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How to Run Qwen3.8-Flash-Next on Two DGX Sparks Over One Cable · Encrypted Ambition: Where Ambition Meets Encryption - Inside The Minds Of Disruptors.

• 0:14
  Host: Today we look at how two NVIDIA DGX Spark units can serve a large language model as a single engine.
• 0:21
  Analyst: The setup uses a direct QSFP cable between the units’ ConnectX-7 ports, creating a private link that carries all tensor-parallel traffic.
• 0:30
  Host: Can you explain what hardware we’re talking about and why it matters?
• 0:34
  Analyst: Each unit is a GB10 chassis, containing eight GPUs and a 400 Gbps NIC. The cable keeps inter-unit communication isolated from the office network.
• 0:46
  Host: Which model is being served over that link?
• 0:49
  Analyst: It’s RadixArk’s Qwen3.8-Flash-Next-NVFP4, a 27-billion-parameter model quantized to NVFP4 for speed and efficiency.
• 1:00
  Host: How did Petronella get these units to work together?
• 1:03
  Analyst: They published a public recipe on GitHub. The repo contains a launch script, a smoke-test script, and a README that walks through each step.
• 1:12
  Host: What does the launch script do?
• 1:14
  Analyst: It starts one vLLM rank per unit. Each rank runs a container based on oxbyte/qwen3.8-flash-next-dual-spark:latest.
• 1:24
  Host: When was this configuration measured?
• 1:26
  Analyst: The measurement took place on 2026-10-02 on Petronella’s own DGX Spark pod, with both units’ GPU clocks locked to a 300-to-2300 MHz range.
• 1:38
  Host: Did the engine pass any correctness tests before speed was recorded?
• 1:42
  Analyst: Yes, a correctness gate ran first. It passed all eight tests before any speed numbers were logged.
• 1:50
  Host: What are the decode speeds for a single user?
• 1:52
  Analyst: One user sees 35.2 tokens per second on prose and 52.6 tokens per second on code.
• 2:00
  Host: How does the system handle multiple users concurrently?
• 2:03
  Analyst: At 32 simultaneous users, the aggregate throughput is 329.6 tokens per second, and that’s the maximum the pair can serve while keeping each user above the 10-token-per-second floor.
• 2:17
  Host: What about the prefill stage for long documents?
• 2:20
  Analyst: Prefill runs at about 3,089 tokens per second for an 8-K-token prompt and 3,069 tokens per second for a 32-K-token prompt, showing a nearly flat curve.
• 2:34
  Host: Energy consumption is always a concern. How efficient is this setup?
• 2:38
  Analyst: The pair draws 251 W at one user and 285 W at the full 32-user ceiling, achieving 1.16 tokens per joule.
• 2:49
  Host: Does the model support very long context windows?
• 2:52
  Analyst: A needle-retrieval test passed at every length up to 120,001 tokens, confirming that the 131,072-token context window is usable in practice.
• 3:05
  Host: Can you walk through the configuration flags used in the launch script?
• 3:08
  Analyst: Sure. The script sets --tensor-parallel-size 2 and --enable-expert-parallel for one rank per unit, and uses --max-num-seqs 64 and --gpu-memory-utilization 0.75 to cap concurrency.
• 3:25
  Host: What about context length and memory usage?
• 3:28
  Analyst: The --max-model-len flag is set to 131072 tokens, which allows the KV pool to hold 1,277,645 tokens, enough for about nine full-length requests at once.
• 3:44
  Host: I see there’s a --enforce-eager flag. Why is eager execution chosen?
• 3:50
  Analyst: Eager execution disables CUDA graphs. Petronella measured the engine with this flag, so any deviation would change the baseline numbers.
• 4:00
  Host: How is network transport handled across the cable?
• 4:03
  Analyst: NCCL is pinned to the RDMA transport with NCCL_NET=IB and RoCE v2 on a single HCA, ensuring all tensor-parallel traffic stays on the direct link.
