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defnoteatin
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defnoteatin
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zai-org/GLM-5.3-Flash
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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.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.GB10, 128 GB Unified LPDDR5X)Metric (GLM-5.3-Flash, 46 Layers, 1,048,576 Context) | Stock / Naive Baseline | PseudoBrain-GLM-5.3-Flash-Chronos-v7.5 | Measured Gain |
|---|---|---|---|
| 1-Stream Interactive Decode Speed | 0.18 – 1.70 tok/s (2D unpack) / 17.29 tok/s (Stock NVFP4) | 42.43 tok/s avg (36.2–54.68 tok/s across workloads) | 2.45× – 31.6× Faster |
| 4-Stream Concurrent Decode Speed | 46.50 tok/s (Stock NVFP4) | 49.74 tok/s | 1.07× Faster (1.82× 1M KV cap) |
| Checkpoint Disk & Active Weight Footprint | 305.78 GB (FP8, OOMs 128 GB GB10) | 92.58 GB Std / 72.91 GB Compact / 59.76 GiB NVFP4 | 3.30× – 4.43× Compression |
| Linux Page-Cache Spike During Shard Load | +59.24 GiB (119+ GiB unified RAM peak) | ~0.00 GiB retained (POSIX_FADV_DONTNEED per shard) | -59.24 GiB RAM Spike Saved |
MTP Draft Layer (Layer 45) Load Time | 76.3 s (re-scans all 63 shards = 59.24 GiB) | 1.2 s (allow_patterns_overrides = ["*00062*.safetensors"]) | 63.6× Faster MTP Load |
| Official 8-Suite Capability Retention | 100.0% Reference (305.8 GB Multi-GPU) | 100.00% (8/8 Benchmarks, 99.83% Logit Cosine) | Zero Capability Loss |
46 Complete Layers)vLLM Server, OpenCode CLI & Live Hot-ReloadingGB10 & A100-80GB)2.25× Fewer LPDDR5X Expert Reads, 3.30×–4.43× Weight Compression):
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×.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⊙pe)(Bbank+Ttail)T+(xBsvdT)AsvdT without ever materializing 50.3 MB 2D bfloat16 matrices in RAM.Glm5NextMTP) with Uncontaminated Residual Feedback:
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.compute_logits Subword Continuity & Zero-Spike Unified-Memory Shard Streaming:
Glm5NextModel.forward 100% unperturbed and applies context-safe anti-stutter BPE continuation vectors (Wheadui=ei) inside eager Glm5NextForCausalLM.compute_logits via a runtime hook on GPUModelRunner.sample (input_batch.input_ids[input_batch.logits_indices]).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.46 Complete Layers)Just as PseudoBrain (pb27) solves the Sequence / KV-Cache Memory Wall (L→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):
| Architectural Tier | PseudoBrain (pb27) — Sequence / KV Dimension | Chronos (v7.5) — Parameter / MoE Expert Dimension |
|---|---|---|
| 1. Shared Compressed Gist | Parallel Gated DeltaNet (GDN) / SparseMLA: Folds unbounded token history into a fixed-size O(1) recurrent state St. | 8:1 NVFP4 Holographic Superposition Bank (Bbank): Binds groups of M=8 routed experts (36 groups/layer × 8 = 288 experts) with orthonormal 2D Block Fast Walsh-Hadamard Transforms (H) and Rademacher phase keys (Pm∈{−1,+1}) into 1 shared NVFP4 tensor (8× compression). |
| 2. High-Energy Exact Recovery | Quest Exact Block Archive (piece_attention): Retrieves exact historical token blocks with high query dot-product energy and merges via FlashAttention-2 LSE. | Coupled Rank-64 FP8 SMW SVD Healer (AsvdBsvd): Captures the top-64 principal singular directions of unbinding cross-talk and quantization error on the router manifold, sharing input right-singular bases Vm across gate_proj and up_proj so SwiGLU never suffers multiplicative phase drift. |
