Community GPU Benchmark and WAN GameDay

⚡ Opt-In Community GPU Benchmark & WAN GameDay (v1.8.0)

Have a consumer gaming PC, laptop, or workstation GPU (RTX 3060, RTX 3070, RTX 4060, RTX 4070, RTX 4090, ARC, Radeon, or Apple Silicon / CPU fallback)? You can help validate Project HiveMind's 8 GB VRAM invariant, 128-Expert 3-Tier Hierarchical MoE, FP8 KV-Cache Pager, and EF21-DiLoCo outer sync on real community hardware!

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⚖️ Non-Monetary Compute-Reciprocity Charter (0.00 EUR / USD)

• 100% Opt-In & Local: Runs in under 30 seconds entirely on your local machine; makes zero external network calls and uploads no personal data.
• No Financial Payment (0.00 EUR / USD): Submitting a benchmark receipt (+500 PTS) or joining a WAN GameDay (+1,000 PTS) provides no monetary payout. Points (PTS / $HIVE) grant reciprocal computing processing benefits (priority distributed Sparse MoE inference & QLoRA fine-tuning slots) when the live production HiveMind Swarm is deployed and used by a large community.

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1. Run the Opt-In Community GPU Benchmark (< 30 Seconds)

Implemented in src/hivemind/cli/community_gpu_benchmark.py and scripts/run_community_gpu_benchmark.py, the benchmark executes 5 real PyTorch/HiveMind workloads:

1. Workload 1 — 4-Bit NF4 + Double Quantization & QLoRA Forward/Backward (src/hivemind/core/qlora_4bit.py): Measures quantization compression ratio, reconstruction error, and step throughput (tokens/sec).
2. Workload 2 — 128-Expert 3-Tier Hierarchical MoE + Speculative Token-Tree Prefetch (src/hivemind/core/btm_moe.py): Measures 4 x 8 x 4 routing latency (ms), active parameter sparsity (16x), and speculative cache hit rate.
3. Workload 3 — FP8 (E4M3FN) Paged KV-Cache (src/hivemind/core/swarm_fabric.py): Verifies that peak MoE + KV-cache memory stays strictly < 2,252.8 MB (2.2 GB) at 4,096 context length.
4. Workload 4 — Stateful EF21-DiLoCo Top-2% Sparse Outer Sync (src/hivemind/p2p/gossip_averaging.py): Measures error-feedback compression throughput and residual contractive decay.
5. Workload 5 — Interactive < 1.5s Game Preemption (src/hivemind/watchdog/game_detector.py): Measures checkpoint pause-and-yield latency (ms) when a foreground game process is detected.

Execute the Benchmark

# Quick tier (~5-10 seconds, works on CUDA GPU or CPU fallback)
PYTHONPATH=. python scripts/run_community_gpu_benchmark.py --tier quick \
  --contributor-handle "@your_github_handle"

# Standard tier (~20-30 seconds, deeper batch & context sweep)
PYTHONPATH=. python scripts/run_community_gpu_benchmark.py --tier standard \
  --contributor-handle "@your_github_handle" \
  --output artifacts/community_gpu_benchmark_receipt.json

Share Your Receipt in Discussion #84 (+500 PTS)

Copy the Markdown table and JSON receipt printed by scripts/run_community_gpu_benchmark.py (including receipt_sha256) and post it in GitHub Discussion #84 — Opt-In Community GPU Benchmark Receipts Board! The 3-Hour Autonomous Orchestrator will verify your receipt_sha256 and credit +500 PTS (+1,000 PTS for multi-host WAN GameDay receipts).

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2. Run the Multi-Host WAN QUIC + TURN Benchmark (scripts/run_wan_quic_turn_benchmark.py)

To benchmark aioquic 0-RTT session resumption, BBRv1 pacing, and RFC 5766 TURN ChannelBind (0x4000 fast-path framing, PR #63, PR #77) under simulated 1%–5% packet loss and CGNAT:

PYTHONPATH=. python scripts/run_wan_quic_turn_benchmark.py

This generates artifacts/wan_quic_turn_benchmark_report.json with measured 0-RTT handshake latency, TURN ChannelBind header overhead reduction (77.8% savings: 36 B -> 8 B), and AdaptiveDiLoCoCadenceController (SI-03) cadence adaptation (H=11 on regional QUIC vs H=226 on TURN relay).

