The Validation Checkpoint: Measuring Throughput After Server Recovery

A Single Bash Command That Confirms Weeks of Optimization

In the middle of a complex, multi-week effort to train an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 model, there is a deceptively simple message that captures the culmination of an intense debugging and optimization cycle. Message [msg 3888] consists of a single bash command followed by its output: the assistant waits two minutes, then checks the inference log and server throughput. On its surface, it is a routine monitoring step. But to understand why this message matters—why it was written at this exact moment, what it reveals, and what it assumes—requires tracing the thread of failures, recoveries, and hard-won optimizations that led to it.

The Context: Recovery from Catastrophe

The immediate backdrop to this message is a server crash. Just a few messages earlier, the assistant had started the SGLang inference server with an aggressive --mem-fraction-static 0.93 configuration, aiming to maximize GPU KV cache capacity. The server loaded successfully, reporting max_total_num_tokens=231120—nearly double the previous capacity of 116,171 tokens. But when the inference pipeline began sending requests, the server promptly OOM'd ([msg 3877]). The crash was not subtle: the error traceback pointed to deepseek_v2.py line 2919, deep in the model's forward pass, with only 0.82 GB of GPU memory remaining after CUDA graph capture (<msg id=3878-3879>). A large prefill batch had blown through the meager headroom.

The assistant's response was methodical. It killed all processes, reset the GPUs from the Proxmox host ([msg 3860]), and restarted with a more conservative --mem-fraction-static 0.88 ([msg 3881]). This time, the server came up with max_total_num_tokens=159277 and 5.47 GB of headroom per GPU ([msg 3884]). The hierarchical cache (--enable-hierarchical-cache --hicache-size 48) was retained, providing 48 GB of host memory per TP rank for KV cache overflow. The assistant then started the inference pipeline and killed a stale process that had survived the crash (<msg id=3886-3887>).

The Message Itself: A Deliberate Pause

With the server running and the inference pipeline freshly launched, the assistant issues message [msg 3888]:

Good. Now let it run for a couple minutes and check throughput:

[bash] sleep 120 && ssh root@10.1.230.174 'echo "=== INFERENCE LOG ==="; tail -10 /data/eagle3/synth_100k/logs/inference_all.log; echo; echo "=== SERVER THROUGHPUT ==="; tail -10 /data/eagle3/synth_100k/logs/sglang_inference.log | grep -E "throughput|running"'

=== INFERENCE LOG ===

=== SERVER THROUGHPUT === [2026-02-24 11:59:51 TP0] Decode batch, #running-req: 121, #token: 139222, token usage: 0.87, cuda graph: True, gen throughput (token/s): 1317.32, #queue-req: 29 [2026-02-24 11:59:55 TP0] Decode batch, #running-req: 119, #token: 140807, token usage: 0.88, cuda graph: True, gen throughput (token/s): 1272.17, #queue-req: 31 [2026-02-24 11:59:59 TP0] Decode batch, #running-req: 117, #token: 142127, token usage: 0.89, cuda graph: True, gen throughput ...

The two-minute wait is not arbitrary. It is a deliberate design choice: long enough for the server to accumulate a meaningful batch of concurrent requests, short enough to catch problems before they compound. The assistant is simultaneously validating two things: that the inference pipeline is producing output (the inference log) and that the server is handling the load without crashing again (the throughput metrics).

What the Output Reveals

The results are a mixed signal. The inference log is conspicuously empty—=== INFERENCE LOG === is followed by nothing. This is a yellow flag. The inference script run_inference.py may still be in its startup phase, or it may have encountered a silent error that prevented it from writing log output. The assistant does not comment on this emptiness, perhaps because the server throughput numbers are so encouraging that they dominate attention.

And the throughput numbers are indeed impressive. The server is running at 1,272–1,317 tokens per second, with 117–121 concurrent requests, token usage at 0.87–0.89 (nearly full), and only 29–31 requests in the queue. This is a dramatic improvement over the ~600 tok/s baseline the assistant was working with earlier in the session (see [chunk 28.0]). The hierarchical cache is doing its job: the GPU alone at mem_fraction_static=0.88 would hold only ~159K tokens, but with the hicache spilling to host memory, the server is handling 139K–142K tokens actively in the decode batch while maintaining a queue of pending requests.

The Reasoning Behind the Numbers

To understand why these numbers matter, one must understand the bottleneck the assistant has been fighting. The KV cache—the memory that stores key-value pairs for each token in the context window—is the primary constraint on throughput for long-context models like Kimi-K2.5. With 8 GPUs in tensor parallelism, each GPU holds a shard of the KV cache. At the default mem_fraction_static=0.85, the server could only fit about 116K total tokens, limiting concurrent requests to roughly 50 at 4K average token length. Increasing to 0.88 pushed this to 159K tokens, but the real breakthrough came from the hierarchical cache, which uses host RAM (48 GB per rank) as a secondary tier, allowing the scheduler to keep many more request contexts alive even when their KV entries are evicted from GPU memory.

