The 44.77 GB Confirmation: Validating Hierarchical KV Cache in a Production LLM Deployment

The Message

ssh root@10.1.230.174 'systemctl is-active sglang-kimi.service && journalctl -u sglang-kimi -o cat --no-pager -n 5'

Output:

active
[2026-03-01 23:45:08 TP1] Allocating 44.77 GB host memory for hierarchical KV cache.
[2026-03-01 23:45:08 TP0] Allocating 44.77 GB host memory for hierarchical KV cache.
[2026-03-01 23:45:08 TP5] Allocating 44.77 GB host memory for hierarchical KV cache.
[2026-03-01 23:45:08 TP4] Allocating 44.77 GB host memory for hierarchical KV cache.
[2026-03-01 23:45:08 TP2] Allocating 44.77 GB host memory for hierarchical KV cache.

Context and Motivation

This message represents a quiet but significant milestone in a long-running production deployment effort. The assistant and user had been working for hours—across multiple segments and dozens of messages—to deploy the Kimi-K2.5 INT4 model, a massive 547 GB parameter model spread across 8 NVIDIA RTX PRO 6000 Blackwell GPUs. The journey had included everything from EAGLE-3 speculative decoding optimization with topk=1 and spec_v2 overlap scheduling, to systemd service creation, to tool-call and reasoning parser configuration.

The immediate trigger for this message was a user query in [msg 5737]: "What about hicache?" The assistant had just finished explaining four options for expanding KV cache capacity, and the user cut directly to the most impactful one: the hierarchical KV cache (HiCache) feature built into SGLang. This feature uses system RAM as an L2 tier for the KV cache, allowing evicted GPU cache entries to spill to CPU memory instead of being discarded. When future requests share common prefixes, those entries can be reloaded from CPU RAM over PCIe rather than being recomputed from scratch.

The assistant initially added HiCache with a ratio of 2.0 ([msg 5738]), but the user pushed for more: "can we do ratio 4?" ([msg 5741]). The assistant updated the systemd service configuration accordingly, setting --hicache-ratio 4.0, and restarted the service. The restart took considerable time—the model is 547 GB and must be loaded across 8 GPUs with tensor parallelism—and the assistant had been polling for health with a timeout that expired ([msg 5745]). The user noted "Needs a while" ([msg 5747]), acknowledging the expected startup time.

Message 5748 is the follow-up check. The assistant runs a compound command: first check if the systemd service is active, and if so, grab the last 5 lines of the service's journal. The result confirms both conditions: the service is running, and the hierarchical KV cache has successfully allocated 44.77 GB of host memory per GPU.

What This Message Reveals

The output carries multiple layers of meaning. First, the simple word "active" from systemctl is-active tells us the SGLang server process started, loaded the model, initialized all tensor parallel workers, and is now ready to serve requests. This is non-trivial for a 547 GB model spread across 8 GPUs with speculative decoding enabled—any number of issues could have caused a crash during startup.

Second, the journal lines confirm that the hierarchical KV cache is working. Each of the five visible tensor parallel workers (TP0 through TP5) reports allocating 44.77 GB of host memory. The output shows only 5 lines because the -n 5 flag limits the journalctl output, but the allocation likely occurred on all 8 TP workers. The 44.77 GB figure validates the memory math behind the --hicache-ratio 4.0 setting. Earlier in the conversation, the assistant had traced the KV cache allocation: each GPU had approximately 10.4 GB of KV cache for 159,277 tokens. With a ratio of 4.0, the expected host-side allocation would be roughly 4 × 10.4 = 41.6 GB per GPU. The actual 44.77 GB is slightly higher, likely because the hicache ratio applies to the full KV cache pool capacity (which includes some headroom from the --mem-fraction-static 0.88 setting) rather than the exact allocated token count. Across all 8 GPUs, this amounts to approximately 358 GB of system RAM dedicated to the L2 KV cache—a massive expansion of effective cache capacity.

Assumptions and Reasoning

The assistant made several assumptions in this message. The primary assumption was that the service would be active by the time the check ran. Given that the previous health check had timed out, this was not guaranteed—the model loading process could still be in progress, or it could have crashed. The compound command systemctl is-active sglang-kimi.service && journalctl ... is a defensive pattern: it only fetches the journal if the service is confirmed active, preventing confusing output from a partially started or failed service.

