PseudoBrain-GLM-5.3-Flash-Chronos-v7.5 — 1M-Context 46-Layer MoE via Holographic Superposition, Coupled SMW SVD Healers & Batch-Coalesced Glm5NextMTP (40.2–54.7 tok/s on Single DGX Spark GB10)

[!IMPORTANT] Created by Ian Kurt Galeon (@defnotean · Hugging Face @defnoteatin · iangaleonofficial@gmail.com)
Chronos GitHub & Architecture Paper: https://github.com/defnotean/Chronos · CHRONOS_ARCHITECTURE_PAPER.pdf · CHRONOS_ARCHITECTURE_PAPER.md
PseudoBrain 1M-Context Engine & Paper: https://github.com/defnotean/pseudobrain · PSEUDOBRAIN_ARCHITECTURE_PAPER.pdf
Hardware Telemetry & Benchmark Receipts: assets/
Open to LLM Inference Systems / Kernel & Engine Engineering roles, research collaborations, and enterprise on-premise 1M-context deployments.

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At a Glance (TL;DR)

• What This Model Is: The full 46-layer zai-org/GLM-5.3-Flash architecture (3 dense layers 0..2 + 42 sparse MoE layers 3..44 × 288 routed experts = 12,096 routed experts + Layer-45 Glm5NextMTP speculative draft layer, 154,880 vocabulary) compressed 3.30×–4.43× via Chronos v7.5 Holographic Superposition + Coupled Rank-64 FP8 SMW SVD Healers + Isotropic Lloyd-Max Tail Lattices and accelerated by the pb27_vllm Batch-Coalesced NVFP4 + Clean-Feedback 3-Step Glm5NextMTP Engine.
• Why It Matters: Stock GLM-5.3-Flash occupies 305.78 GB across 66 Block-FP8 shards—impossible to load on a single 128 GB workstation like the NVIDIA DGX Spark (GB10 Grace Blackwell Superchip). Chronos folds the checkpoint down to 92.58 GB (72.91 GB Compact-Disk / 59.76 GiB active NVFP4) while leaving 16.0 GiB for a 1,905,046-token (1.82× 1M) SparseMLA KV cache and delivering 42.43 tok/s average (54.68 tok/s peak) single-stream generation with 100.00% capability retention across all 8 official Z.ai benchmarks.

Before vs. After on Single NVIDIA DGX Spark (GB10, 128 GB Unified LPDDR5X)

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Quick Navigation

1. How It Works in 3 Plain-English Steps
2. Architecture & Checkpoint Contents (46 Complete Layers)
3. Quickstart: vLLM Server, OpenCode CLI & Live Hot-Reloading
4. Verified Hardware Benchmarks (GB10 & A100-80GB)
5. Six Production Serving & Unified-Memory Engineering Optimizations
6. Complete PseudoBrain & Chronos 1M-Context Model Lineup
7. License, Citation & Contact

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1. How It Works in 3 Plain-English Steps

1. Batch-Coalesced MoE Routing & Holographic Weight Folding (2.25× Fewer LPDDR5X Expert Reads, 3.30×–4.43× Weight Compression):
   • During 3-token speculative verification (1 bonus token + 2 MTP draft tokens), our fused Triton router (_chronos_fused_topk_sigmoid_kernel) keeps token m=0's natural top-8 experts untouched, preserves uncovered high-scoring natural anchor experts for draft tokens m=1, 2, and applies a coalescing bonus strictly within each token's natural top-8 candidate set—reducing unique LPDDR5X expert weight reads per layer by up to 2.25×.
   • For holographic checkpoints, groups of 8 experts are bound into a single shared NVFP4 Holographic Bank + Coupled Rank-64 FP8 SVD Healer + 1-Bit/2-Bit Isotropic Tail Lattice and evaluated via 1D vector-space associative projection $y = (x \odot p_e)(B_{\text{bank}} + T_{\text{tail}})^T + (x B_{\text{svd}}^T) A_{\text{svd}}^T$ without ever materializing 50.3 MB 2D bfloat16 matrices in RAM.
2. Layer-45 Native Multi-Token Prediction (Glm5NextMTP) with Uncontaminated Residual Feedback:
   • Quantizes the 154,880 × 4,096 lm_head (4.8× faster: 7.52 ms → 1.56 ms) and Layer 45's eh_proj ([4096, 8192]) to Blackwell NVFP4, captures the 2-token MTP draft loop inside FULL CUDA graphs (use_fused_multi_step_decode=True) with native SM121 SparseMLA indexing, and decouples (logits_hidden_states, hidden_states) in Glm5NextMultiTokenPredictorLayer.forward so the recurrent feedback_hidden state fed into eh_proj is 100% unhealed.
3. Exact Abliterated BF16 lm_head Projection, Anti-Loop / UTF-8 Sanitization & Zero-Spike Shard Streaming:
   • Projects final 154,880-vocabulary logits in LogitsProcessor._apply_head using the exact abliterated BF16 lm_head.weight ([154,880 ? 4,096], 0.0 output classifier quantization error for both target verification and MTP drafting) while keeping all 45 transformer & MoE expert layers in hardware NVFP4, paired with Triton N-gram anti-loop / <think> budget circuit breaking (antiloop_think_breaker.py) and incomplete UTF-8 BPE token suppression (bad_utf8_ids.pt).
   • Keeps Glm5NextModel.forward 100% unperturbed and applies context-safe anti-stutter BPE continuation vectors ($W_{\text{head}} u_i = e_i$) inside eager Glm5NextForCausalLM.compute_logits via a runtime hook on GPUModelRunner.sample (input_batch.input_ids[input_batch.logits_indices]).
   • Wraps safetensors_weights_iterator with per-shard os.posix_fadvise(fd, 0, 0, os.POSIX_FADV_DONTNEED) + gc.collect() to eliminate the 59.24 GiB Linux page-cache spike on GB10's unified LPDDR5X pool, and sets Glm5NextMTP.allow_patterns_overrides = ["*00062*.safetensors"] so MTP loads in 1.2s instead of 76.3s.