The throughput of ~1,300 tok/s represents roughly a 2–2.5x improvement over the initial baseline of ~600 tok/s. This is the payoff from the optimization work in this segment: the KV cache tuning, the hierarchical cache configuration, and the careful balancing of mem_fraction_static to leave enough headroom for transient allocations during prefill.

Assumptions Embedded in This Message

Every monitoring step carries assumptions, and this one is no exception. The assistant assumes that:

The server will remain stable for the two-minute wait period. Given that the previous server crashed within seconds of receiving requests, this is not a trivial assumption. The assistant has changed only the mem_fraction_static value (from 0.93 to 0.88), leaving all other parameters identical. The assumption is that 5.47 GB of headroom per GPU is sufficient to absorb prefill spikes.
The inference pipeline is functioning correctly. The empty inference log should have been a stronger signal. The assistant had just killed a stale inference process and started a new one; it is possible the new process inherited a corrupted state or failed to initialize properly. The assistant does not investigate the empty log, instead focusing on the server throughput.
The throughput numbers are representative. A two-minute window at near-peak load (token usage 0.87–0.89) gives a snapshot, but not necessarily a stable measurement. The queue of 29–31 requests suggests the system is slightly over-subscribed, which is actually desirable for measuring peak throughput—it ensures the batch is always full.
The hierarchical cache is functioning correctly. The assistant does not explicitly verify that hicache evictions and prefetches are happening without error. The throughput numbers are consistent with a working cache, but silent failures (e.g., all requests hitting the GPU cache directly, with hicache never being exercised) would produce the same throughput numbers.

What This Message Creates: Output Knowledge

This message produces several concrete pieces of knowledge:

Throughput validation: The server achieves ~1,300 tok/s under realistic load with 121 concurrent requests. This is the first measurement after the crash recovery, and it confirms that the mem_fraction_static=0.88 + hicache=48GB configuration is viable.
Token usage ceiling: At 0.87–0.89 token usage, the system is operating near its maximum capacity. Any increase in request rate would likely lead to queue growth and latency degradation rather than throughput improvement.
Queue depth signal: The 29–31 queued requests indicate the system is slightly over-loaded at the current concurrency settings (150 short-concurrency, 32 long-concurrency). This is a tuning signal: the assistant may need to reduce concurrency or increase the hicache size to fully absorb the load.
Inference pipeline status: The empty inference log is itself a data point. It either means the pipeline is still initializing (unlikely after two minutes) or that it has encountered an issue. This creates an action item for the next round: investigate the inference script's output.

The Thinking Process: What the Assistant Is Really Doing

Although the message appears to be a simple monitoring command, the assistant's thinking process is visible in the structure of the command itself. The assistant chooses to check both the inference log and the server throughput in a single SSH command, rather than making two separate calls. This is an efficiency optimization—reducing latency and network overhead. The sleep 120 is placed before the SSH command in the bash invocation, meaning the assistant's tool call will block for two minutes before returning any output. This is a deliberate trade-off: the assistant sacrifices responsiveness (it cannot act on any other information during the wait) for a consolidated view of system state.

The choice of tail -10 for the inference log and grep -E "throughput|running" for the server log reveals what the assistant considers important. For the inference pipeline, it wants to see the last 10 lines of output—any errors, progress bars, or completion messages. For the server, it specifically filters for throughput and running request counts, ignoring the more verbose per-batch metadata. This filtering shows that the assistant's primary concern is performance validation, not debugging. If the server were crashing, the error traceback would appear in the unfiltered log, but the assistant is not looking for that. It is looking for confirmation that the optimization worked.

Mistakes and Missed Signals

The most significant oversight in this message is the empty inference log. The assistant does not remark on it, does not investigate, and does not adjust its behavior. In the context of the broader session, this turns out not to be a critical issue—the inference pipeline eventually produces results. But at this moment, the assistant has incomplete information and proceeds as if all is well. A more cautious approach would have been to check whether the inference process is still alive (pgrep -f run_inference) or to look for error output on stderr.

A second subtle issue is the token usage metric. The assistant reports token usage at 0.87–0.89, which is close to saturation. In a production system, sustained operation above 0.85 token usage increases the risk of OOM during prefill spikes—exactly the problem that caused the previous crash. The assistant has already learned this lesson the hard way (the 0.93 → 0.88 retreat), but the current configuration is still operating near the margin. A future improvement might be to add a token usage alarm or to reduce mem_fraction_static further to increase the safety buffer.

Conclusion: A Pivot Point in the Pipeline

Message [msg 3888] is a pivot point in the inference pipeline optimization arc. It represents the first successful throughput measurement after a crash-and-recover cycle, and it validates that the hierarchical cache configuration is delivering the expected performance gains. The ~1,300 tok/s throughput is a concrete, measurable improvement over the ~600 tok/s baseline, and it gives the assistant confidence to proceed with the large-scale data generation needed for EAGLE-3 training.

The message also exemplifies a pattern that recurs throughout the session: the assistant uses short, focused monitoring steps to validate system state before proceeding to the next phase. Each validation creates a checkpoint—a moment of certainty in an otherwise uncertain process. The empty inference log is a loose thread, but it is a minor one. The dominant signal is clear: the server is stable, the throughput is strong, and the pipeline can move forward.