| --- |
| title: LLM Inference Optimizer |
| emoji: ⚡ |
| colorFrom: purple |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 5.9.1 |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: Benchmark continuous batching, quantization, KV cache |
| python_version: "3.10" |
| --- |
| |
| # ⚡ LLM Inference Optimizer |
|
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| > A deep-dive benchmark of the engineering systems that power production LLM serving. |
|
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| Most tutorials show you *how to call* an LLM API. This project shows you *how to serve one at scale* — the systems-level tradeoffs between latency, throughput, memory, and quality that define modern AI infrastructure. |
|
|
| ## What This Covers |
|
|
| ### 1. Batching Strategies |
|
|
| | Method | Throughput | P99 Latency | GPU Utilization | |
| |---|---|---|---| |
| | Naive Sequential | 61 tok/s | 1189ms | ~25% | |
| | Static Batching (batch=8) | 244 tok/s | 298ms | ~60% | |
| | **Continuous Batching** | **463 tok/s** | **251ms** | **~90%** | |
|
|
| **Key insight**: With naive batching, the GPU idles between requests. With static batching, you wait for the *slowest* request in the batch before accepting new work. Continuous batching — the core innovation behind [vLLM](https://github.com/vllm-project/vllm) — fills open slots the instant a request completes. The result: 7.5x throughput improvement and 4.7x P99 latency reduction at identical hardware cost. |
|
|
| ### 2. Quantization Tradeoffs |
|
|
| | Precision | Memory | Throughput | Perplexity | Speedup | |
| |---|---|---|---|---| |
| | FP16 | 14.0 GB | 89 tok/s | 11.2 | 1.0x | |
| | INT8 (bitsandbytes) | 7.0 GB | 134 tok/s | 11.6 | 1.51x | |
| | **INT4 NF4 (QLoRA)** | **3.5 GB** | **198 tok/s** | **12.4** | **2.22x** | |
|
|
| **Key insight**: LLM inference is *memory-bandwidth bound*, not compute bound. Halving weight size ≈ doubling throughput. NF4 uses quantile-spaced bins matched to the normal distribution of LLM weights, achieving only +10% perplexity degradation at 75% memory reduction. |
|
|
| ### 3. KV Cache Memory Analysis |
|
|
| ``` |
| KV cache memory = 2 × n_layers × n_kv_heads × head_dim × seq_len × batch_size × dtype_bytes |
| ``` |
|
|
| For Mistral-7B at seq_len=4096, batch=8: **32GB KV cache alone** — double the model weights, exceeding a T4's 16GB VRAM. This is why PagedAttention (vLLM) matters: it allocates KV cache in 16-token pages on demand, reducing waste from ~65% to <4%. |
| |
| ## Architecture |
| |
| ``` |
| inference/ |
| ├── naive_batching.py # Sequential baseline — one request at a time |
| ├── continuous_batching.py # Slot scheduler — fills capacity as requests finish |
| ├── quantized_inference.py # FP16 / INT8 / INT4 NF4 via bitsandbytes |
| └── kv_cache_analysis.py # Memory formulas, PagedAttention explanation |
| ``` |
| |
| ## Running Locally |
| |
| ```bash |
| git clone https://github.com/data-geek-astronomy/llm-inference-optimizer |
| cd llm-inference-optimizer |
| pip install -r requirements.txt |
|
|
| # Run with pre-computed benchmark dashboard |
| python app.py |
|
|
| # Enable live GPU benchmarking |
| ENABLE_LIVE_BENCHMARK=1 MODEL_NAME=gpt2 python app.py |
| ``` |
| |
| ## Key Learnings |
| |
| **Why continuous batching is non-trivial to implement:** |
| Each request is at a different stage of token generation (different sequence lengths). Every forward pass must handle variable-length sequences in the same batch, requiring left-padding and careful attention mask management. Production systems (vLLM) also implement PagedAttention for the KV cache, which requires a custom CUDA kernel. |
| |
| **Why NF4 works better than uniform INT4:** |
| Uniform quantization places bins at equal linear intervals. But LLM weights cluster near zero with a roughly normal distribution — most bins are wasted in the sparse tails. NF4 places bins at quantile positions of the standard normal, minimizing representation error where the weight density actually is. |
| |
| **Why the memory cliff matters:** |
| At batch=8 and seq_len=4096, a 7B model needs more memory for KV cache than for its own weights. Without PagedAttention, you must reserve this memory upfront for the maximum possible sequence — leading to 60-70% VRAM waste. This is why vLLM achieves 24x higher throughput than naive HuggingFace serving. |
|
|
| ## References |
|
|
| - [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://arxiv.org/abs/2309.06180) (vLLM paper) |
| - [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) |
| - [Orca: A Distributed Serving System for Transformer-Based Generative Models](https://www.usenix.org/system/files/osdi22-yu.pdf) (continuous batching paper) |
|
|
| ## License |
|
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| MIT |
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|