• 4:16
  Host: What’s the required setup order when starting the two ranks?
• 4:19
  Analyst: Unit B, acting as rank 1, starts first and runs headless. Then unit A, rank 0, starts and exposes the OpenAI-compatible API on port 8888. Finally a smoke-test confirms the engine is up.
• 4:37
  Host: If the cable names differ on the units, how do you adjust?
• 4:40
  Analyst: You use IFACE and HCA environment variables to override the cable interface and RDMA device names. Checking ip -br link and ibv_devices on each unit reveals the actual names.
• 4:55
  Host: What if the launch fails? How can you diagnose it?
• 4:58
  Analyst: The launch script preserves the previous container’s logs before replacing it. Those logs are often the only record of why a failure occurred.
• 5:07
  Host: Security is a big concern. How is the API exposed?
• 5:11
  Analyst: vLLM binds to 0.0.0.0 with no API key. That means port 8888 should stay on a private network or behind an authenticating proxy.
• 5:24
  Host: What about scaling beyond two units?
• 5:26
  Analyst: The same image and settings scale to four units, yielding 41.4 tokens per second for a single prose user and 482.4 tokens per second at 32 users, with 648.7 tokens per second at 64 users.
• 5:44
  Host: And eight units?
• 5:45
  Analyst: Eight units as one engine reach 45.7 tokens per second for a single prose user, 604.7 tokens per second at 32 users, and 769.7 tokens per second at 64 users, but the tokens per joule drop to 0.58 at 32 users.
• 6:05
  Host: So two units are most efficient per watt?
• 6:08
  Analyst: Exactly. Two units deliver 1.16 tokens per joule, the highest among the configurations measured.
• 6:16
  Host: What about the cost of the cable itself?
• 6:18
  Analyst: Petronella sells a 0.5 m QSFP112 400 G DAC cable for $159 on the DGX Spark cluster cable page. Similar pages exist for other GB10 systems.
• 6:32
  Host: Can this recipe be used on other GB10 chassis?
• 6:35
  Analyst: Yes. The same image and settings work on Dell Pro Max, ASUS Ascent, HP ZGX Nano, Lenovo ThinkStation, and MSI EdgeXpert GB10 units.
• 6:47
  Host: Why does the recipe serve only 131,072 tokens when the model supports 262,144?
• 6:54
  Analyst: Serving the full 262,144 would halve the number of full-length requests that fit in the KV pool, so the recipe trades maximum context for higher concurrency.
• 7:06
  Host: How does the system lock GPU clocks?
• 7:09
  Analyst: Both units had their GPU clocks locked to a 300-to-2300 MHz range for the entire measurement, ensuring consistency across runs.
• 7:19
  Host: What about model quality? Does it matter?
• 7:22
  Analyst: On a retrieval-grounded CMMC battery, the two-unit setup scored 0.978, matching a larger FP8 model on a single RTX PRO 6000 GPU.
• 7:34
  Host: What are the known limits of this release?
• 7:36
  Analyst: The API binding to 0.0.0.0 with no key, the script being limited to two units, and the fact that the numbers come from a single run on 2026-10-02.
• 7:50
  Host: If an organization wants to deploy this, what steps should they take first?
• 7:55
  Analyst: Start by verifying the cable link with the gb10-cluster-check repository, then follow the gb10-cluster-guide for cabling and network setup.
• 8:05
  Host: Where can they find the launch scripts and documentation?
• 8:07
  Analyst: The public recipe lives on GitHub at https://github.com/capetron/qwen3.8-flash-next-dual-dgx-spark, with launch/node.sh, scripts/smoke-test.sh, and a README.
• 8:23
  Host: How can they confirm they’re using the same image version?
• 8:26
  Analyst: Pull oxbyte/qwen3.8-flash-next-dual-spark:latest, then run docker image inspect to check the image ID matches the measured one.
• 8:37
  Host: Is there a way to get support from Petronella?