The assistant also assumed that the hierarchical cache initialization would produce log lines containing "Allocating ... host memory for hierarchical KV cache." This assumption was based on the research done in [msg 5723], where the assistant had investigated SGLang's HiCache implementation and understood its initialization behavior. The grep pattern used to extract the relevant lines was intentionally simple, relying on the journalctl output format.

A subtle but important assumption was that the --hicache-ratio 4.0 setting would produce a proportional increase in host memory allocation. The assistant had previously calculated that the GPU KV cache was ~10.4 GB, so ratio 4.0 should yield ~41.6 GB. The actual 44.77 GB is close but not exact, suggesting the ratio applies to a slightly different base value than the assistant assumed. This discrepancy is minor and does not indicate a problem—the allocation succeeded, and the system has ample RAM (approximately 396 GB free, with 358 GB now allocated, leaving ~38 GB headroom).

Input Knowledge Required

To fully understand this message, one needs knowledge spanning several domains. The reader must understand the concept of a KV (Key-Value) cache in transformer-based LLMs—the mechanism that stores computed attention keys and values for reuse across decoding steps, avoiding redundant computation. They must understand tensor parallelism (TP), where a single model is sharded across multiple GPUs, each handling a portion of the computation. The TP0, TP1, etc., labels in the log lines refer to these parallel workers.

The reader also needs familiarity with SGLang's hierarchical cache feature, which implements a two-tier cache architecture: GPU HBM as L1 and CPU DRAM as L2. The --hicache-ratio parameter controls how much host memory to allocate relative to the GPU cache size. The write_through write policy means every KV entry written to the GPU cache is also written to the host cache, ensuring consistency. The kernel I/O backend uses custom CUDA kernel code for efficient PCIe transfers between GPU and CPU memory.

System administration knowledge is also required: the systemctl is-active command checks systemd service state, and journalctl retrieves service logs. The compound command with && ensures the second command only runs if the first succeeds—a common shell pattern for conditional execution.

Output Knowledge Created

This message creates concrete, verifiable knowledge about the production deployment. It confirms that:

  1. The systemd service starts successfully with the HiCache configuration.
  2. The hierarchical KV cache allocates the expected amount of host memory (44.77 GB per GPU).
  3. The allocation happens during server initialization, before the server accepts requests.
  4. The tensor parallel workers independently allocate their own host memory pools.
  5. The total system RAM commitment (~358 GB) is sustainable given the available ~396 GB. This knowledge is immediately actionable. It validates the configuration change and gives the operator confidence that the system is running correctly. It also provides a baseline for future tuning: if the operator wants to increase the ratio further, they now know the actual per-GPU allocation formula and can predict the memory impact.

Broader Significance

In the context of the entire coding session, this message represents the culmination of a long optimization journey. Earlier segments had focused on diagnosing why EAGLE-3 speculative decoding was performing worse than baseline ([segment 33]), analyzing NCCL all-reduce bottlenecks ([segment 34]), testing various allreduce optimization approaches that were systematically eliminated ([segment 35]), upgrading the CUDA stack to version 13 ([segment 36]), and benchmarking EAGLE-3 vs baseline throughput ([segment 37]). The transition to production deployment in [segment 38] marked a shift from experimental optimization to hardened service configuration.

The hierarchical KV cache is the final piece of that production configuration. It doesn't increase peak throughput, but it dramatically improves the server's ability to handle diverse request patterns with shared prefixes—a common scenario in real-world LLM serving where system prompts, few-shot examples, and conversation histories create reusable KV cache entries. By dedicating 358 GB of system RAM as a cache overflow, the server can retain far more prefix data than the GPU memory alone would allow, reducing recomputation and improving latency for requests that share context with previous ones.

The 44.77 GB figure will also serve as a reference point for future capacity planning. If the operator needs to run additional services on the same machine, they now know the exact memory footprint of the HiCache and can make informed trade-offs between cache size and available RAM for other workloads.

Conclusion

Message 5748 is a brief status check that packs significant meaning. In two lines of command and five lines of output, it validates a complex configuration change, confirms the memory math behind a key optimization feature, and marks the successful transition of a massive LLM deployment into a production-ready state. The assistant's choice of a conditional compound command reflects careful engineering practice—only fetch detailed logs if the service is confirmed healthy. The output reveals that the hierarchical KV cache is working exactly as designed, allocating 44.77 GB of host memory per GPU for a total of ~358 GB of L2 cache capacity. For anyone following this deployment journey, those five log lines are the sound of a complex machine clicking into place.