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**Benefits**:
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- 2× faster inference
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- 50% memory reduction
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- Minimal accuracy loss (<1% perplexity degradation)
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**Usage**:
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```python
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from tensorrt_llm import LLM
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# Automatic FP8 quantization
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llm = LLM(
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model="meta-llama/Meta-Llama-3-70B",
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dtype="fp8",
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quantization="fp8"
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)
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```
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**Performance** (Llama 3-70B on 8× H100):
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- FP16: 5,000 tokens/sec
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- FP8: **10,000 tokens/sec** (2× speedup)
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- Memory: 140GB → 70GB
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### INT4 Quantization (Maximum compression)
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**Benefits**:
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- 4× memory reduction
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- 3-4× faster inference
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- Fits larger models on same hardware
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**Usage**:
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```python
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# INT4 with AWQ calibration
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llm = LLM(
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model="meta-llama/Meta-Llama-3-405B",
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dtype="int4_awq",
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quantization="awq"
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)
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# INT4 with GPTQ calibration
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llm = LLM(
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model="meta-llama/Meta-Llama-3-405B",
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dtype="int4_gptq",
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quantization="gptq"
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)
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```
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**Trade-offs**:
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- Accuracy: 1-3% perplexity increase
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- Speed: 3-4× faster than FP16
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- Use case: When memory is critical
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## In-Flight Batching
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**What it does**: Dynamically batches requests during generation instead of waiting for all sequences to finish.
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**Configuration**:
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```python
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# Server configuration
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trtllm-serve meta-llama/Meta-Llama-3-8B \
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--max_batch_size 256 \ # Maximum concurrent sequences
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--max_num_tokens 4096 \ # Total tokens in batch
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--enable_chunked_context \ # Split long prompts
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--scheduler_policy max_utilization
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```
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**Performance**:
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- Throughput: **4-8× higher** vs static batching
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- Latency: Lower P50/P99 for mixed workloads
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- GPU utilization: 80-95% vs 40-60%
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## Paged KV Cache
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**What it does**: Manages KV cache memory like OS manages virtual memory (paging).
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**Benefits**:
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- 40-60% higher throughput
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- No memory fragmentation
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- Supports longer sequences
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**Configuration**:
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```python
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# Automatic paged KV cache (default)
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llm = LLM(
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model="meta-llama/Meta-Llama-3-8B",
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kv_cache_free_gpu_mem_fraction=0.9, # Use 90% GPU mem for cache
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enable_prefix_caching=True # Cache common prefixes
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)
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```
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## Speculative Decoding
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**What it does**: Uses small draft model to predict multiple tokens, verified by target model in parallel.
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**Speedup**: 2-3× faster for long generations
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**Usage**:
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```python
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from tensorrt_llm import LLM
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# Target model (Llama 3-70B)
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llm = LLM(
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model="meta-llama/Meta-Llama-3-70B",
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speculative_model="meta-llama/Meta-Llama-3-8B", # Draft model
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num_speculative_tokens=5 # Tokens to predict ahead
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)
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# Same API, 2-3× faster
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outputs = llm.generate(prompts)
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```
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**Best models for drafting**:
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- Target: Llama 3-70B → Draft: Llama 3-8B
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- Target: Qwen2-72B → Draft: Qwen2-7B
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- Same family, 8-10× smaller
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## CUDA Graphs
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**What it does**: Reduces kernel launch overhead by recording GPU operations.
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**Benefits**:
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- 10-20% lower latency
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- More stable P99 latency
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- Better for small batch sizes
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**Configuration** (automatic by default):
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```python
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llm = LLM(
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model="meta-llama/Meta-Llama-3-8B",
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enable_cuda_graph=True, # Default: True
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cuda_graph_cache_size=2 # Cache 2 graph variants
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)
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```
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## Chunked Context
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**What it does**: Splits long prompts into chunks to reduce memory spikes.
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**Use case**: Prompts >8K tokens with limited GPU memory
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**Configuration**:
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```bash
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trtllm-serve meta-llama/Meta-Llama-3-8B \
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--max_num_tokens 4096 \
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--enable_chunked_context \
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--max_chunked_prefill_length 2048 # Process 2K tokens at a time
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```
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## Overlap Scheduling
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**What it does**: Overlaps compute and memory operations.
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**Benefits**:
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- 15-25% higher throughput
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- Better GPU utilization
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- Default in v1.2.0+
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**No configuration needed** - enabled automatically.
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## Quantization Comparison Table
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| Method | Memory | Speed | Accuracy | Use Case |
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|--------|--------|-------|----------|----------|
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| FP16 | 1× (baseline) | 1× | Best | High accuracy needed |
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| FP8 | 0.5× | 2× | -0.5% ppl | **H100 default** |
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| INT4 AWQ | 0.25× | 3-4× | -1.5% ppl | Memory critical |
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| INT4 GPTQ | 0.25× | 3-4× | -2% ppl | Maximum speed |
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## Tuning Workflow
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1. **Start with defaults**:
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```python
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llm = LLM(model="meta-llama/Meta-Llama-3-70B")
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```
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2. **Enable FP8** (if H100):
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```python
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llm = LLM(model="...", dtype="fp8")
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```
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3. **Tune batch size**:
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```python
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# Increase until OOM, then reduce 20%
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trtllm-serve ... --max_batch_size 256
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```
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4. **Enable chunked context** (if long prompts):
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```bash
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--enable_chunked_context --max_chunked_prefill_length 2048
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```
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5. **Try speculative decoding** (if latency critical):
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```python
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llm = LLM(model="...", speculative_model="...")
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```
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## Benchmarking
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```bash
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# Install benchmark tool
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pip install tensorrt_llm[benchmark]
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# Run benchmark
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python benchmarks/python/benchmark.py \
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--model meta-llama/Meta-Llama-3-8B \
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--batch_size 64 \
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--input_len 128 \
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--output_len 256 \
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--dtype fp8
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```
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**Metrics to track**:
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- Throughput (tokens/sec)
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- Latency P50/P90/P99 (ms)
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- GPU memory usage (GB)
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- GPU utilization (%)
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## Common Issues
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**OOM errors**:
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- Reduce `max_batch_size`
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- Reduce `max_num_tokens`
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- Enable INT4 quantization
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- Increase `tensor_parallel_size`
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**Low throughput**:
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- Increase `max_batch_size`
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- Enable in-flight batching
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- Verify CUDA graphs enabled
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- Check GPU utilization
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**High latency**:
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- Try speculative decoding
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- Reduce `max_batch_size` (less queueing)
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- Use FP8 instead of FP16
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