• 8:39
  Analyst: They offer a private AI solutions practice. You can call Penny at 919-348-4912 or use the contact form at /contact-us/ for assistance.
• 8:52
  Host: What about the broader context of private AI deployments?
• 8:55
  Analyst: Petronella designs and runs private AI on customer owned hardware since 2002, focusing on data sovereignty and compliance.
• 9:04
  Host: Can you elaborate on the energy efficiency benefits?
• 9:07
  Analyst: At 1.16 tokens per joule, the two-unit pair uses 251 W at single user and 285 W at full load, making it suitable for an office environment.
• 9:20
  Host: How do the numbers for four and eight units compare in terms of tokens per joule?
• 9:24
  Analyst: Four units achieve 0.90 tokens per joule at 32 users, rising to 1.18 at 64 users. Eight units drop to 0.58 tokens per joule at 32 users.
• 9:38
  Host: Does the recipe include any special security hardening steps?
• 9:41
  Analyst: Beyond keeping port 8888 private, the recipe doesn’t add extra hardening. Petronella recommends using an authenticating proxy or firewall.
• 9:51
  Host: What about the logistics of setting up the checkpoint?
• 9:54
  Analyst: Download the checkpoint on both units with huggingface-cli using the specific revision ID, then pull the container image before launching.
• 10:03
  Host: If an organization wants to start with two units and expand later, is that feasible?
• 10:08
  Analyst: Yes. The same image and settings scale to more units, so you can begin with two and add more as workload demands grow.
• 10:16
  Host: Are there any references or further reading for the audience?
• 10:19
  Analyst: They can explore Petronella’s LLM benchmark hub, the GB10 cluster guide, the cluster check tool, and the private AI solutions page for deeper insight.
• 10:30
  Host: So, stepping into the practical side, what’s the first on-prem step an IT lead should tackle?
• 10:35
  Analyst: Begin by pulling the specific container image and checkpoint on every unit.
• 10:40
  Host: Which exact image tag and revision does the recipe lock?
• 10:44
  Analyst: It pulls oxbyte/qwen3.8-flash-next-dual-spark:latest and uses a Hugging Face revision ID.
• 10:53
  Host: Why is the revision ID critical for consistency?
• 10:56
  Analyst: Because the quantized weights change between revisions, and the recipe’s performance numbers assume that exact set.
• 11:03
  Host: Once the image is pulled, how do we handle the checkpoint?
• 11:06
  Analyst: Run huggingface-cli download on each unit, specifying the exact revision, so both ranks load identical weights from local disk.
• 11:15
  Host: Does the recipe assume any specific network configuration for the direct cable?
• 11:20
  Analyst: Yes, it requires a small private subnet for the QSFP connection, and you must set CABLE_SUBNET to match.
• 11:28
  Host: What about the NIC and RDMA device names? Those can vary.
• 11:33
  Analyst: Run ip -br link to see the cable interface and ibv_devices for the RDMA device; then export IFACE and HCA accordingly.
• 11:45
  Host: If those names are wrong, what failure do we see?
• 11:48
  Analyst: The engine will fail to establish a NCCL connection over RDMA, and the launch script will exit with an error before the API is up.
• 11:57
  Host: The script includes a smoke test; how does that help?
• 12:01
  Analyst: It sends a simple request to the OpenAI-compatible endpoint and verifies a correct response, ensuring the correctness gate passed.
• 12:10
  Host: What happens if the smoke test fails?
• 12:12
  Analyst: The script saves the previous container’s logs before replacing it, so you can inspect why it failed.
• 12:18
  Host: That’s useful. How do we stop the engine cleanly?
• 12:22
  Analyst: Run docker rm -f qwen38-flash-next on both units; the script handles cleanup automatically when you re-launch.
• 12:31
  Host: What about GPU clock settings? The article mentions a locked range.
• 12:36
  Analyst: Both units had clocks locked between 300 and 2300 MHz for the entire run, ensuring consistent performance across runs.
