Your Outcome
By the end of this recipe you'll have a single DGX Spark serving Qwen3.8-27B — a 27-billion-parameter reasoning model — through vLLM at high decode throughput, with 128K-token context, native tool calling, and chain-of-thought reasoning. One desktop. No cluster. No RoCE fabric. No speculative-decoding patches.
This is the accessible tier of our DGX Spark line: same hardware class as the dual-node DeepSeek-V4-Flash blueprint, but a model that fits one box with room to spare.
Why Qwen3.8-27B on One Spark?
| | This Recipe (Qwen3.8-27B) | Blueprint (DeepSeek-V4-Flash) | |---|---|---| | Model | Qwen3.8-27B (27B params) | DeepSeek-V4-Flash (671B MoE) | | Nodes | 1 × DGX Spark | 2 × DGX Spark (TP2) | | Interconnect | None (single GPU) | 200 Gbps RoCE | | Tensor parallelism | 1 (no split needed) | 2 across nodes | | Speculative decoding | Optional / not required | DSpark MTP-5 (required for speed) | | Correctness patches | Standard vLLM path | 21+ DSpark patches | | Context | 128K+ with large KV headroom | 200K (KV-OOM at 1M) | | Throughput | High single-node decode | 160–190 tok/s aggregate @ c=6 | | Complexity | Minimal | Substantial | | Price | €79 | €149 |
A 27B model at FP8 weighs ~27 GB; at NVFP4/INT4 it's ~14 GB. On a 128 GB unified-memory GB10 that leaves 90+ GB for the KV cache — enough for very long contexts and high concurrency without the memory juggling a 671B model demands.
The Problem This Solves
You want frontier-grade reasoning and tool use on hardware you already own, without standing up a two-node fabric. Most "run a big model locally" guides assume you're fighting for every gigabyte. Qwen3.8-27B flips that: it fits one Spark with margin to spare, so the configuration is about throughput and context, not survival.
No NCCL-over-RoCE debugging. No request-stable KV-slot patches. No gpu-memory-utilization values that freeze the machine. Just a standard vLLM deployment, tuned for the GB10.
What This Recipe Does
This is the exact single-node configuration we run for Qwen3.8-27B, version-pinned and crash-tested:
- Ansible playbook for one DGX Spark: vLLM service deployment, OOM safety, and node_exporter monitoring
- The production
qwen3-27b-wrapper.shwith every env var, every flag, and every GB10-specific workaround documented - OOM survival guide — the 7 mitigations that prevent GB10 unified-memory freezes (swap disable, overcommit off, memory reserves, Docker limits, page cache drop, readahead tuning)
- Quantization guide — FP8 vs NVFP4/INT4 trade-offs, and the exact
gpu-memory-utilizationthat maximizes KV cache without risking a freeze - Troubleshooting guide — every failure we hit (TileLang OOM during JIT, MTP drafter OOM, context-length KV errors) and the exact fix
- Performance baseline — decode throughput, prefill curves, prefix-cache impact, and concurrency scaling
What You'll Be Able To Do
- Serve Qwen3.8-27B at FP8 (or NVFP4) precision on one Spark — high decode throughput, 128K+ context, tool calling, chain-of-thought reasoning
- Deploy with Ansible — one command provisions the node, configures the service, and starts vLLM
- Monitor with Prometheus + Grafana — real-time GPU utilization, tok/s throughput, KV cache pressure
- Scale concurrency — the large KV-cache headroom supports many parallel short chats, or a few very long ones
- Handle OOM safely — the 7 mitigations that prevent the dreaded GB10 system freeze
- Upgrade model recipes — swap Qwen3.8-27B for Qwen3.5-122B FP8 or MiniMax M2.7 NVFP4 with one config change (subject to memory fit)
Who Will Benefit
- Engineers with one DGX Spark who want frontier-grade reasoning without a second node
- Teams building sovereign AI infrastructure with regulatory requirements for on-prem inference
- Anyone who hit the GB10 OOM freeze and needs a known-good, low-risk configuration
- Developers running agentic coding or analysis workflows who need tool calling + reasoning locally
- Organizations that want to validate the DGX Spark platform before committing to a dual-node build
What Success Looks Like
You have a single vLLM instance serving Qwen3.8-27B at http://spark:8000 with an OpenAI-compatible API. Short prompts decode at high tok/s; long documents prefill quickly into a generously sized KV cache. Your Ansible playbook redeploys the node from scratch in minutes. Prometheus graphs show stable GPU utilization and comfortable KV-cache headroom. The node survives reboots and concurrent load without crashing.
What's Inside
| Component | What You Get |
|-----------|-------------|
| Ansible playbook | vLLM service, OOM safety, node_exporter monitoring |
| Wrapper script | Production qwen3-27b-wrapper.sh with every flag documented |
| Systemd service | vllm-qwen3-27b.service with proper lifecycle management |
| OOM safety guide | 7 mitigations + sysctl configuration + Docker memory limits |
| Quantization guide | FP8 vs NVFP4/INT4, gpu-memory-utilization tuning for max KV cache |
| Troubleshooting | Every failure mode we encountered and the exact fix |
| Performance data | Decode/prefill throughput curves, prefix-cache benchmarks, concurrency scaling |
Format & Delivery
- Complete Ansible role with tasks, templates, handlers, and defaults
- Copy-paste wrapper script with inline documentation
- Reference architecture diagram (single-node)
- Immediate digital download (.zip)
Ready for the full dual-node frontier build? The Dual DGX Spark Cluster Blueprint deploys DeepSeek-V4-Flash (671B) across two Sparks with TP2, RoCE, speculative decoding, and full observability.
