Long-Context LLM Infrastructure: Building Systems for Million-Token Windows

Updated December 8, 2025

December 2025 Update: Million-token context windows now production-ready with Gemini 2.5 Pro (2M tokens), Claude Sonnet 4 (1M tokens), and open-source Qwen2.5-1M. KV cache for 1M tokens requires ~15GB per user—manageable with modern infrastructure. Context parallelism achieving 93% efficiency on 128 H100s for 405B models. NVFP4 KV cache quantization halving memory requirements. "Lost in the middle" phenomenon showing 40% context degradation at scale. Prefill latency exceeding 2 minutes for maximum context lengths driving architectural tradeoffs.

Google's Gemini 2.5 Pro processes 2 million tokens in a single context window—the equivalent of reading 30 novels, watching 12 hours of video, or analyzing an entire codebase in one prompt.¹ Claude Sonnet 4 expanded to 1 million tokens. Open-source alternatives including Qwen2.5-1M and MiniMax-M1 match these capabilities without API dependencies.² The infrastructure enabling these advances represents a fundamental departure from systems designed for 4,000-token contexts. Organizations deploying long-context models discover that memory requirements, latency characteristics, and cost structures scale non-linearly with context length. Building production systems for million-token windows requires understanding the unique infrastructure challenges these capabilities introduce.

The architecture of long-context processing

Long-context LLMs transform how models interact with information, but the underlying infrastructure challenges scale dramatically with context length.

Attention complexity creates the fundamental bottleneck. Standard transformer attention scales quadratically with sequence length—O(n²) complexity means doubling tokens quadruples computation and memory.³ Flash Attention and similar optimizations reduce practical scaling to O(n), but even linear scaling becomes expensive at million-token scale. Processing 1 million tokens requires 250x the attention computation of 4,000-token contexts, creating infrastructure demands that compound across every layer of the model.

KV cache growth dominates memory requirements during inference. For each token in context, the model stores key-value pairs used for subsequent attention calculations. The cache grows linearly with context length, quickly consuming available GPU memory. A 70B parameter model with 128K token context requires approximately 40GB of KV cache per user—exceeding single-GPU capacity before accounting for model weights.⁴

Prefill latency determines time-to-first-token for long inputs. Before generating output, models must process the entire input context. At maximum context lengths, prefill exceeds 2 minutes even on optimized infrastructure.⁵ Applications requiring interactive response times cannot simply extend context windows without considering the user experience implications of extended initial delays.

Position encoding limitations required architectural innovations to extend beyond original training lengths. Techniques like LongRoPE dynamically adjust rotary position embeddings to extrapolate beyond 2 million tokens.⁶ Qwen2.5-1M employs Adaptive Base Frequency adjustments and Dual Chunk Attention to maintain coherence across ultra-long sequences.⁷ These techniques enable longer contexts without retraining from scratch, but introduce complexity into model serving infrastructure.

Memory requirements for million-token contexts

Understanding precise memory requirements enables infrastructure planning for long-context deployments.

KV cache sizing follows a predictable formula: 2 × 2 × head_dim × n_heads × n_layers × max_context_length × batch_size. For practical reference, a 1,000,000-token KV cache requires approximately 15GB.⁸ The NVIDIA DGX platform supports 4TB of system memory with 1TB/s aggregate bandwidth to GPUs, enabling KV cache transfers from CPU memory in 15ms compared to 2+ minute recomputation.

Model-specific requirements vary substantially:

Model Size	2K Context	32K Context	128K Context
4B parameters	~0.2GB	~3GB	~12GB
8B parameters	~0.3GB	~5GB	~20GB
70B parameters	~1.6GB	~27GB	~42GB

These figures represent KV cache alone. Adding model weights (140GB for Llama 3 70B in FP16) and activation memory makes 70B models with 128K+ context impossible to run on single GPUs.⁹

Batch size multiplication compounds memory pressure. Serving 8 concurrent users with Llama-2-13B at 4,096 tokens requires 25GB of KV cache—nearly matching the 26GB for model weights.¹⁰ Long-context applications serving multiple concurrent users face multiplicative memory demands that rapidly exhaust even multi-GPU configurations.