• 12:45
  Host: If an organization wants to scale from two to four units, what changes in the launch process?
• 12:51
  Analyst: The same image and settings apply; you simply add two more ranks, update the rank numbers, and ensure the cable network can support the extra links.
• 13:00
  Host: Does the recipe automatically handle multiple ranks, or do we need a different script?
• 13:04
  Analyst: The provided launch script is for two units only; for more ranks you would need to modify the script or use a cluster orchestrator that can launch multiple instances.
• 13:14
  Host: What about the per-user speed floor? How does that impact planning?
• 13:18
  Analyst: The recipe enforces a 10 tok/s floor; at 32 users the pair stays above that threshold, so 32 concurrent interactive sessions are supported.
• 13:29
  Host: If an organization requires more than 32 concurrent users, what configuration would you recommend?
• 13:35
  Analyst: Deploy four units; the benchmark shows 64 users at the 10 tok/s floor with 648.7 tok/s aggregate, while still maintaining a respectable 0.90 tok/J at 32 users.
• 13:51
  Host: What if the use case is high-speed single-user workloads rather than many users?
• 13:56
  Analyst: An eight-unit engine gives 45.7 tok/s prose and 63.5 tok/s code for one user, but the energy cost rises to 0.58 tok/J at 32 users, so it’s a trade-off.
• 14:12
  Host: How do we monitor power consumption in a production environment?
• 14:15
  Analyst: Use the NVIDIA System Management Interface or the vendor’s power monitoring tools; the benchmark reports 251 W at single user and 285 W at full load for the pair.
• 14:28
  Host: Does the recipe provide any guidance on handling failures during inference?
• 14:32
  Analyst: If a rank crashes, the other continues until you restart; the launch script will replace the failed container and preserve logs for diagnosis.
• 14:42
  Host: What about security beyond keeping port 8888 private? Any hardening recommendations?
• 14:48
  Analyst: Use a firewall or reverse proxy that authenticates requests; the recipe itself does not embed any token validation.
• 14:57
  Host: Is there any risk if the RDMA link goes down during operation?
• 15:00
  Analyst: The engine will lose inter-rank communication, causing inference to stall or fail; the script’s smoke test will detect this on restart.
• 15:09
  Host: What about the long-context capability? How do we verify that?
• 15:13
  Analyst: The long-context gate tests needle retrieval up to 120,001 tokens; you can run the same test script to confirm your environment supports that length.
• 15:24
  Host: Does the recipe allow us to adjust the context length beyond 131,072?
• 15:30
  Analyst: Changing max-model-len to a larger number would reduce the number of full-length requests that fit in the KV pool; the recipe sets it to balance concurrency and memory.
• 15:41
  Host: What about the quality of the model outputs? How does that factor into deployment decisions?
• 15:46
  Analyst: The benchmark reports a 0.978 score on a retrieval-grounded CMMC battery, comparable to a larger FP8 model; that indicates the quantized version still delivers high-fidelity responses.
• 16:00
  Host: If quality is acceptable, what operational metrics should we monitor post-deployment?
• 16:05
  Analyst: Track token throughput, per-user latency, and energy consumption; also watch for any error rates from the smoke test or inference logs.
• 16:15
  Host: Are there any common pitfalls when first deploying this recipe?
• 16:18
  Analyst: Mistakes include not verifying the cable link with gb10-cluster-check, using the wrong subnet, ignoring the IFACE/HCA mismatch, and overlooking the need to keep port 8888 private.
• 16:32
  Host: What about the licensing of the recipe and container image?
• 16:35
  Analyst: The recipe repo is MIT-licensed; the container image is also open source, and the checkpoint is shared under RadixArk’s licensing terms.
• 16:44
  Host: How does this setup compare to a single-GPU deployment in terms of cost?
• 16:49
  Analyst: A single GPU would not support the 131,072-token window or the 32-user ceiling; the two-unit pair offers a balanced trade-off between performance, energy, and hardware cost.