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3. 🚀 Operation Polyglot-Singularity-128: Large-Scale 8-Domain Hugging Face & 128-Expert Multi-MoE GPU Campaign (HM-S7-09 / HM-S7-10)

To validate the Branch-Train-Merge (BTM) + 128-Expert 3-Tier Sparse MoE + EF21-DiLoCo + Reputation-Weighted TIES (SI-04) thesis at scale on real UTF-8 token cross-entropy (nats/token, perplexity), run scripts/run_multi_moe_hf_campaign.py (src/hivemind/cli/multi_moe_hf_campaign.py):

The 8 Curated Hugging Face Datasets & Subjects (4 Super-Clusters)

Super-Cluster	Domain Specialist	Hugging Face Dataset	Scientific / Engineering Subject
CLUSTER-00-SYSTEMS-FORMAL	Rust_Systems_Kernel	bigcode/the-stack-v2-dedup (Rust)	Zero-copy DMA rings, lock-free atomics & borrow-checker invariants
CLUSTER-00-SYSTEMS-FORMAL	Lean4_Formal_Mathematics	hoskinson-center/proofnet	Machine-checked Lean 4 theorems for Byzantine quorums & contraction bounds
CLUSTER-01-CRYPTO-SECURITY	ZK_Cryptography_And_Audits	ASSERT-KTH/DISL-Solidity-Smart-Contracts	ZK-OPoI v4 4-way Merkle spot checks & L2 state-channel escrow audits
CLUSTER-01-CRYPTO-SECURITY	Cyber_Vulnerability_Patching	SWE-bench/SWE-bench_Verified	Automated CWE vulnerability remediation & spectral backdoor (FL-08) defense
CLUSTER-02-GPU-ML-KERNELS	Triton_CUDA_Kernel_Synth	ScalingIntelligence/KernelBench	Fused Triton/CUDA NF4 dequantization & FP8 paged KV-cache kernels
CLUSTER-02-GPU-ML-KERNELS	Distributed_DiLoCo_Protocols	HuggingFaceFW/fineweb-edu	Stateful EF21-DiLoCo outer sync, Kademlia DHT & QUIC 0-RTT transport
CLUSTER-03-FRONTIER-SCIENCE	Biomedical_Clinical_Genomics	qiaojin/PubMedQA	CRISPR-Cas9 off-target genomics, clinical trial QA & oncology pathways
CLUSTER-03-FRONTIER-SCIENCE	Astrophysics_JWST_Spectroscopy	MultimodalUniverse/multimodal_universe	JWST transmission spectroscopy biosignatures ($\text{H}_2\text{O} + \text{CH}_4/\text{CO}_2$) & Kepler transits

Run the 8-Domain Multi-MoE Campaign on Your GPU

# Standard 8-domain campaign (12 steps/domain, real UTF-8 token cross-entropy + 128-Expert MoE upcycling)
PYTHONPATH=. python scripts/run_multi_moe_hf_campaign.py \
  --contributor-handle "@your_github_handle" \
  --tier standard

# Optional: stream extra live rows directly from Hugging Face Datasets Server API
PYTHONPATH=. python scripts/run_multi_moe_hf_campaign.py \
  --contributor-handle "@your_github_handle" \
  --tier deep \
  --live-hf-stream

Post your generated Markdown table and campaign_sha256 receipt in GitHub Discussions (+1,500 PTS non-monetary compute-reciprocity credits; 0.00 EUR / USD).