Memory architecture options include:

• Pure GPU deployment: Maximum performance but limited by HBM capacity (80GB per H100)
• CPU offloading: Trade latency for capacity, transferring KV cache as needed
• Unified memory architectures: Grace Hopper and Grace Blackwell systems with high-bandwidth CPU-GPU interconnects optimized for this pattern
• Disaggregated serving: Separate prefill and decode phases across specialized infrastructure

KV cache optimization techniques

Infrastructure optimization for long-context serving focuses heavily on KV cache management.

PagedAttention treats KV cache like virtual memory, allocating non-contiguous blocks rather than requiring contiguous reservation for maximum possible context.¹¹ The technique dramatically reduces memory fragmentation, enabling higher concurrent request counts on fixed GPU memory. vLLM and TensorRT-LLM both implement PagedAttention as foundational capability.

KV cache quantization reduces precision of cached values. NVFP4 quantization cuts KV cache memory footprint by 50% compared to FP8, effectively doubling achievable context length or batch size with less than 1% accuracy loss on benchmarks.¹² The technique unlocks longer sequences without hardware upgrades.

KV cache offloading moves inactive cache to CPU memory or disk, loading on demand for subsequent tokens.¹³ The approach delivers up to 14x faster time-to-first-token for large inputs compared to recomputing KV cache from scratch. NVIDIA's Dynamo library and inference frameworks like vLLM implement intelligent offloading strategies.

Prefix caching shares KV cache across requests with common prefixes.¹⁴ System prompts, few-shot examples, and common document preambles can be computed once and reused across requests. LMCache combined with vLLM achieves 3-10x latency reductions for workloads with prefix sharing opportunities.

Compression techniques reduce KV cache size through learned compression, attention sink tokens, and dynamic eviction of less-important cached values.¹⁵ These methods trade accuracy for capacity, requiring application-specific evaluation of acceptable degradation.

Context parallelism for scale-out serving

Single-GPU approaches cannot handle million-token contexts for large models. Distributed approaches parallelize context processing across multiple devices.

Context parallelism (CP) partitions input sequences across GPUs rather than splitting model weights.¹⁶ Each GPU processes a portion of the sequence, with communication required for attention operations spanning partitions. The approach directly addresses the primary long-context bottleneck: memory pressure from extended sequences.

Ring Attention implements context parallelism through coordinated communication patterns. Query, Key, and Value tensors are segmented across GPUs, with each device computing partial attention results that are aggregated across the ring.¹⁷ Pass-KV and Pass-Q variants optimize for different workload characteristics—prefill versus decode, persistent versus transient caches.

Research results demonstrate the approach's effectiveness. Context parallelism achieves near-linear scaling for million-token prefill latency with up to 128 H100 GPUs across 16 nodes.¹⁸ Llama 3 405B processes 1 million token contexts in 77 seconds with 93% parallelization efficiency and 63% FLOPS utilization. 128K context prefill completes in 3.8 seconds on this configuration.

Unified Sequence Parallelism (USP) combines strengths of DeepSpeed-Ulysses-Attention and Ring-Attention. Pure Ring Attention suffers efficiency penalties from segmented FlashAttention processing. Hybrid approaches achieve better performance by using collective communication patterns where appropriate.¹⁹

Practical scaling guidance: With context parallel degree n, expect roughly 1/n memory reduction for attention operations. Degree-4 parallelism enables sequences 3-4x longer than single-GPU limits.²⁰ However, communication overhead prevents linear scaling—system design must balance parallelism degree against efficiency losses.

The lost-in-the-middle challenge

Long-context models exhibit systematic failures that infrastructure alone cannot solve but must accommodate.

U-shaped attention patterns cause models to attend more reliably to content at context beginnings and endings while "losing" information in the middle.²¹ Empirical studies show the effect peaks when inputs consume approximately 50% of model capacity. Beyond that threshold, bias shifts toward only recent content.