• 17:03
  Host: What resources can an organization consult to avoid misconfigurations?
• 17:08
  Analyst: The GB10 cluster guide walks through cabling, the cluster check tool verifies the link, and the benchmark hub documents every measured configuration.
• 17:18
  Host: Can the recipe be used on other GB10 systems, not just DGX Spark?
• 17:23
  Analyst: Yes; the recipe is agnostic to chassis, and a pod of four MSI EdgeXpert GB10 units matched the performance within a few percent.
• 17:33
  Host: What about the cost of the QSFP cable; is it a significant expense?
• 17:37
  Analyst: The 0.5 m QSFP112 400G DAC cable for DGX Spark retail at $159, and similar cables are available for other GB10 models.
• 17:50
  Host: If an organization wants to keep the deployment fully open source, what should they keep in mind?
• 17:56
  Analyst: Ensure you pull the exact image tag and verify the image ID with docker image inspect; any change in the image can alter performance.
• 18:05
  Host: Is there a way to monitor the correctness gate in production?
• 18:08
  Analyst: While the script runs the gate only during startup, you can add a periodic health check that calls the smoke test endpoint and logs any deviation.
• 18:17
  Host: What about scaling beyond eight units; does the recipe support that?
• 18:22
  Analyst: The underlying image and settings work for any rank count; you would need a cluster orchestrator or a custom script to launch more than four ranks, but the performance characteristics would follow the same trend.
• 18:34
  Host: Can we adjust the concurrency settings to handle more than 64 sequences?
• 18:38
  Analyst: Increasing --max-num-seqs would push GPU memory usage beyond the 75 % limit; you would need to raise gpu-memory-utilization or add more GPUs.
• 18:50
  Host: What about the effect of disabling CUDA graphs?
• 18:53
  Analyst: The recipe runs with --enforce-eager, so CUDA graphs are not used; changing that would alter the measured numbers and could affect latency for some workloads.
• 19:04
  Host: If we want to keep the same performance but reduce power, what can we do?
• 19:08
  Analyst: You could lower the GPU clock range, but that will also reduce throughput; the 300-2300 MHz lock was chosen to balance peak performance and energy.
• 19:19
  Host: How do we handle updates to the model or its checkpoint?
• 19:22
  Analyst: Pull the new checkpoint revision, verify the image ID, and redeploy the container; run the smoke test to confirm the correctness gate before exposing the API.
• 19:33
  Host: What about integrating this engine into a larger application stack?
• 19:37
  Analyst: Expose the OpenAI-compatible endpoint behind a service mesh or API gateway; ensure authentication and rate limiting are in place before routing traffic to the 8888 port.
• 19:49
  Host: Is there any guidance on how to monitor the health of the RDMA link in production?
• 19:54
  Analyst: Use ibv_devinfo and the cluster check tool to verify link status; set up alerts for loss of RDMA connectivity, as that will halt inference.
• 20:05
  Host: What is the recommended approach for backups or disaster recovery of the model data?
• 20:09
  Analyst: Because the checkpoint resides on local disk, maintain a versioned copy on a separate storage tier; the container image can be pulled from the registry as needed.
• 20:19
  Host: So far we've covered the recipe and the numbers; now let's talk about the broader impact.
• 20:24
  Analyst: Deploying Qwen3.8-Flash-Next on two DGX Sparks gives you a long-context engine that can handle over 120,000 tokens in a single request.
• 20:35
  Host: That means you can embed entire contracts or codebases without splitting the prompt, which simplifies downstream pipelines.
• 20:43
  Analyst: Exactly, and the flat prefill performance at 8K and 32K tokens keeps latency predictable for large documents.
• 20:51
  Host: What should an organization do first when considering this setup?
• 20:54
  Analyst: Start by ensuring your infrastructure supports a direct QSFP cable and that RDMA is enabled on the ConnectX-7 ports.
• 21:03
  Host: So the cable is a single point of failure?
