The ML Model Deployment Hub serves as the architectural index and engineering reference for transitioning trained machine learning models, vision architectures, and large language models (LLMs) into resilient, low-latency, and cost-effective production serving systems.
Deploying modern machine learning systems requires balancing computational throughput, GPU VRAM constraints, latency percentiles (P95/P99), numerical quantization degradation, and distributed telemetry.
Production inference topologies vary based on latency budgets, statefulness, and throughput requirements:
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| ML INFERENCE SERVING TOPOLOGIES |
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| 1. Real-Time Synchronous (Low Latency < 50ms) |
| - gRPC / HTTP REST endpoints behind load balancers |
| - Triton Inference Server, TorchServe, ONNX Runtime |
| |
| 2. High-Throughput Token Streaming (LLM / Generative AI) |
| - Continuous Batching + Server-Sent Events (SSE) / WebSockets |
| - vLLM (PagedAttention), TensorRT-LLM, TGI (Text Generation Inference) |
| |
| 3. Asynchronous Batch / Queue-Based (High Latency Tolerance) |
| - Kafka / SQS buffer + Celery workers / Ray Serve |
| - Offline batch scoring (Apache Spark, Ray Core) |
| |
| 4. Embedded / Edge On-Device |
| - TensorRT, CoreML, TFLite, WebGPU (Wasm / WebLLM) |
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In autoregressive Large Language Model generation, memory bandwidth—rather than raw FLOP compute—is the primary bottleneck due to the dynamic growth of the Key-Value (KV) cache.
For a batch of B sequences of length L in a model with N_{\text{layers}} layers and hidden dimension D:
In traditional naive memory allocation, contiguous virtual memory buffers must be pre-allocated for maximum sequence lengths (L_{\max}), causing up to 60–80% internal and external memory fragmentation.
Traditional Contiguous Pre-allocation vs. PagedAttention Memory:
Traditional Naive Buffers (Fragmented):
Req 1: [ Tok1 | Tok2 | Tok3 | ... Unused Reserved Space (Wasted) ... ]
Req 2: [ Tok1 | Tok2 | ... Unused Reserved Space ... ]
PagedAttention (vLLM Non-Contiguous Page Table):
Logical Blocks: [ Block 0 ] -> [ Block 1 ] -> [ Block 2 ]
| | |
Physical Page Frame: ( Frame 7 ) ( Frame 2 ) ( Frame 9 )
PagedAttention (Kwon et al.) partitions the KV-cache into fixed-size physical memory blocks (typically 16 or 32 tokens per block). Physical pages are allocated dynamically on-demand as generation proceeds, eliminating internal fragmentation and enabling near-zero memory waste.
Furthermore, continuous dynamic batching (iteration-level scheduling) inserts newly arriving requests into the execution batch immediately at every token decoding step, raising GPU compute utilization to >90\%.
Quantization reduces the numerical bit-width of model weights and activation tensors, reducing memory bandwidth pressure and increasing tensor core throughput.
Quantization Spectrum:
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| Precision Format | Bits per Parameter| Memory per 7B Model| Hardware Support |
+-------------------+-------------------+--------------------+------------------------+
| FP32 (Full) | 32 bits (4 bytes) | ~28 GB VRAM | All CPUs / GPUs |
| FP16 / BF16 | 16 bits (2 bytes) | ~14 GB VRAM | NVIDIA Volta / Ampere+ |
| FP8 (E4M3 / E5M2) | 8 bits (1 byte) | ~7 GB VRAM | NVIDIA Hopper / Ada |
| INT8 (W8A8 / W8A16)| 8 bits (1 byte) | ~7 GB VRAM | Turing / Ampere / CPUs |
| INT4 (AWQ / GPTQ) | 4 bits (0.5 byte) | ~3.5 - 4.5 GB VRAM | Ampere / Ada / Hopper |
+-------------------+-------------------+--------------------+------------------------+
Mapping continuous real values x \in [\alpha, \beta] to discrete b-bit integer grid q \in [0, 2^b - 1]:
where S = \frac{\beta - \alpha}{2^b - 1} is the Scale Factor and Z = \left\lfloor -\frac{\alpha}{S} \right\rceil is the Zero-Point.
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| Serving Engine | Primary Use Case | Key Optimizations |
+---------------------------+-----------------------------------+------------------------+
| vLLM | Production LLM Serving | PagedAttention, chunked|
| | | prefill, continuous bat|
| NVIDIA TensorRT-LLM | Maximum Throughput on NVIDIA GPUs | In-flight batching, |
| | | FP8 kernel fusion |
| Triton Inference Server | Heterogeneous Multi-Model Serving | Dynamic batching, multi|
| | (PyTorch, ONNX, TensorRT, Python) | framework concurrent ex|
| ONNX Runtime | Cross-Platform CPU/GPU Inference | Graph optimization, |
| | | execution providers |
| TGI (Hugging Face) | Production Open-Source LLMs | Speculative decoding, |
| | | FlashAttention-2 |
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Production MLOps Pipeline:
[ Model Registry (MLflow/W&B) ] ---> [ CI/CD Validation & Quantization ]
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v
[ Canary / Shadow Deployment (Istio/Envoy) ] <--- [ Triton / vLLM Cluster ]
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v v
[ Prometheus / Grafana ] [ Drift Detection Engine (Evidently/Alibi) ]
- GPU Duty Cycle / VRAM - Population Stability Index (PSI)
- Time to First Token (TTFT)- Kolmogorov-Smirnov (KS) Test
- Token Generation Latency - Feature Embedding Cosine Drift
\text{PSI} < 0.1: No significant shift; \text{PSI} > 0.25: Actionable drift requiring automated retraining.