Recall degradation at scale affects even frontier models. While Gemini 1.5 maintains recall capabilities up to 1 million tokens, average recall hovers around 60%—meaning 40% of relevant context facts are effectively lost to the model.²² This fundamental limitation affects what long-context applications can reliably accomplish.

Gemini-specific issues demonstrate the pattern. After approximately 20% context window utilization, Gemini 2.5 Flash exhibits contextual memory degradation, confusing past information with current state.²³ The model takes actions based on outdated information, contradicting previous instructions and breaking task continuity.

Infrastructure implications include:

• Strategic context organization: Place critical information at context boundaries rather than middle
• Redundant retrieval: Repeat important facts multiple times throughout context
• Hybrid RAG approaches: Combine long context with retrieval to ensure critical information surfaces
• Chunked processing: Process very long inputs in stages with summarization rather than relying on pure long context

The lost-in-the-middle phenomenon means organizations cannot assume that "more context is better." Infrastructure investments enabling million-token contexts may deliver diminishing returns if model architecture cannot reliably utilize all provided context.

Training infrastructure for extended context

Training models with extended context lengths introduces challenges beyond inference serving.

Activation memory explosion grows with sequence length and model depth, quickly exceeding GPU capacity.²⁴ NVIDIA NeMo Framework addresses this through activation recomputation—trading compute for memory by regenerating activations during backward passes rather than storing them.

Context parallelism for training distributes sequences across devices during both forward and backward passes. AWS Neuron and NeMo implement CP for training, partitioning input tensors along the sequence dimension.²⁵ The technique enables training sequence lengths impossible on individual GPUs.

Staged training approaches avoid the computational burden of training directly on million-token sequences. Qwen2.5-1M follows a five-stage expansion strategy, gradually increasing context length through training phases.²⁶ Each stage adapts the model to longer contexts while limiting per-stage compute requirements.

Activation offloading extends GPU memory capacity by streaming activations to CPU memory or NVMe storage during forward passes, reloading during backward passes.²⁷ The technique trades training speed for ability to train with longer contexts on fixed hardware.

Organizations planning custom long-context training should budget 3-5x the GPU hours of standard training for equivalent model sizes, accounting for efficiency losses from parallelism overhead and memory management.

Provider comparison for long-context deployment

Different providers offer distinct long-context capabilities with varying infrastructure implications.

Google Gemini leads context length with 2 million tokens available in Gemini 2.5 Pro.²⁸ The model uses Mixture-of-Experts with sparse expert activation to reduce compute costs for extended contexts. Gemini's approach combines joint vision-language embeddings with retrieval-augmented compression, enabling multimodal long-context processing.

Anthropic Claude offers 1 million tokens in Claude Sonnet 4, with Claude Enterprise plans providing 500K tokens.²⁹ Claude emphasizes precision and alignment over extreme scalability, with reported stronger resistance to lost-in-the-middle effects than some competitors.

Open-source options include Qwen2.5-1M and MiniMax-M1 at 1 million tokens.³⁰ Gradient extended Llama 3 to 1 million tokens using Berkeley AI Research distributed attention techniques.³¹ Self-hosted deployment of these models requires infrastructure investment but eliminates API dependency and per-token costs.

Infrastructure provider comparison:

Provider	Max Context	Architecture	Self-Host Option
Gemini 2.5 Pro	2M tokens	MoE + retrieval fusion	No
Claude Sonnet 4	1M tokens	Dense transformer	No
Qwen2.5-1M	1M tokens	Dense + DCA	Yes
MiniMax-M1	1M tokens	MoE	Yes
Gradient Llama	1M tokens	Dense + distributed attention	Yes

Cost and latency tradeoffs

Long-context capabilities introduce cost structures fundamentally different from standard inference.

Per-token costs increase with context length due to attention computation, memory requirements, and infrastructure overhead.³² Processing 1 million tokens with Gemini 2.5 Pro costs substantially more than equivalent tokens with smaller context models. The relationship is non-linear—longer contexts are disproportionately expensive.

Prefill latency dominates user experience for long inputs. Small contexts complete prefill in under one second up to 10,000 tokens. Maximum context lengths require 2+ minutes before generation begins.³³ Interactive applications cannot tolerate these delays; batch processing or asynchronous workflows better match long-context latency characteristics.