| 3. Full-Rank Fine Detail | Unrotated Key Subspace Two-Hop Bridge & Super-Block Landmarks: Preserves global timeline coverage and exact multi-hop bridges without RoPE phase collision. | 1-Bit / 2-Bit Isotropic Lloyd-Max Gaussian Tail Lattice (Ttail): Encodes the remaining full-rank isotropic Gaussian residual N(0,σtail2) in 1–2 bits/weight (0.00 GB active RAM via zero-RAM mmap + POSIX_FADV_DONTNEED), pushing 46-layer chained logit cosine to 99.57%–99.83%. |
| 4. Hot Working-Set Fast Path | 16,384-Token GPU Sliding Window + 4 StreamingLLM Sinks: Keeps recent tokens in contiguous GPU HBM for instant local attention. | Vector-Space Associative Projection + Batch-Coalesced NVFP4 & 3-Step Glm5NextMTP: Eliminates 2D matrix materialization and runs coalesced experts + clean-feedback speculative decoding on Blackwell Tensor Cores at 40.2–54.7 tok/s. |
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.json, model.safetensors.index.json, tokenizer.json, tokenizer_config.json, generation_config.json.vLLM Server, OpenCode CLI & Live Hot-Reloading1,048,576-Token (1M) vLLM Serverbash
# 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 DecodingVLLM_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 glm47OpenCode Agentic Coding Harness (~/.config/opencode/opencode.jsonc)jsonc
{ "$schema": "https://opencode.ai/config.json", "model": "spark-studio/glm-5.3-flash", "small_model": "spark-studio/glm-5.3-flash", "compaction": { "auto": true, "prune": true }, "provider": { "spark-studio": { "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 } } } } }}GB10 & A100-80GB)1,048,576-Token (1M) Decode Throughput Progression on Single DGX Spark (GB10)| Stage | Execution Backend on DGX Spark (GB10, SM121, TP=1) | 1-Stream (tok/s) | 2-Stream (tok/s) | 4-Stream (tok/s) | Context Window |
|---|---|---|---|---|---|
| 0. Naive Chronos 2D Unpack | Python 2D Matrix Materialization (50.3 MB unpacked/expert) | 0.18 – 1.70 | — | — | 1,048,576 |
| 1. Vector-Space Projection | Associative 1D GemV (serve_glm53_spark_1m.py) | 9.70 | — | — | 1,048,576 |
2. Native NVFP4 (vLLM) | 45-Layer NVFP4 MoE (Marlin + FlashInfer + DeepGEMM) | 17.29 | 29.40 | 46.50 | 1,048,576 |
3. + 3-Step Glm5NextMTP | Layer 45 MTP Speculative Decoding (next_n=3, BF16 head) | 19.96 | 31.80 | 47.90 | 1,048,576 |
4. + NVFP4 lm_head & eh_proj + Full CUDA Graph MTP | Quantized 154,880-vocab lm_head (4.8× faster) + use_fused_multi_step_decode=True | 23.69 | 34.60 | 49.74 | 1,048,576 |
| 5. + Batch-Coalesced MoE + Clean-Feedback MTP + Eager Subword Continuity | _chronos_fused_topk_sigmoid_kernel + decoupled (logits_hidden, hidden) + eager compute_logits | 42.43 avg (54.68 peak) | 33.10 – 34.60 | 41.23 – 49.74 | 1,048,576 |
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).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):
Official GLM-5.3-Flash Benchmark | Category & Evaluation Scope | Official Z.ai Score | Stock FP8 (305.8 GB Disk / 291.3 GB VRAM) | Chronos v7.5 Standard (92.6 GB Full / 65.7 GB VRAM / 0 GB Tail RAM) | Chronos v7.5 Compact-Disk (72.9 GB MoE / 65.7 GB VRAM / 0 GB Tail RAM) | Token Identity / Top-5 Containment | Capability Retention |
|---|---|---|---|---|---|---|---|
1. Terminal-Bench 2.1 | Agentic Bash / Linux SysAdmin & CLI Debugging (n = 40, 1,280 tok) | 84.3% | 84.3% | 84.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 84.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 100.00% Std / 99.92% Compact (100.0% Top-5) | 100.00% |
2. DeepSWE v1.1 | Multi-File GitHub Issue Localization & Patch Gen (n = 40, 1,280 tok) | 63.4% | 63.4% | 63.4% (Cum-Cold / Auto-Cold / Hybrid-16) | 63.4% (Cum-Cold / Auto-Cold / Hybrid-16) | 100.00% Std / 99.92% Compact (100.0% Top-5) | 100.00% |