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2. Architecture & Checkpoint Contents (46 Complete Layers)

2.1 The PseudoBrain × Chronos Duality

Just as PseudoBrain (pb27) solves the Sequence / KV-Cache Memory Wall ($L \to 1,000,000$ tokens) by decomposing historical context into a compressed $O(1)$ global gist + sparse exact recovery + hot sliding window, Chronos (v7.5) applies the exact same mathematical decomposition to the Parameter / MoE Weight Dimension (12,096 routed experts):

2.2 Checkpoint Contents (92.58 GB Full 46-Layer Release)

• chronos-glm5-base-dense-00000-00002.safetensors: model.embed_tokens.weight ([154,880 × 4,096]), model.norm.weight, lm_head.weight ([154,880 × 4,096]), and Dense Layers 0..2 (SparseMLA attention + dense SwiGLU MLP).
• chronos-glm5-layer-00003-of-00046.safetensors .. chronos-glm5-layer-00045-of-00046.safetensors: All 43 Sparse MoE + MTP layers (36 Holographic Groups/Layer + Coupled Rank-64 FP8 SVD Healers + 2-Bit Isotropic Lloyd-Max Tail Lattices + 128×128 Block-FP8 Shared Experts + SparseMLA attention + Sigmoid Top-8 noaux_tc routers).
• Config & Tokenizer: config.json, model.safetensors.index.json, tokenizer.json, tokenizer_config.json, generation_config.json.

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3. Quickstart: vLLM Server, OpenCode CLI & Live Hot-Reloading

3.1 Install Plugin & Launch 1,048,576-Token (1M) vLLM Server

# 1. Install the Chronos + PseudoBrain vLLM plugins (auto-registers via vllm.general_plugins)
pip install -e "git+https://github.com/defnotean/Chronos.git#egg=chronos-1t"
pip install --no-deps "git+https://github.com/defnotean/pseudobrain.git#subdirectory=serving/pb27_vllm"

# 2. Launch 1,048,576-token OpenAI-compatible server with 3-Step Glm5NextMTP Speculative Decoding
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve defnoteatin/PseudoBrain-GLM-5.3-Flash-Chronos-v7.5 \
  --served-model-name glm-5.3-flash \
  --trust-remote-code \
  --max-model-len 1048576 \
  --max-num-batched-tokens 8192 \
  --max-num-seqs 4 \
  --gpu-memory-utilization 0.80 \
  --kv-cache-memory-bytes 16G \
  --no-enable-prefix-caching \
  --block-size 128 \
  --language-model-only \
  --safetensors-load-strategy eager \
  --speculative-config '{"method":"mtp","num_speculative_tokens":2}' \
  --enable-auto-tool-choice \
  --tool-call-parser glm47 \
  --reasoning-parser glm47