• 21:05
  Analyst: Yes, because tensor parallelism exchanges activation data on every layer; if the link drops, inference stops entirely.
• 21:14
  Host: How do we monitor that link in production?
• 21:17
  Analyst: Run the cluster check tool before each launch and set up alerts on ibv_devinfo output for link loss.
• 21:24
  Host: What about the software side-how do we keep the engine healthy?
• 21:27
  Analyst: Use the smoke test after each deployment to confirm the correctness gate passes before exposing the API.
• 21:34
  Host: The smoke test runs a small request; does it check token limits too?
• 21:39
  Analyst: It verifies the model can generate a short completion and that the engine is reachable; token limits are covered in the benchmark suite.
• 21:47
  Host: Security is always a concern; what steps should we take with the open-API endpoint?
• 21:52
  Analyst: vLLM binds to 0.0.0.0 with no API key, so place the service behind a proxy or firewall that authenticates requests.
• 22:03
  Host: Would a service mesh help?
• 22:04
  Analyst: A mesh can enforce rate limits, TLS termination, and traffic routing to the 8888 port, adding a layer of protection.
• 22:13
  Host: If we need to scale beyond two units, what changes are required?
• 22:17
  Analyst: The same image and settings work on more ranks; you only need to launch additional nodes and adjust the launch script to include extra ranks.
• 22:25
  Host: Do we need a different cable for more units?
• 22:28
  Analyst: For a ring or switched fabric, you would use a switch or a multi-port cable; the recipe assumes a single direct link.
• 22:36
  Host: What about power consumption as we add units?
• 22:39
  Analyst: Two units draw 251 W at one user and 285 W at full load, giving 1.16 tokens per joule; adding units increases power but improves throughput.
• 22:52
  Host: Is the energy efficiency drop significant with four or eight units?
• 22:56
  Analyst: Four units maintain about 0.90 tokens per joule at 32 users, while eight units drop to 0.58 tokens per joule at the same load.
• 23:07
  Host: So choosing the right number of units depends on user load and energy goals.
• 23:11
  Analyst: Exactly; if you need a large context pool, eight units as one engine can help, but if you prioritize throughput, two or four units are more efficient.
• 23:22
  Host: What common mistakes do teams make when deploying this setup?
• 23:25
  Analyst: Skipping the CABLE_SUBNET variable will cause the launch to fail; always provide a subnet that the RDMA link actually uses.
• 23:34
  Host: Another mistake?
• 23:35
  Analyst: Assuming the script works for more than two units without adjusting the launch order; unit B must start first as rank 1, then unit A as rank 0.
• 23:45
  Host: How do we handle model updates?
• 23:47
  Analyst: Pull the new checkpoint revision, verify the image ID matches the measured one, redeploy the container, and run the smoke test again.
• 23:56
  Host: Does the container image change often?
• 23:58
  Analyst: The tag is latest, but the image ID is stable; check docker image inspect to confirm before updating.
• 24:06
  Host: Are there any recommendations for backing up the checkpoint?
• 24:09
  Analyst: Store a versioned copy on separate storage; the checkpoint resides on local disk for each unit, so keep an off-site backup.
• 24:17
  Host: What about software updates to vLLM or the underlying libraries?
• 24:21
  Analyst: Update the container image only after verifying that the new build uses the same GPU clock range and NCCL settings.
• 24:29
  Host: How do we ensure the GPU clocks stay in the 300-2300 MHz range during production?
• 24:34
  Analyst: Configure the GPU power limit in the container runtime or use nvidia-smi to lock the clocks before launching the engine.
• 24:43
  Host: If the clocks drift, what impact does that have?
• 24:46
  Analyst: It can reduce throughput and alter the measured token per joule figure, so keep them locked for consistent performance.
• 24:53
  Host: Do we need to monitor GPU temperature as well?
• 24:55
  Analyst: Yes, high temperatures can throttle performance; set up monitoring for GPU temps and fan speeds.