Accessibility gaps emerge between resource-rich enterprises and smaller organizations. The computational costs of processing large contexts create barriers independent of model access.³⁴ Organizations must evaluate whether their use cases genuinely require million-token contexts or whether clever prompting, RAG, or summarization better serves their needs.

Economic breakeven analysis should compare: - Cost of long-context processing for complete documents - Cost of chunked processing with overlap - Cost of RAG retrieval with shorter context - Engineering cost of implementing chunking/RAG versus simple long-context

Often, hybrid approaches combining modest context lengths (32K-128K) with intelligent retrieval outperform pure long-context approaches on both cost and accuracy dimensions.

Production deployment patterns

Deploying long-context models in production requires patterns distinct from standard LLM serving.

Tiered context handling routes requests based on input length. Short contexts serve from standard infrastructure optimized for low latency. Medium contexts (32K-128K) use configurations balancing capacity and performance. Long contexts (128K+) route to specialized infrastructure accepting higher latency for capacity.

Prefill-decode separation runs the compute-intensive prefill phase on different infrastructure than token-by-token decode.³⁵ Prefill benefits from high-throughput batch processing; decode requires low-latency, sequential generation. Disaggregation optimizes each phase independently.

Asynchronous processing handles requests exceeding interactive latency budgets. Submit long-context jobs to queue-based systems returning results when ready rather than blocking on synchronous API calls. The pattern matches user expectations for document-scale processing.

Caching strategies become essential for workloads with repeated context patterns. Cache computed KV states for system prompts, reference documents, and common prefixes. Aggressive caching dramatically reduces effective cost for repetitive workloads.

Graceful degradation handles context lengths approaching infrastructure limits. Rather than failing on excessive input, truncate strategically (preserving ends over middle), summarize programmatically, or switch to RAG-based approaches automatically.

Building long-context infrastructure

Organizations preparing for long-context deployments should plan infrastructure across several dimensions.

GPU selection favors high-memory configurations. H100 and H200 with 80GB HBM enable larger contexts per GPU than 40GB alternatives. Grace Hopper's unified memory architecture particularly suits long-context workloads with its high-bandwidth CPU-GPU interconnect.

Networking requirements increase with context parallelism. NVLink within nodes and InfiniBand across nodes enable the communication patterns context parallelism requires. Bandwidth-constrained infrastructure limits achievable parallelism degree.

Memory hierarchy design should plan for KV cache offloading. High-capacity CPU memory (the DGX example supports 4TB) with high-bandwidth interconnects enables efficient cache migration. NVMe storage provides additional capacity tier for massive contexts.

Serving framework selection matters significantly. vLLM and TensorRT-LLM both offer long-context optimizations, but capabilities evolve rapidly. Evaluate frameworks against specific context length targets and model choices.

Introl's infrastructure deployment expertise across our global coverage area includes designing systems for long-context workloads. The intersection of memory architecture, networking, and serving optimization requires integrated planning that benefits from deployment experience.

The long-context infrastructure horizon

Long-context capabilities will continue expanding beyond current million-token limits. Experimental systems already demonstrate 100 million token contexts in research settings.³⁶ Infrastructure planning should anticipate continued scaling.

Hardware evolution favors long-context workloads. Each GPU generation improves memory capacity and bandwidth. Unified memory architectures reduce the penalty for cache offloading. The infrastructure challenges that seem acute today will moderate as hardware advances.

Algorithmic improvements will reduce per-token costs for extended contexts. Sparse attention patterns, linear attention variants, and improved compression techniques continue emerging from research. Production infrastructure should remain adaptable to incorporate these advances.

Application patterns will mature as organizations learn which use cases genuinely benefit from million-token contexts versus alternatives. The current enthusiasm for maximum context lengths will likely stabilize into nuanced understanding of when long context adds value versus when other approaches perform better.

Building infrastructure for long-context LLMs requires substantial investment but positions organizations for capabilities that will define next-generation AI applications. Document understanding, code analysis, video processing, and conversational agents all benefit from expanded context. Organizations that master long-context infrastructure gain competitive advantages as these applications mature from research curiosities to production requirements.

