The Diagnostic Query: Verifying SGLang's Internal State After GPU Topology Reconfiguration

In the middle of a complex GPU topology reconfiguration on a Proxmox-hosted machine learning server, a single diagnostic command reveals the quiet confidence of an engineer who knows exactly what to check next. Message [msg 6075] is deceptively simple — a curl to a local endpoint, a Python one-liner to parse JSON, and three numbers printed to stdout. But behind those three fields — max_total_num_tokens, tp_size, and kv_cache_dtype — lies the culmination of an intricate hardware partitioning operation that split eight NVIDIA RTX PRO 6000 Blackwell GPUs between two virtualized environments.

The Message

ssh root@10.1.230.174 'curl -s http://localhost:30000/get_server_info | python3 -c "import sys,json; info=json.load(sys.stdin); print(\"max_total_num_tokens:\", info.get(\"max_total_num_tokens\", \"N/A\")); print(\"tp_size:\", info.get(\"tp_size\", \"N/A\")); print(\"kv_cache_dtype:\", info.get(\"kv_cache_dtype\", \"N/A\"))"'

The response:

max_total_num_tokens: 471474
tp_size: 4
kv_cache_dtype: bf16

Why This Message Was Written

The assistant had just completed a multi-step surgical reconfiguration of the GPU topology on the Proxmox host (kpro6). The original deployment used all eight Blackwell GPUs in a single LXC container running SGLang with tensor parallelism of 8 (TP=8). The new requirement was to split these eight GPUs: four (on NUMA node 0) would remain with the nvidia driver for the LXC container running SGLang, while the other four (on NUMA node 1) would be moved to the vfio-pci driver for passthrough to a SEV-SNP VM.

This reconfiguration involved:

• Unbinding four GPUs from the nvidia driver and rebinding them to vfio-pci ([msg 6050])
• Editing the LXC configuration to remove the /dev/nvidia4 through /dev/nvidia7 mount entries ([msg 6055])
• Creating a new PCI mapping (pro6000-vm) in Proxmox's /etc/pve/mapping/pci.cfg for the VM-bound GPUs ([msg 6058])
• Updating the SGLang systemd service file from --tp 8 to --tp 4 ([msg 6068])
• Starting the container and verifying the server came up ([msg 6071])
• Running a smoke test chat completion to confirm the model was generating text (<msg id=6072-6074>) After the smoke test succeeded, the assistant faced a critical question: Is the server actually running with the correct configuration? The smoke test proved the model was alive and generating coherent responses, but it did not confirm the internal parameters that would determine performance, stability, and capacity. The /get_server_info endpoint provides exactly this kind of introspection.

The Three Fields and Their Significance

tp_size: 4

This is the most immediately critical field. The assistant had changed the service file from --tp 8 to --tp 4, but a configuration error — for instance, a stale service file, a caching issue, or a misread environment variable — could have left the server attempting to initialize with TP=8. Since only four GPUs were available to the container, a TP=8 initialization would either fail outright or silently degrade in unpredictable ways. The value 4 confirms that the new configuration took effect correctly.

The importance of this verification cannot be overstated. Tensor parallelism determines how the model's layers are sharded across GPUs. With TP=4, each GPU holds one quarter of each transformer layer. If the server had somehow retained TP=8, it would expect eight GPUs to distribute the shards, and with only four available, the distributed initialization would hang or crash — exactly the kind of failure that is time-consuming to debug from logs alone.

max_total_num_tokens: 471474

This field reports the total KV cache capacity across all GPUs. The KV (key-value) cache stores attention states for previously generated tokens, enabling the model to reuse them rather than recomputing. The capacity of 471,474 tokens represents the total number of token positions that can be cached simultaneously across the four GPUs.

This number is a direct function of the available GPU memory, the KV cache dtype, and the model architecture. With 97,887 MiB per GPU (four GPUs = ~391,548 MiB total), and approximately 80 GB consumed by the model weights themselves (as seen in [msg 6074] where memory usage was ~78-79 GB per GPU), the remaining memory is allocated to the KV cache. The value 471,474 tokens is consistent with BF16 KV cache on four Blackwell GPUs with the Qwen3.5-122B model (or whatever model is loaded).