• 25:02
  Host: What about monitoring the inference metrics themselves?
• 25:05
  Analyst: Collect token per second, latency, and per-user floor metrics; compare them against the benchmark values to spot regressions.
• 25:14
  Host: If we see a drop in throughput, what should we check first?
• 25:18
  Analyst: Verify the RDMA link is healthy, confirm the GPU clocks are locked, and ensure no background processes are consuming GPU memory.
• 25:27
  Host: Speaking of memory, what is the maximum concurrency we can handle?
• 25:31
  Analyst: The recipe sets --max-num-seqs to 64 and --gpu-memory-utilization to 0.75, which balances concurrency with memory usage for the 131072-token context window.
• 25:45
  Host: Can we increase that number for higher concurrency?
• 25:48
  Analyst: You could, but you would risk exceeding GPU memory and causing evictions; the measured numbers are based on the 0.75 utilization setting.
• 25:58
  Host: What if our workload requires a larger context window than 131072 tokens?
• 26:03
  Analyst: The native model supports 262144 tokens, but serving the full length would reduce the number of concurrent full-length requests; consult with the team before increasing the context size.
• 26:15
  Host: Are there any security concerns beyond the open API?
• 26:18
  Analyst: Make sure the container runs with least privilege, restrict network access to the 8888 port, and use a firewall or API gateway to enforce authentication.
• 26:29
  Host: How do we handle rate limiting for multiple users?
• 26:32
  Analyst: Implement rate limits at the service mesh or gateway level; the engine itself does not enforce per-user limits.
• 26:39
  Host: What about handling very long prompts-do we need special tuning?
• 26:43
  Analyst: The prefill throughput remains flat across prompt sizes, so no additional tuning is required for longer inputs.
• 26:50
  Host: If we need to integrate this into a retrieval pipeline, what steps should we take?
• 26:55
  Analyst: Expose the OpenAI endpoint, let the pipeline submit queries, and use the 10-token-per-second floor to ensure interactive latency remains acceptable.
• 27:05
  Host: What if the pipeline generates many short requests; does that affect performance?
• 27:10
  Analyst: The engine can handle up to 64 sequences; however, you should monitor the per-user floor and ensure it stays above 10 tokens per second for a good experience.
• 27:20
  Host: Do we need to adjust the batch size for such workloads?
• 27:23
  Analyst: Batch size is internal to vLLM; the launch script sets concurrency parameters, so you don't need to modify batch size directly.
• 27:32
  Host: What questions do listeners often ask about this setup?
• 27:36
  Analyst: Common questions include how to choose the number of units, how to secure the endpoint, how to monitor RDMA, and how to handle model updates without downtime.
• 27:46
  Host: Any tips for answering those questions?
• 27:48
  Analyst: Explain the trade-offs between throughput and energy, emphasize the importance of the RDMA link, and show how to use the smoke test for zero-downtime updates.
• 27:58
  Host: Another frequent question is about data sovereignty-how does this work?
• 28:02
  Analyst: Because the model runs on local hardware and the checkpoint is stored locally, you maintain full control over data residency and can comply with regulatory requirements.
• 28:13
  Host: What about compliance with standards like CMMC?
• 28:16
  Analyst: The engine has been tested on a CMMC battery, scoring 0.978, which demonstrates strong alignment with compliance requirements.
• 28:25
  Host: Finally, what is the key takeaway for organizations looking to adopt this technology?
• 28:30
  Analyst: Invest in the proper hardware, secure the RDMA link, follow the launch script exactly, monitor health metrics, and use the smoke test to guarantee correctness before exposing the API.
• 28:43
  Host: Thank you for walking us through all of that.
• 28:45
  Analyst: Remember to keep the image ID stable and verify it with docker image inspect after pulling a new tag.
• 28:52
  Host: That ensures you are running the same configuration the benchmarks were measured with.
• 28:56
  Analyst: With those practices in place, you can confidently deliver a reliable, high-performance LLM service on premises.