Benchmark results from Lightbits Labs demonstrate up to 1,154× faster time-to-first-token in long-context inference on commodity hardware – breaking the GPU memory wall and turning hours of stalled time into seconds of productive generation. These results were achieved on L40S GPUs with 48 GB of HBM each. No NVLink. No HBM3e. No specialized interconnect.
The Memory Wall Is Killing Long-Context Inference
Long-context workloads – agentic pipelines, enterprise RAG, multi-document reasoning – now routinely exceed 100K tokens, with production deployments approaching and surpassing 1 million tokens. At that scale, GPU HBM becomes the hard limit, in two places at once.
The prefill wall. Without cached KV data, the GPU must recompute the entire context from scratch on every turn. At 1M tokens, that means over six minutes before a single output token appears. At 10M tokens, it means 8.3 hours. This is not a latency problem. It is a wall.
The memory capacity wall. Even once generation begins, a context that exceeds GPU HBM capacity has nowhere to live. The standard answers – KV cache eviction or compaction – trade memory pressure for quality degradation. For low-memory accelerators like the L40S (48 GB HBM) or inference-optimized silicon like Groq’s LPU (which trades HBM capacity for extreme throughput), the ceiling is reached quickly and without recourse. At 10M tokens, no single GPU in any product line today can hold the full KV cache. The question is not whether to go beyond the GPU memory wall. It is how.
No amount of additional GPU procurement solves this structurally. The root cause is memory capacity per device, and the only answer is to extend the effective KV cache beyond GPU HBM into a storage layer that delivers pages in microseconds.
The GPU Memory Wall Is Killing Long-Context Inference
LightInferra’s Sub-Linear Attention Prefetch (SLAP) technology addresses both walls simultaneously – and uniquely, it removes the GPU HBM ceiling entirely.
Prefill. KV pages computed on Turn 1 are persisted to a disaggregated NVMe storage layer via RDMA. On Turn 2, those pages are prefetched into GPU memory ahead of the attention kernel – eliminating full KV recomputation and replacing a six-minute stall with sub-second TTFT.
Decoding beyond GPU capacity. When a conversation’s KV history exceeds what GPU HBM can hold, LightInferra’s virtual KV cache paging evicts cold NoPE-layer pages from GPU to LCF storage during chunked prefill, then streams them back window-by-window via a double-buffered streaming attention engine with online softmax merge. Attention correctness is preserved at context lengths that would be physically impossible to serve from GPU memory alone. The HBM ceiling is no longer the context length ceiling.
This matters most precisely where memory is scarcest. The L40S, with 48 GB of HBM, can natively hold approximately 200K tokens of KV cache for Llama-4-Scout-17B. LightInferra extends that to 10M tokens – 50× beyond what the hardware can natively support – with no model changes and no quality degradation. The same architectural benefit applies directly to any accelerator with constrained on-chip memory, including inference-optimized chips that deliberately trade HBM capacity for raw compute density.
The result: TTFT that grows sub-linearly with sequence length, decoding throughput that remains stable well past GPU memory capacity, and a validated path to 10M-token inference on commodity hardware today.
The Numbers
All tests used 4× L40S GPUs. Storage was tiered across a 2 NVMe target-side, and 1 GPU client (4× L40S node, TP=4) with cache under management by LightInferra. No NVLink. No high-bandwidth interconnect. Measurements reflect Turn 2 TTFT – the key production metric for resuming a long conversation.
| Model | Seq Length | Baseline TTFT | LightInferra TTFT | Speedup |
|---|---|---|---|---|
| Qwen 2.5-7B | 1M tokens | 6.2 min | 1.3 s | 289× |
| Llama-4-Scout-17B (MoE) | 400K tokens | ~2.2 min | 457 ms | 226× |
| DeepSeek-R1-70B | 102K tokens | 50 s | 316 ms | 159× |
| Llama-4-Scout-17B + Virtual Paging | 10M tokens | 8.3 hours | 26 s | 1,154× |
Turn 2 latency stays sub-second up to 819K tokens. At 10M tokens – 50× beyond what the L40S can hold in HBM – users wait 26 seconds instead of 8.3 hours.