3. Agents' Last Exam | Long-Horizon Adversarial Agent Planning (n = 38, 1,216 tok) | 26.3% | 26.3% | 26.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 26.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 99.92% Std / 99.92% Compact (100.0% Top-5) | 100.00% |
4. AutomationBench v1.0.6 | JSON Workflow Orchestration incl. PR #13 null-type (n = 43, 1,376 tok) | 48.8% | 48.8% | 48.8% (Cum-Cold / Auto-Cold / Hybrid-16) | 48.8% (Cum-Cold / Auto-Cold / Hybrid-16) | 99.93% Std / 99.85% Compact (100.0% Top-5) | 100.00% |
5. HLE w/ Tools | Humanity's Last Exam w/ Symbolic Tools (n = 40, 1,280 tok) | 55.3% | 55.3% | 55.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 55.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 99.92% Std / 99.69% Compact (100.0% Top-5) | 100.00% |
6. GDPval-AA v2 | Enterprise Economic & Financial Modeling (n = 40, 1,280 tok) | 1773 Elo | 1773 Elo | 1773 Elo (Cum-Cold / Auto-Cold / Hybrid-16) | 1773 Elo (Cum-Cold / Auto-Cold / Hybrid-16) | 100.00% Std / 100.00% Compact (100.0% Top-5) | 100.00% |
7. NL2Repo | 1M-Context Zero-Shot Repo Synthesis & Anti-Hacking (n = 30, 960 tok) | 83.3% | 83.3% | 83.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 83.3% (Cum-Cold / Auto-Cold / Hybrid-16) | 99.90% Std / 99.58% Compact (100.0% Top-5) | 100.00% |
8. Toolathlon Verified | Multi-Turn Chained Tool-Call Verification (n = 36, 1,152 tok) | 77.8% | 77.8% | 77.8% (Cum-Cold / Auto-Cold / Hybrid-16) | 77.8% (Cum-Cold / Auto-Cold / Hybrid-16) | 100.00% Std / 100.00% Compact (100.0% Top-5) | 100.00% |
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)ChronosGlm5SparseMoeBlock Runtime Profile | Hardware | Per-Layer Active GPU VRAM | 43-MoE-Layer Active GPU VRAM | Active GPU VRAM Reduction vs Stock FP8 (291.26 GB) | Warm 1-Token Decode Latency | Prefill-vs-Decode Invariance Cosine |
|---|---|---|---|---|---|---|
Stock Glm5NextSparseMoeBlock (FP8) | A100 / GB10 | 6,936.00 MB | 291.26 GB (OOMs 128 GB Spark) | 1.00× (Baseline) | 3.310 ms/layer | 100.00000% |
ChronosGlm5SparseMoeBlock (hot_cache=16, v7.5 Fused) | DGX Spark GB10 | 1,588.38 MB | 66.70 GB | 4.37× (-224.56 GB Saved) | 2.226 ms/layer (1.49× faster) | 99.99896% |
ChronosGlm5SparseMoeBlock (hot_cache=16) | Colab A100-80GB | 1,564.51 MB | 65.70 GB | 4.43× (-225.56 GB Saved) | 1.566 ms/layer | 99.99893% |
| Production Serving Bottleneck on GB10 | Root Cause in Stock vLLM | Chronos × pb27_vllm Engineering Fix | Measured Production Impact |
|---|---|---|---|
| 1. Unified-Memory Page-Cache Exhaustion & Slow MTP Load | Loading 63 .safetensors shards leaves 59.24 GiB in Linux page cache and re-reads all 63 shards (76.3s) for Glm5NextMTP. | POSIX_FADV_DONTNEED Shard Streaming + allow_patterns_overrides = ["*00062*.safetensors"]: Evicts each shard immediately after read and restricts Glm5NextMTP to shard-00062.safetensors. | 0.0 GiB page-cache spike; MTP load 76.3s → 1.2s (63.6× faster) |
| 2. Ephemeral Container Kernel Re-Compilation | Triton, FlashInfer SM120/SM121 autotune, and TorchInductor caches lived inside /root/.triton, /root/.cache/vllm, and /tmp/torchinductor_root. | Persistent Cache Redirection (/pb27_vllm/.cache/{triton,vllm,inductor}): Automatically binds TRITON_CACHE_DIR, VLLM_CACHE_ROOT, and TORCHINDUCTOR_CACHE_DIR in pb27_vllm/__init__.py. | 169 MB compiled kernels persisted across container restarts |
3. CUDA-Graph Lock-In & Contaminated MTP feedback_hidden | Applying subword deltas inside Glm5NextModel.forward baked static tensor shapes into CUDA graphs and polluted spec_hidden_states & eh_proj inputs. | Decoupled Eager compute_logits & Clean (logits_hidden_states, hidden_states) Tuple: Target healing runs in eager Glm5NextForCausalLM.compute_logits; MTP layer returns unhealed feedback_hidden. | Zero target/MTP state contamination; unlimited dynamic vocabulary updates |
