Karti Tripathi
2584ea908e
ci / rust (push) Successful in 2m27s
Governed compute for unified-memory AI hardware — machines where CPU and GPU share one pool and there is no separate VRAM allocation to bounce off. Over-commit that pool and the box thrashes and wedges, SSH and ping included, before the OOM killer gets a turn. Compute does not run inference. It supervises the servers that do: - Admission control against two ceilings — a declared budget, and what the machine actually has free. The refusal is the feature. - A 1 Hz watchdog on MemAvailable that stops the newest model before thrash, and defers to a Scene transition rather than racing it. - Scenes: named sets of models activated as one transactional unit, with pre-flight validation and rollback to the previously active Scene on failure. Scenes reference model ids, never weight paths or commands, so a Scene obtained from elsewhere cannot introduce code. - Process ownership bound to (boot_id, pid, start_time_ticks, pgid == pid), so a reused PID can never be group-killed. - A protocol-transparent TCP gateway, so clients keep one address while model runtimes move behind it. - An MCP server, so agents drive the node as tools. - A read-only HTTP API for dashboards: loopback by default, CORS off unless an origin is named, and it never mutates. One binary, six direct dependencies, no async runtime outside the MCP and API surfaces. Apache-2.0. Generated from the internal monorepo by scripts/publish-compute.sh, which refuses to publish a tree it cannot prove clean.
180 lines
6.3 KiB
YAML
180 lines
6.3 KiB
YAML
apiVersion: lumbridge/v1
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kind: Registry
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version: 1
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# Local to this box. Ids are the stable contract that scenes reference; everything
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# under `serve` may evolve (new quants, backends, context). Footprints are worst-case
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# unified memory held once serving (weights + KV/cache + encoder).
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models:
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ears:
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name: "Nemotron 3.5 ASR streaming (0.6B)"
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footprint_gb: 5
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channel: stable
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health: "http://localhost:8006/health"
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serve:
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kind: python
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port: 8006
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command: ["~/asr-env/bin/python", "~/asr_server.py"]
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env:
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ASR_MODEL_PATH: "~/models/nemotron-asr-0.6b"
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brain:
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name: "Qwen3.6 35B-A3B MoE (NVFP4)"
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footprint_gb: 66 # 26GB weights + ~30GB KV for 64k ctx (fp8)
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channel: stable
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health: "http://localhost:8001/v1/models"
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health_contains: "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast"
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serve:
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kind: vllm
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executable: "~/vllm-env/bin/vllm"
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port: 8001
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weights: "~/models/Qwen3.6-35B-A3B-NVFP4-Fast"
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served_name: # stable aliases so downstream agents survive a weight swap
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- brain
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- "local-moe"
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- "unsloth/Qwen3.6-35B-A3B-NVFP4-Fast"
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- "unsloth/Qwen3.6-27B-NVFP4"
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args:
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max-model-len: 65536
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kv-cache-dtype: fp8
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gpu-memory-utilization: 0.55
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enforce-eager: true
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max-num-batched-tokens: 4096
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limit-mm-per-prompt: '{"image": 0, "video": 0}'
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enable-auto-tool-choice: true
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tool-call-parser: qwen3_coder
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reasoning-parser: qwen3
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moe-backend: flashinfer_b12x # Unsloth DGX Spark recipe; critical for speed
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env:
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CUTE_DSL_ARCH: sm_121a
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voice:
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name: "Chatterbox voice cloning (TTS)"
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footprint_gb: 4
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channel: stable
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health: "http://localhost:8095/health"
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serve:
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kind: uvicorn
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port: 8095
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command:
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- "~/cbx-env/bin/uvicorn"
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- "voice_api:app"
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- "--host"
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- "0.0.0.0"
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- "--port"
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- "8095"
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- "--app-dir"
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- "~/chatterbox"
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brain-laguna:
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name: "Poolside Laguna S 2.1 118B-A8B (NVFP4 + DFlash)"
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# 71 GB main weights + draft, runtime/JIT overhead, and a conservative
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# 64k FP8 KV allowance. This is a replacement for `brain`, never a peer.