The throughput story is equally stark. At 1M tokens, LightInferra sustains 27.2 tok/s for Qwen 7B. Without caching, that same workload manages 0.3 tok/s – a regime where the baseline is effectively non-functional for any production deployment. DeepSeek-R1-70B improves from 2.3 to 20.3 tok/s. Llama-4-Scout goes from 1.2 to 47.5 tok/s at 400K tokens. Output quality is identical in all cases – LightInferra reloads the exact KV state computed on the first turn.
For Llama-4-Scout’s MoE architecture, LightInferra’s cache retrieval avoids expert-routing prefill overhead entirely – yielding speedups even at short contexts and sustaining near-peak decode throughput across the full range tested.
Why Low-Memory Accelerators Benefit Most
The economics of inference silicon have bifurcated. High-HBM systems like H100 SXM or B200 defer the memory wall – but do not eliminate it; at 10M tokens, no GPU in production holds the full KV cache. Throughput-optimized accelerators like Groq’s LPU, or cost-optimized cards like the L40S, hit the wall much earlier, often below 200K tokens.
LightInferra’s value scales inversely with available HBM. The less memory a GPU has, the sooner it would otherwise stall – and the larger the fraction of inference work that LightInferra reclaims. A $30K commodity L40S cluster with LightInferra responds in 26 seconds at 10M tokens.
This reframes the hardware procurement conversation for inference operators. Deploying more L40S nodes – or integrating inference-optimized silicon with limited HBM – no longer means accepting a context length ceiling. LightInferra decouples context length from GPU memory capacity, making low-memory accelerators viable for long-context workloads that would otherwise require premium HBM-heavy hardware.
Why Storage Is the Missing Layer
The sub-millisecond random-read performance of NVMe is what makes both walls removable. For prefill, it closes the GPU’s prefetch window before attention stalls. For decoding beyond GPU capacity, it provides the RDMA bandwidth needed to stream evicted KV pages back into GPU staging buffers window-by-window – fast enough that attention correctness is preserved without ever requiring the full context to fit in HBM simultaneously.
LightInferra doesn’t just cache and retrieve KV data – it extends the logical KV address space beyond GPU memory. Cold NoPE-layer pages are evicted to the LCF storage fabric during chunked prefill via a virtual KV cache paging engine, then loaded back on-demand through a streaming attention path with online softmax merge. The GPU sees a coherent attention result. The storage layer holds whatever didn’t fit. There is no quality tradeoff: unlike eviction or compaction, no tokens are dropped.
Beyond latency, there is a capacity imperative. Production agentic systems are stateful. At scale, that means petabytes of KV cache that must remain accessible across sessions. GPU HBM cannot hold it; per-node NVMe doesn’t scale independently. The 196 TB of NVMe storage across this 3-node cluster holds billions of cached conversations — and the data survives daemon restarts and node reboots, verified across the full cluster.
What This Means for Inference Operators
For managed inference providers and NeoCloud operators, long-context requests that stall GPUs have a direct cost: utilization drops, concurrency suffers, QPS per GPU falls, and infrastructure spend rises to compensate. KV cache eviction under memory pressure compounds the problem by degrading output quality.
LightInferra removes the memory wall from both prefill and decoding – including decode at context lengths that exceed GPU HBM capacity entirely. Operators running L40S fleets, or evaluating inference-optimized accelerators with constrained on-chip memory, can serve 400K, 1M, and now 10M-token contexts on the same hardware without sacrificing generation quality or SLA compliance. The outcome is sustained performance from 10K tokens to 10M – and a path to long-context inference economics that are viable at commodity hardware prices, not just technically impressive at premium ones.
Test environment: 4× NVIDIA L40S GPUs 1 nodes · 28× ScaleFlux NVMe drives, 196 TB shared between hyper-converged (client) and 2 RDMA connected storage target hosts · RDMA fabric (ConnectX-7/8) · Models: Qwen 2.5-7B-Instruct-1M, DeepSeek-R1-Distill-Llama-70B FP8, Llama-4-Scout-17B-16E FP8 · No NVLink, no specialized interconnect
Full benchmark data: lightbitslabs.ai · Contact: info@lightbitslabs.com