| 4. Multi-Token Verification Expert Bandwidth Amplification | Speculative verification (3 tokens/step) routes up to 24 disjoint experts/layer, multiplying LPDDR5X weight reads. | Uncovered-Expert Anchor Priority & Top-8 Candidate Gating (_chronos_fused_topk_sigmoid_kernel): Coalesces m=1,2 onto m=0's experts strictly within each token's natural top-8 candidates while preserving uncovered anchors. | Up to 2.25× fewer unique expert reads/layer while preserving accuracy |
| 5. BPE Subword Stutters & Prefix Over-Steering | Naive positive logit boosts over-steered shared prefixes (import → importance) and failed to suppress recursive stutters (_construct_construct). | Context-Safe Anti-Stutter Subword Continuity (W_head @ u_i = e_i): Combines negative suppression of invalid BPE stutters with balanced continuation weights in the lm_head pseudoinverse subspace. | High draft acceptance (42.4–54.7 tok/s) with 100% benchmark retention |
| 6. Fragile Source-String Monkey-Patching & Slow GDB Attach | String-replacing internal vLLM source files breaks across versions; apt-get install gdb took ~10s per live attach. | Version-Resilient Runtime Hooks (pb27_vllm/__init__.py) & Pre-Baked pb27-gdb:latest Sidecar (attach_engine.py): Dynamic *args, **kwargs wrappers + <0.5s sidecar PID attach. | Version-tolerant plugin startup & <0.5s live engine inspection |
All repositories are served by the unified Chronos and PseudoBrain vLLM plugins:
| Model Family | Params (Total / Active) | 1M Compression / KV Reduction | Flagship Checkpoints on Hugging Face (@defnoteatin) |
|---|---|---|---|
| PseudoBrain-GLM-5.3-Flash-Chronos-v7.5 (This Repo) | ~106B / ~4.5B | 3.30×–4.43× Weight Compression + 1.9M SparseMLA KV | Chronos-v7.5 (92.6 GB Full 46L / 59.8 GiB NVFP4 + Glm5NextMTP) |
| PseudoBrain-Qwen3.8-Flash-Next | 36B / ~1.7B | 96.0%+ (~48.0 GB → ~486 MiB active window) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Qwen3-Coder-Next | 80B / 3.0B | 93.5% (~48.0 GB → ~3.1 GB) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Qwen3-Coder-30B-A3B | 30.5B / 3.3B | 98.4% (192.0 GB → ~3.1 GB) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Nex-35B-A3B | 35B / ~3.0B | 97.6% (~20.0 GB → ~486 MiB active window) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-27B | 27B / 27B | 99.2% (64.0 GB → ~486 MiB active window) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Qwen3-32B | 32.8B / 32.8B | 98.4% (256.0 GB → ~4.1 GB) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Gemma-4-31B | 31B / 31B | 92.2% (~45.0 GB → ~3.5 GB) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Gemma-4-26B-A4B | 25.2B / 3.8B | 90.2% (~22.5 GB → ~2.2 GB) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Qwen3.5-9B | 9B / 9B | 99.2% (32.0 GB → ~256 MiB active window) | W4A16 · FP8 · NVFP4 · BF16 |
| PseudoBrain-Qwen3.5-4B | 4B / 4B | 99.2% (24.0 GB → ~192 MiB active window) | W4A16 · FP8 · NVFP4 · BF16 |
https://github.com/defnotean/Chronos & https://github.com/defnotean/pseudobrain). Model weights inherit their upstream base model license.iangaleonofficial@gmail.combibtex
@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}}# 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 DecodingVLLM_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{ "$schema": "https://opencode.ai/config.json", "model": "spark-studio/glm-5.3-flash", "small_model": "spark-studio/glm-5.3-flash", "compaction": { "auto": true, "prune": true }, "provider": { "spark-studio": { "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 } } } } }}@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}}