References

1. Codingscape. "LLMs with largest context windows." Codingscape Blog, 2025. https://codingscape.com/blog/llms-with-largest-context-windows

2. VentureBeat. "MiniMax-M1 is a new open source model with 1 MILLION TOKEN context and new, hyper efficient reinforcement learning." VentureBeat, 2025. https://venturebeat.com/ai/minimax-m1-is-a-new-open-source-model-with-1-million-token-context-and-new-hyper-efficient-reinforcement-learning

3. NVIDIA. "Scaling to Millions of Tokens with Efficient Long-Context LLM Training." NVIDIA Technical Blog, 2024. https://developer.nvidia.com/blog/scaling-to-millions-of-tokens-with-efficient-long-context-llm-training/

4. BentoML. "KV cache offloading." LLM Inference Handbook, 2024. https://bentoml.com/llm/inference-optimization/kv-cache-offloading

5. Micron. "1 million token context: The good, the bad and the ugly." Micron Insights, 2024. https://www.micron.com/about/blog/company/insights/1-million-token-context-the-good-the-bad-and-the-ugly

6. ArXiv. "LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens." ArXiv, February 2024. https://arxiv.org/abs/2402.13753

7. Ajith Prabhakar. "Qwen2.5-1M: The First Open-Source AI Model with a 1 Million Token Context Window." Ajith Vallath Prabhakar, February 2025. https://ajithp.com/2025/02/02/qwen2-5-1m-open-source-ai-1-million-token-context/

8. Micron. "1 million token context: The good, the bad and the ugly." Micron Insights, 2024.

9. SelfHostLLM. "GPU Memory Calculator for LLM Inference." SelfHostLLM, 2024. https://selfhostllm.org/

10. Omri Mallis. "Techniques for KV Cache Optimization in Large Language Models." Omri Mallis Blog, 2024. https://www.omrimallis.com/posts/techniques-for-kv-cache-optimization/

11. NVIDIA. "Mastering LLM Techniques: Inference Optimization." NVIDIA Technical Blog, 2023. https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/

12. NVIDIA. "Optimizing Inference for Long Context and Large Batch Sizes with NVFP4 KV Cache." NVIDIA Technical Blog, 2024. https://developer.nvidia.com/blog/optimizing-inference-for-long-context-and-large-batch-sizes-with-nvfp4-kv-cache/

13. NVIDIA. "Accelerate Large-Scale LLM Inference and KV Cache Offload with CPU-GPU Memory Sharing." NVIDIA Technical Blog, 2024. https://developer.nvidia.com/blog/accelerate-large-scale-llm-inference-and-kv-cache-offload-with-cpu-gpu-memory-sharing/

14. NVIDIA. "Introducing New KV Cache Reuse Optimizations in NVIDIA TensorRT-LLM." NVIDIA Technical Blog, 2024. https://developer.nvidia.com/blog/introducing-new-kv-cache-reuse-optimizations-in-nvidia-tensorrt-llm/

15. ———. "Introducing New KV Cache Reuse Optimizations in NVIDIA TensorRT-LLM." NVIDIA Technical Blog, 2024.

16. Meta Engineering. "Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism." Engineering at Meta, October 2025. https://engineering.fb.com/2025/10/17/ai-research/scaling-llm-inference-innovations-tensor-parallelism-context-parallelism-expert-parallelism/

17. GitHub. "long-context-attention: USP: Unified Sequence Parallel Attention for Long Context Transformers." GitHub, 2024. https://github.com/feifeibear/long-context-attention

18. ArXiv. "Context Parallelism for Scalable Million-Token Inference." ArXiv, November 2024. https://arxiv.org/abs/2411.01783

19. GitHub. "long-context-attention." GitHub, 2024.

20. Hugging Face. "Enabling Long Context Training with Sequence Parallelism in Axolotl." Hugging Face Blog, 2024. https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl

21. Medium. "Long Context in LLMs: What Million-Token Models Can — and Can't — Do." Foundation Models Deep Dive, 2024. https://medium.com/foundation-models-deep-dive/long-context-in-llms-what-million-token-models-can-and-cant-do-115af71ede4e