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footprint_gb: 86
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channel: stable
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health: "http://localhost:8001/v1/models"
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health_contains: "poolside/Laguna-S-2.1-NVFP4"
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serve:
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kind: vllm
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executable: "~/laguna-env/bin/vllm"
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port: 8001
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weights: "~/models/Laguna-S-2.1-NVFP4"
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served_name:
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- brain
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- "local-moe"
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- "poolside/Laguna-S-2.1-NVFP4"
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args:
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default-chat-template-kwargs: '{"enable_thinking": false}'
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speculative-config: '{"model":"/srv/models/Laguna-S-2.1-DFlash-NVFP4","num_speculative_tokens":15}'
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max-model-len: 65536
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kv-cache-dtype: fp8
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gpu-memory-utilization: 0.70
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max-num-seqs: 8
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max-num-batched-tokens: 4096
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enforce-eager: true
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enable-auto-tool-choice: true
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tool-call-parser: poolside_v1
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reasoning-parser: poolside_v1
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override-generation-config: '{"temperature":0.7,"top_p":0.95}'
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env:
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CUTE_DSL_ARCH: sm_121a
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MAX_JOBS: "4"
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PATH: "/usr/local/cuda/bin:/usr/local/bin:/usr/bin:/bin"
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brain-gemma:
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name: "Gemma 4 31B-IT (NVFP4)"
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# 32.67 GB weights (4 shards, measured via HF repo metadata) + KV/activation
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# headroom under a 0.35 gpu-memory-utilization cap. Gemma's mostly-sliding-window
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# attention (10 of 60 layers are full-attention, rest capped at a 1024-token
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# window) means its real KV cost at 64k context is far lighter than brain/
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# brain-laguna's dense-attention KV — this cap is a conservative first pass,
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# not yet confirmed by an actual load on the reference node.
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footprint_gb: 44
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channel: stable
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health: "http://localhost:8001/v1/models"
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health_contains: "nvidia/Gemma-4-31B-IT-NVFP4"
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serve:
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kind: vllm
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executable: "~/gemma-env/bin/vllm"
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port: 8001
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weights: "~/models/Gemma-4-31B-IT-NVFP4"
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served_name:
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- brain
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- "local-moe"
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- "nvidia/Gemma-4-31B-IT-NVFP4"
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args:
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max-model-len: 65536
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kv-cache-dtype: fp8
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gpu-memory-utilization: 0.35
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quantization: modelopt
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max-num-seqs: 8
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max-num-batched-tokens: 4096
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enforce-eager: true
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# Tool-call/reasoning parser support for Gemma 4 is unconfirmed in vLLM
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# as of this writing — verify before relying on function-calling.
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env:
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CUTE_DSL_ARCH: sm_121a
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music:
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name: "ACE-Step 1.5 XL 4B sft (music generation)"
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footprint_gb: 24 # measured ~21GB resident live on the reference node; 24 declared for safety
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channel: stable
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health: "http://localhost:8010/health"
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serve:
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kind: exec
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port: 8010
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# 4B XL sft DiT + 1.7B LM (pt backend). Custom FastAPI wrapper (POST /generate -> WAV).
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# Runs as a systemd --user unit on the node today; this is the equivalent launch.
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command: ["~/ACE-Step-1.5/.venv/bin/python", "~/ace_step_server.py"]
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env:
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ACESTEP_CONFIG_PATH: acestep-v15-xl-sft
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ACESTEP_LM_MODEL_PATH: acestep-5Hz-lm-1.7B
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ACESTEP_LM_BACKEND: pt
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ACESTEP_API_PORT: "8010"
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ACESTEP_API_HOST: 0.0.0.0
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image:
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name: "FLUX.2-klein-9B (BF16)"
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# BFL's own card claims ~29GB VRAM, but an independent benchmark that
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# actually ran this checkpoint on DGX Spark GB10 measured 66 GB resident
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# (95s/image) — trust the on-hardware number for admission control until
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# we re-measure it ourselves. Non-commercial license (BFL FLUX license);
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# fine for personal use, not for anything monetized. Requires accepting
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# the license on HF and `hf auth login` before the weights can download.
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footprint_gb: 66
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channel: stable
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health: "http://localhost:8007/health"
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serve:
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kind: diffusers
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port: 8007
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command: ["~/flux-env/bin/python", "~/flux_server.py"]
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env:
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FLUX_MODEL_PATH: "~/models/FLUX.2-klein-9B"
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FLUX_API_PORT: "8007"
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# Alternatives, ready to register when wanted:
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# image-klein-4b -> FLUX.2-klein-4B, ~13GB, Apache-2.0 (commercial-safe), untested on Spark
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# image-qwen -> Qwen-Image 20B, Apache-2.0, ~63GB/212s measured on Spark, best in-image text rendering
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