Production LLM inference is a balancing act. Throughput, measured in tokens per second across all users, must climb while latency, the time to first token and inter-token latency for an individual user, must fall. Achieving both requires moving beyond naive single-request serving to a stack of software and hardware optimizations. The techniques below are what separate prototype APIs from infrastructure that can serve millions of daily requests at scale.
Batching Strategies
The simplest way to improve GPU utilization is to process multiple requests together. Static batching groups a fixed set of prompts, but it is inefficient because sequences complete at different lengths. The entire batch must wait for the longest generation, leaving compute units idle.
Dynamic batching improves on this by grouping requests that arrive within a short window. While better, it still suffers from the same tail-latency problem.
Continuous batching, also called in-flight batching, solves this at the iteration level. Instead of waiting for every sequence in a batch to finish, the scheduler swaps out completed sequences and swaps in new requests after every forward pass. This keeps the GPU saturated and is now standard in production engines like vLLM and TGI.
KV Cache Management and PagedAttention
For autoregressive transformers, the key-value cache is the dominant memory consumer. A naive implementation preallocates a contiguous buffer sized to the model's maximum context length for every request. This wastes memory on short prompts and creates internal fragmentation, limiting batch size.
PagedAttention, introduced by vLLM, treats the KV cache like an operating system's virtual memory. The cache is divided into fixed-size blocks that are allocated non-contiguously and mapped via a block table. When a sequence generates a new token, it only needs a new block, not a full reallocation. This allows significantly higher batch sizes and throughput on the same hardware.
Quantization and Compression
Quantization reduces the precision of weights and activations, shrinking model size and accelerating matrix multiplications. INT8 quantization typically recovers near-full accuracy with a 2x memory reduction. More aggressive schemes like GPTQ, AWQ, and GGUF push weights to INT4, enabling large models to fit on fewer GPUs.
The tradeoff is accuracy versus latency. INT4 can introduce perplexity degradation for reasoning-heavy models. For production APIs, INT8 or FP8 mixed precision often offers the best balance on modern NVIDIA hardware with dedicated tensor cores for low-precision math.
Speculative Decoding
Speculative decoding reduces per-request latency by using a small draft model to generate candidate tokens, which a larger target model then verifies in parallel. If the draft is accurate, multiple tokens are accepted per forward pass of the large model. This is especially effective for code generation and structured outputs where local patterns are predictable.
The overhead is memory, you must host both models, and the draft model must share the target model's tokenizer. When throughput headroom exists, speculative decoding can cut time-to-final-token without sacrificing accuracy.
Continuous Batching and Scheduling
Modern inference engines use iteration-level scheduling to maximize throughput. Beyond simple continuous batching, advanced schedulers implement prefix caching. When multiple users share a system prompt or when an agent performs multi-turn reasoning, the precomputed key-value states for the shared prefix are stored and reused. This avoids redundant computation and dramatically reduces time-to-first-token for long conversations.
Function calling and JSON mode, features common in agentic workloads, also benefit from optimized scheduling. Constrained decoding can be fused into the sampling loop so that token generation adheres to a grammar or schema without expensive post-hoc filtering.
Model Parallelism and Tensor Sharding
When a model exceeds the memory of a single GPU, parallelism strategies become mandatory. Tensor parallelism splits individual layers across devices, requiring high-bandwidth interconnects like NVLink to keep latency low. Pipeline parallelism assigns contiguous layers to different GPUs, but introduces bubble overhead unless microbatching is tuned carefully.
For Mixture-of-Experts architectures like DeepSeek R1 671B MoE or GLM 5, expert parallelism routes tokens to specific GPU workers. Efficient all-to-all communication patterns determine whether an MoE model achieves its theoretical throughput or becomes network-bound.
Platform Considerations for Production Workloads
Implementing these optimizations in-house requires maintaining custom CUDA kernels, scheduling logic, and multi-node orchestration. Most engineering teams will see better ROI by routing inference to a managed platform that already implements continuous batching, PagedAttention, and quantization.
Oxlo.ai is a developer-first AI inference platform built for these production constraints. It offers fully OpenAI SDK compatible APIs, so switching requires only a base URL change.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-r1-671b",
messages=[{"role": "user", "content": "Explain speculative decoding"}],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content, end="")
Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, Oxlo.ai uses request-based pricing. You pay one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this model eliminates the cost explosion tied to input tokens and can be significantly cheaper. Oxlo.ai also offers no cold starts on popular models, streaming responses, function calling, JSON mode, and vision support across 45+ open-source and proprietary models.
Pricing is transparent. The Free plan includes 60 requests per day and access to 16+ free models, while paid tiers scale to dedicated GPU clusters for Enterprise workloads. For exact rates, see the Oxlo.ai pricing page.
Conclusion
High-throughput, low-latency inference is not the result of any single trick. It is a stack of batching, memory management, quantization, parallelism, and scheduling optimizations. For teams running production chat, coding agents, or vision pipelines, building this stack from scratch diverts engineering resources from product development.
Oxlo.ai provides an optimized inference backend with predictable request-based pricing, OpenAI SDK compatibility, and a broad model catalog including Llama 3.3 70B, Qwen 3 32B, DeepSeek V4 Flash, and Kimi K2.6. If your workloads are growing in context length or complexity, it is worth evaluating a platform that aligns cost with requests rather than tokens.
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