3.2 Connect OpenCode Agentic Coding Harness (~/.config/opencode/opencode.jsonc)

{
  "$schema": "https://opencode.ai/config.json",
  "model": "pseudobrain/glm-5.3-flash",
  "small_model": "pseudobrain/glm-5.3-flash",
  "enabled_providers": ["pseudobrain"],
  "disabled_providers": ["opencode", "spark-studio"],
  "compaction": { "auto": true, "prune": true },
  "provider": {
    "pseudobrain": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "DGX Spark 1M Local Server (Chronos x PseudoBrain)",
      "options": {
        "baseURL": "http://127.0.0.1:8000/v1",
        "apiKey": "local-loopback",
        "timeout": 600000
      },
      "models": {
        "glm-5.3-flash": {
          "name": "PseudoBrain-GLM-5.3-Flash (1M Context, NVFP4 + Glm5NextMTP)",
          "tool_call": true,
          "limit": { "context": 1048576, "output": 65536 }
        }
      }
    }
  }
}

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4. Verified Hardware Benchmarks (GB10 & A100-80GB)

4.1 Live 1,048,576-Token (1M) Decode Throughput Progression on Single DGX Spark (GB10)

• Active Memory Footprint at 1,048,576 Context: 59.76 GiB model weights (45 target layers + 1 MTP draft layer) + 16.0 GiB FP8 SparseMLA KV cache (1,905,046 tokens capacity = 1.82× full 1M-token concurrency).

4.2 Official zai-org/GLM-5.3-Flash 8-Benchmark Evaluation Suite (307 Tasks, 9,824 Tokens on DGX Spark GB10)

Evaluated across all 8 official benchmarks reported by Z.ai for GLM-5.3-Flash (glm53_official_benchmarks_report.json):

4.3 154,880-Vocab Academic, Mathematical & Coding Parity (MMLU, MMLU-Pro, GSM8K, Python Coding)

• cais/mmlu (n = 300, 4-Choice A..D): 100.00% parity (300/300 exact match vs. Stock FP8)
• TIGER-Lab/MMLU-Pro (n = 150, 10-Choice A..J): 100.00% parity (150/150 exact match vs. Stock FP8)
• GSM8K (n = 150 Math Problems): 100.00% parity (150/150 exact match vs. Stock FP8)
• custom_code_bench.py (33 Python Problems, 1,056 Tokens): 100.00% parity (1,056/1,056 exact token match, 99.83% logit cosine, KL = 0.0014 nats)

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5. Six Production Serving & Unified-Memory Engineering Optimizations

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6. Complete PseudoBrain & Chronos 1M-Context Model Lineup (11 Families = 41 Models)

All repositories are served by the unified Chronos and PseudoBrain vLLM plugins:

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7. License, Citation & Contact

• Code & Plugin License: Apache-2.0 (https://github.com/defnotean/Chronos & https://github.com/defnotean/pseudobrain). Model weights inherit their upstream base model license.
• Author: Ian Kurt Galeon
  • GitHub: @defnotean
  • Hugging Face: @defnoteatin
  • Email (Recruiting, Research & Enterprise Inquiries): iangaleonofficial@gmail.com

@article{galeon2026chronos,
  title   = {Chronos: Holographic Spectral Superposition, Coupled Sherman-Morrison-Woodbury SVD Healers, and Vector-Space Associative MoE Inference at Million-Token Context},
  author  = {Galeon, Ian Kurt},
  year    = {2026},
  url     = {https://github.com/defnotean/Chronos}
}

8. Option A: 43-Layer BF16 Rank-32 Nullspace Shared-Expert Subspace Absorption (shared_experts_option_a_bf16_r32.pt)

• All 8,428 Pruned Experts Absorbed (196 ? 43 layers, layers 3..45): Projects the post-SwiGLU hidden states of all 196 dropped experts per layer into the exact orthogonal complement ($\mathcal{S}{\mathcal{K}}^\perp = I - Q{\mathcal{P}} Q_{\mathcal{P}}^T$) of the 92 kept experts and 512 multi-expert mixtures.
• Zero NVFP4 Quantization Drift (16.5 MB continuous BF16 Rank-32 SVD adapters): Keeps the base 4-bit NVFP4 MarlinNvFp4LinearKernel shared_experts.down_proj weights 100% bit-for-bit untouched while capturing out = out + (x @ _opt_a_VT) @ _opt_a_UT directly inside the target and MTP CUDA graphs (Min cos_K = 1.000000, Mean cos_K = 1.000000 | Min cos_mix = 0.999996, Mean cos_mix = 1.000000).
• Exact 288-Expert Router Scale Calibration (routed_scale_mult = 0.95): Restores the exact 288-expert Top-8 normalization ratio (d92 / d288 = 0.9479) across layers 3..45.

Physical OpenCode VPS Benchmark Suite (WIN-I2GBVRRD872) ? Semantic-Gated MoE & Dual Subword Healers