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4. 🛰️ Season 9 & Season 10 GPU + HiveMind Swarm Contributor Campaigns (v2.5.0)

Implemented in src/hivemind/cli/season9_10_gpu_swarm_campaigns.py and scripts/run_season9_10_gpu_swarm_campaigns.py, Season 9 (Wave 8 — Operation Decoupled-Quorum) and Season 10 (Wave 9 — Operation Lattice-Codec) introduce 4 hardware-verified GPU + HiveMind Swarm campaigns:

Campaign ID	Tasks & Research Gates	Subsystems Benchmarked on Contributor GPU / HiveMind Node	Symbolic Compute Credits (0.00 EUR/USD)
CAMP-S9-E5-QUORUM-RDA-GPU	HM-S9-01, HM-S9-05, HM-S9-13, HM-S9-14 (G1/G2)	Decoupled Quorum (K < M), Continuous Async Straggler Folding (drop vs deferred_token_weighted vs eager_one_step), Outer-Momentum Restart (K_restart), and Pairwise Delta-Cosine Radial-Directional Averaging (RDA)	+3,500 PTS (Compute Quota Only)
CAMP-S9-E7-SIGMA-LOOPED-MOE-GPU	HM-S9-02, HM-S9-03 (G1)	128-Expert 3-Tier Sparse MoE with FoMoE sigma_moe Sigmoid Routing + Peri-LN, measured ghost-expert Skip-Token FLOP accounting (O_e = 4), and LoopedExpertBackbone (OrthoInj, LearnedDepthPrior, Terminal-KV Sharing)	+3,500 PTS (Compute Quota Only)
CAMP-S10-E11-LATTICE-KV-WIRE-GPU	HM-S10-01, HM-S10-05 (G1)	Conway-Sloane E8 / Z / sign Lattice Delta Quantizer in ComputeVisibleSparseLoCoCompressor + FP8PagedKVCache 5-Codec Benchmark (fp8_e4m3, a2_rope_plane, e8_lattice, scalar_2bit pos-control, a2_8bit neg-control)	+3,500 PTS (Compute Quota Only)
CAMP-S10-E12-HETERO-SPOTCHECK-WAN-GPU	HM-S9-04, HM-S9-06, HM-S10-08 (G2/G3)	Execution-class-matched DeterministicSpotCheckValidator (two-statistic linf + tensor-fraction rule & Q16 fixed-point SHA-256 digest), Heartbeat CAS Shard Leasing (committed == granted - reclaimed), and Factored Gossip Mix1/Mix2	+3,500 PTS (Compute Quota Only)

Run All 4 Season 9 & 10 GPU Swarm Campaigns (< 20 Seconds)

PYTHONPATH=src python scripts/run_season9_10_gpu_swarm_campaigns.py \
  --contributor-handle "@your_github_handle" \
  --device auto

Active GPU Contributor Roster & Guild Assignments

Contributor	Guild	GPU / Accelerator Profile	Primary Assigned Campaigns
@clark719664	GUILD-01-EU-STEM-SENTINELS	NVIDIA Consumer RTX GPU (<= 6,656 MiB WDDM) + Linux CUDA Node	CAMP-S9-E5-QUORUM-RDA-GPU, CAMP-S9-E7-SIGMA-LOOPED-MOE-GPU
@radialmonster	GUILD-02-GLOBAL-ZK-GUARDIANS	NVIDIA GeForce RTX Consumer GPU + Multi-Thread x86_64 Validator	CAMP-S10-E11-LATTICE-KV-WIRE-GPU, CAMP-S10-E12-HETERO-SPOTCHECK-WAN-GPU
@pedromineo	GUILD-03-BIOMED-OPEN-SCIENCE	Multi-Vendor GPU (CUDA / ROCm / MPS) & WAN Relay Node	CAMP-S9-E5-QUORUM-RDA-GPU, CAMP-S10-E12-HETERO-SPOTCHECK-WAN-GPU
@xaviercallens	CORE-COORDINATOR-SWARM	Local Coordinator Host + GCP TPU v5e (gwenlaya-tpu-1, us-west4-a)	All 4 Season 9 & 10 GPU Swarm Campaigns + Multi-Host WAN Pilot (E8)

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5. Related Wiki Pages & Discussions

• Consumer GPU Node Setup & Steam Tool
• Architecture & 5-Layer Stack
• Discussion #84 — Community GPU Benchmark Receipts Board
• Discussion #85 — Human & AI Agent Coordination Hub