KV Cache Optimization for Long-Context Arabic Inference

Long context windows look free in a vendor demo. They are not. Every extra token a sovereign Omani institution feeds into Gemma 4 or Qwen 3.6 spends real GPU memory on the key-value cache, and Arabic text spends more of it per page than English. This piece walks through the four levers (paging, prefix caching, quantization, eviction) that decide whether a 256k context appliance serves 10 users or 100.

The KV cache problem at long context

During autoregressive generation, every transformer layer writes the keys and values of every prior token into a per-request cache. That cache is read on every new token, so it lives in fast GPU memory. Memory cost is roughly 2 × layers × heads × head_dim × bytes_per_element × sequence_length, multiplied again by concurrent users. For a Gemma 4 class model at 256k tokens in FP16, the cache for a single user can run into tens of gigabytes before the answer even begins.

Naive serving systems waste 60 to 80 percent of that memory on internal and external fragmentation, because each request grabs a contiguous slab sized to its worst case. The vLLM team measured exactly this in the original PagedAttention work and showed how a paging-style allocator collapses that waste to under four percent (Kwon et al., SOSP 2023).

Techniques: paging, prefix caching, quantization, eviction

Four optimisations stack on top of each other. They are independent levers, and modern serving stacks (vLLM, TensorRT-LLM, SGLang) implement most or all of them.

• PagedAttention. Treats the cache as fixed-size physical blocks (typically 16 tokens) with a logical-to-physical translation table per request. Eliminates fragmentation, enables copy-on-write across requests, and lifts throughput 2x to 4x at equal latency versus FasterTransformer or Orca (vLLM design notes).
• Prefix caching. When many requests share a common prefix (a long policy, a system prompt, a retrieved document), the engine computes K and V for that prefix once and reuses the same physical blocks across every dependent request. Time-to-first-token drops dramatically and concurrent capacity rises without buying more memory.
• KV cache quantization. Stores keys and values in FP8 or INT4 instead of FP16. FP8 typically halves cache memory at almost no measurable quality loss; INT4 quadruples capacity but with a slight accuracy hit and reduced generation speed at high batch sizes (vLLM quantized KV cache docs, LMDeploy INT4/INT8 KV cache).
• Eviction policies. When demand exceeds memory, the scheduler evicts blocks from idle or low-priority sessions and either swaps them to host RAM or recomputes them on resume. Good policies (LRU on session, priority bands by user tier) avoid the head-of-line stalls that plague naive servers.

Arabic-specific impact: longer tokens per character

Arabic punishes any technique that bills by the token. Most production sub-word tokenizers were trained on web corpora dominated by English. They split Arabic words, especially modern standard Arabic with rich diacritics and connected forms, into more pieces than the equivalent English. In our internal evaluations on a sample of regulator policy text, the same paragraph emitted 1.4 to 1.8 times more tokens in Arabic than in English. KV cache cost scales linearly with that number, so a 60-page bilingual policy document costs roughly 60 to 80 percent more cache to ingest in its Arabic form.

Two practical consequences for a sovereign deployment. First, prefix caching is even more valuable: a long Arabic system prompt, computed once and shared across 100 citizen-service sessions, recovers most of the Arabic tokenization tax. Second, FP8 quantization shifts from a nice-to-have to a default, because the same physical GPU now has to hold roughly twice the raw cache footprint of an English-only baseline.

Hardware implications and per-tier sizing

Translating these techniques into appliance sizing is the heart of the Gemma 4 256k context architecture work. A few field-tested rules of thumb for Arabic-heavy deployments:

• Hosn Kernel (single H100 80GB class). 30 concurrent Arabic users at 256k context with FP8 cache and aggressive prefix sharing. Suitable for a ministry policy assistant or a single-regulator analyst desk.
• Hosn Tower (dual GPU). 100 concurrent users at 256k, with eviction-on-idle. Suitable for a customer-service desk that serves citizens in MSA Arabic with periodic English handoffs.
• Hosn Rack (quad GPU plus tensor parallelism). 200 plus concurrent users with strict quality-of-service tiers. Suitable for classified-tier workloads where low latency is non-negotiable.

Memory bandwidth, not just capacity, becomes the binding constraint at long context. Each generated token reads the entire KV cache for its layer, so an HBM-bound GPU saturates the bus before it saturates compute. That is why H100 class hardware (3 TB/s plus HBM bandwidth) outperforms cheaper L40S boxes by far more than the headline FLOPS gap suggests. The same reasoning argues against splitting the cache across NVLink-less consumer cards: a single fast GPU often beats two slow ones for long-context Arabic, because the cache stays local and avoids interconnect round-trips.

One operational note worth surfacing for sovereign buyers. Long-context inference is sticky. Once a regulator team starts feeding 60 page policy bundles through the model, throughput targets that looked generous on paper at 32k context become tight at 256k, and tighter again when Arabic is the working language. We recommend planning capacity at the 95th percentile of expected context length, not the median, and reviewing tier sizing every quarter as workloads mature. The four levers in this piece all expand effective capacity without buying more hardware, which buys time to re-tier deliberately rather than under fire.

For a deeper look at how these techniques compose into a working 256k pipeline on a sovereign on-premise box, see the pillar piece on the Gemma 4 256k context architecture. Email [email protected] for a one-hour briefing on sizing the right tier for your Arabic workload.