This number has direct operational implications. It determines the maximum batch size and sequence length the server can handle. For the assistant's upcoming benchmarking and load testing, this capacity defines the upper bound of concurrent request handling.

kv_cache_dtype: bf16

The KV cache dtype being BF16 is a deliberate choice rooted in earlier troubleshooting. In Segment 39, the assistant had discovered that FP8 KV cache produced accuracy issues (NaN outputs) on the Blackwell SM120 architecture. The fix was to force BF16 KV cache, which trades some memory efficiency for numerical stability. The presence of bf16 here confirms that this workaround is active and that the server is not silently falling back to FP8.

This is a subtle but important verification. If the server had ignored the configuration directive and used FP8, the KV cache capacity would be roughly double (~943,000 tokens), but the model might produce degraded outputs. The assistant needed to confirm that the safe, stable configuration was in effect.

Input Knowledge Required

To fully understand this message, one needs familiarity with several concepts:

• SGLang: The serving framework used to deploy large language models. It provides a /get_server_info REST endpoint that exposes runtime configuration.
• Tensor Parallelism (TP): A model parallelism strategy where individual transformer layers are split across multiple GPUs. TP=N means each layer is sharded N ways.
• KV Cache: A memory structure that stores attention key-value pairs for previously generated tokens, avoiding recomputation. Its capacity directly limits batch size and sequence length.
• BF16 vs FP8: Two floating-point formats for representing numbers. BF16 (Brain Floating Point 16) uses 16 bits with 8 exponent bits, offering wider dynamic range. FP8 uses 8 bits, saving memory but potentially losing precision.
• Proxmox PCI Passthrough: The mechanism by which physical PCIe devices (like GPUs) are assigned to virtual machines via the vfio-pci driver.
• NUMA Nodes: Non-Uniform Memory Access domains that group CPUs and memory together. The eight GPUs were split across two NUMA nodes (0 and 1).

Output Knowledge Created

This message produces three concrete pieces of knowledge:

1. Configuration verification: The TP=4 change was applied correctly and the server is using exactly four GPUs.
2. Capacity measurement: The KV cache can hold 471,474 tokens, which sets expectations for throughput benchmarking.
3. Stability confirmation: The BF16 KV cache dtype is active, confirming that the FP8 workaround from Segment 39 is in place. Beyond these direct outputs, the message creates a foundation for the next steps. The assistant now knows the exact capacity available and can proceed to benchmark the server under load, design appropriate batch sizes, and compare performance against the previous 8-GPU configuration. The diagnostic also serves as a sanity check before moving on to more complex tasks like load testing with concurrent requests.

The Thinking Process

The assistant's reasoning is visible in the sequencing of commands leading up to this message. After the smoke test (<msg id=6072-6074>), the assistant did not simply declare victory and move on. Instead, it immediately queried the server info endpoint to validate the internal configuration. This reflects a disciplined engineering approach: a successful chat completion proves the server is alive, but only the server info endpoint proves the server is configured correctly.

The choice of which fields to extract is itself revealing. The assistant could have dumped the entire server info JSON, but instead selected three targeted fields that answer specific questions:

• "Did my TP change actually take effect?" → tp_size
• "How much headroom do I have for batch processing?" → max_total_num_tokens
• "Is the KV cache dtype what I expect?" → kv_cache_dtype This targeted extraction shows that the assistant had a clear mental model of what could go wrong and what needed verification. It was not exploring blindly — it was checking specific hypotheses about the server's state.

Conclusion

Message [msg 6075] is a textbook example of a diagnostic probe in systems engineering. It is brief, targeted, and immediately actionable. In three numbers, it confirms that a complex hardware reconfiguration was executed correctly, that the software stack is configured as intended, and that the system is ready for the next phase of work. For the reader, it offers a window into the disciplined, hypothesis-driven approach that characterizes effective infrastructure work: always verify, never assume, and know exactly what question each diagnostic command is designed to answer.