GLM-5.3-Flash on 4x RTX PRO 6000 Blackwell — Karmic Kraken TP4 MTP3

Launch command

serve local-inference-lab/GLM-5.3-Flash-NVFP4 --additional-config ''\''{"glm53_kda_decode_backend":"auto","kda_prefill_backend":"b12x"}'\''' --attention-backend B12X --block-size 256 --compilation-config ''\''{"cudagraph_mode":"FULL_AND_PIECEWISE"}'\''' --cp-kv-cache-interleave-size 4 --cudagraph-capture-sizes 1 2 4 8 16 32 40 48 64 96 128 192 256 --dcp-kv-cache-interleave-size 4 --decode-context-parallel-size 1 --default-chat-template-kwargs ''\''{"reasoning_effort":"high","clear_thinking":false}'\''' --dtype bfloat16 --enable-auto-tool-choice --enable-chunked-prefill --no-enable-flashinfer-autotune --enable-prefix-caching --enable-prompt-tokens-details --gpu-memory-utilization 0.93 --host 0.0.0.0 --kv-cache-dtype fp8 --linear-backend b12x --load-format instanttensor --mamba-cache-mode align --max-cudagraph-capture-size 256 --max-model-len 1048576 --max-num-batched-tokens 4096 --max-num-seqs 32 --max-parallel-prefills 1 --moe-backend b12x --override-generation-config ''\''{"temperature":1.0,"top_p":0.95}'\''' --pipeline-parallel-size 1 --port 8000 --prefill-compute-share 0.4 --prefill-schedule-interval 1 --quantization modelopt_mixed --reasoning-parser glm45 --recurrent-checkpoint-policy request_boundaries --served-model-name GLM-5.3-Flash-NVFP4 --speculative-config ''\''{"method":"mtp","draft_sample_method":"probabilistic","rejection_sample_method":"standard","moe_backend":"marlin","attention_backend":"B12X","num_speculative_tokens":3}'\''' --tensor-parallel-size 4 --tool-call-parser glm47

Use this recipe with your agent

Paste this into any agent chat — it fetches and applies the recipe:

Fetch https://localairecipes.com/api/v1/recipes/glm-5-3-flash-4x6000-kk-mtp3.json and apply the inference recipe it describes (engine flags, model, quant, context) to my setup. Ask before overwriting any existing config.

Or fetch it directly:

curl -fsSL https://localairecipes.com/api/v1/recipes/glm-5-3-flash-4x6000-kk-mtp3.json

Install requirements

• Docker (or a compatible OCI container runtime)
• NVIDIA Container Toolkit (nvidia-container-toolkit) - exposes host GPUs to the container

NVIDIA driver floor: unknown - check the image tag

Source: NVIDIA CUDA Toolkit Release Notes - driver compatibility tables (https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/, fetched 2026-10-03).

Map the placeholders to your rig

• Hardware-bound values in this recipe (device counts, bind-mount paths, utilization, ports) describe the author's machine, not yours - map them to your own rig before launching. Keep the engine flags, model, quant, and parallelism strategy; adjust paths and ports to the hardware you actually have.
• GPU identity: list your GPUs with `nvidia-smi -L` and substitute your own GPU UUIDs/indices wherever the recipe pins the author's.
• Model directory: bind-mount paths are author-side - point them at your own mount where the model files live.
• Host port: pick a free port on your machine and publish the container there; keep the in-container port unchanged.

Revision notes

Benchmark-qualified configuration from local-inference-lab/rtx6kpro (Karmic Kraken serving report). Five warmed 30-second windows per cell on RTX PRO 6000 Blackwell Max-Q; decode ctx-0 temp 1. Launch argv transcribed verbatim from the repo's unified Docker guide (same composition). Measured: C1 281.3 tok/s, C8 897.6 tok/s, 32K prefill 13816 tok/s.

Revision history

• 7beb95bba1 recipe: update glm-5-3-flash-4x6000-kk-mtp3 (r2: /max_context_len) 10 d ago diff
• bfdf9daa3e history: re-squash corpus to a single root commit after the slug reconciliation (prior tip kept at refs/lar-archive/pre-single-slug-resquash) 25 d ago

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