22. Medium. "Why Gemini 1.5 (and other large context models) are bullish for RAG." Enterprise RAG, 2024. https://medium.com/enterprise-rag/why-gemini-1-5-and-other-large-context-models-are-bullish-for-rag-ce3218930bb4

23. GitHub. "Performance Issues in Gemini's Large Context Windows." Gemini CLI Issues, 2025. https://github.com/google-gemini/gemini-cli/issues/5160

24. NVIDIA. "Scaling to Millions of Tokens with Efficient Long-Context LLM Training." NVIDIA Technical Blog, 2024.

25. AWS. "Context Parallelism Overview." AWS Neuron Documentation, 2024. https://awsdocs-neuron.readthedocs-hosted.com/en/latest/libraries/neuronx-distributed/context_parallelism_overview.html

26. Ajith Prabhakar. "Qwen2.5-1M: The First Open-Source AI Model with a 1 Million Token Context Window." Ajith Vallath Prabhakar, February 2025.

27. NVIDIA. "Scaling to Millions of Tokens with Efficient Long-Context LLM Training." NVIDIA Technical Blog, 2024.

28. Google Cloud. "Long context." Generative AI on Vertex AI Documentation, 2025. https://cloud.google.com/vertex-ai/generative-ai/docs/long-context

29. Codingscape. "LLMs with largest context windows." Codingscape Blog, 2025.

30. VentureBeat. "MiniMax-M1 is a new open source model with 1 MILLION TOKEN context." VentureBeat, 2025.

31. VentureBeat. "How Gradient created an open LLM with a million-token context window." VentureBeat, 2024. https://venturebeat.com/ai/how-gradient-created-an-open-llm-with-a-million-token-context-window

32. McKinsey. "What is a context window for Large Language Models?" McKinsey Explainers, 2024. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-a-context-window

33. Micron. "1 million token context: The good, the bad and the ugly." Micron Insights, 2024.

34. Medium. "The Double-Edged Sword of Massive Context Windows in Modern LLMs." Medium, 2024. https://medium.com/@wmechem/the-double-edged-sword-of-massive-context-windows-in-modern-llms-cd3dbe36c954

35. BentoML. "KV cache offloading." LLM Inference Handbook, 2024.

36. Codingscape. "LLMs with largest context windows." Codingscape Blog, 2025.

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Million-token contexts require 15GB KV cache per user and 2+ minute prefill times. Master the infrastructure for Gemini, Claude, and Qwen at extreme scales.

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Long-Context LLM Infrastructure: Million-Token Guide

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Deploy million-token LLMs with KV cache optimization, context parallelism, and efficient memory management. Cover Gemini 2M, Claude 1M, and Qwen deployment.

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Key takeaways

For ML engineers: - 1M token KV cache requires ~15GB per user; 70B model at 128K context needs ~42GB KV cache alone - Context parallelism achieves 93% efficiency on 128 H100s; processes 1M tokens in 77 seconds for 405B model - NVFP4 KV cache quantization halves memory vs FP8 with <1% accuracy loss

For infrastructure architects: - Gemini 2.5 Pro: 2M tokens; Claude Sonnet 4: 1M tokens; Qwen2.5-1M: 1M tokens (self-hostable) - Prefill latency exceeds 2 minutes at maximum context lengths—batch/async workflows required - DGX platform: 4TB system memory with 1TB/s GPU bandwidth enables 15ms KV cache transfer vs 2+ min recomputation

For capacity planning: - 70B model + 128K context impossible on single GPU (140GB weights + 42GB cache) - Batch size multiplication: 8 users with Llama-2-13B at 4K tokens = 25GB KV cache - Budget 3-5x GPU hours for custom long-context training vs standard training

For application architects: - "Lost in the middle" phenomenon: 40% context degradation; place critical info at boundaries - LMCache + vLLM achieves 3-10x latency reduction with prefix sharing - Hybrid RAG + modest context (32K-128K) often outperforms pure million-token on cost and accuracy