More than a single AI tool — enterprise digital compute infrastructure
One engine, every model · Full-stack offline acceptance
Built on our proprietary V2 universal inference engine, PanEngine is an enterprise-grade AI compute foundation. Break free from single-model, single-scenario limits — support mainstream text, code, music, and video models; compatible with the DeepSeek and Qwen ecosystems; CPU/GPU dual-engine adaptive scheduling; with full-scope access control, unified API output, model management, and multimodal task scheduling for government, classified, xinchuang, entertainment, R&D and more — high performance, lower cost, private, commercial, end-to-end.
Key blockers to enterprise private AI adoption
Most private AI offerings are narrow: one model, one scene, weak multimodal fit, poor extensibility. Many products optimize only music or a single vertical — not full LLM coverage — forcing buyers into multiple systems, ops stacks, high cost, hard integration, and unstable delivery.
Lightweight plug-ins for DeepSeek/Qwen/code/music/video and more — performance gains across the board.
One runtime for text, code, music, and video — no more fragmented multi-system deploys.
7B models on CPU from 32GB RAM — tiered compute to avoid overspending on premium hardware.
Role-based access, call permissions, and resource isolation — plus hardware fingerprint licensing and full operation audit for classified, xinchuang, and SOE acceptance.
One place for model onboarding, version switch, quantization, and multi-model ops; cross-modality queues, priority scheduling, and load balance.
Standard API/SSE gateway for multimodal capabilities — plug into OA, ERP, R&D platforms and more with lower integration cost.
PanEngine V2 universal inference — the private AI moat
V2 is a universal inference base — mainstream open LLMs, DeepSeek and Qwen families, code, AI music, and video models; 7B/14B/72B and large MoE; one platform for office, coding, entertainment, and video production.
Eight proprietary capabilities — full-model incremental inference, universal KV cache reuse, intelligent batching, multimodal orchestration, fault tolerance, full-chain monitoring, and more — applied to every model. Text, code, music, or video: faster inference, lower power, stable long sequences; no more slowdown, decay, or waste.
CPU/GPU dual-engine architecture: 7B dense models on pure CPU from 32GB RAM; tiered hardware for large params, multi-model, and video workloads; GPU scale on demand for high concurrency — light deploy and high-volume production in balance.
Rare text + code + music + video enterprise AI acceleration engine in one architecture — multimodal orchestration engine chains cross-modality pipelines; smart office, R&D, entertainment, and video without separate vertical systems; lower integration, ops, and procurement cost.
Full intranet offline operation; weights, data, and outputs 100% on-prem. Hardware fingerprint licensing, model encryption, full audit trail — xinchuang, classified, copyright, and data-security requirements met.
Unified model management, multimodal task scheduling hub, and standard API/SSE output — with full-scope access control, license audit, full-chain monitoring and alerts; deep integration with OA, ERP, and R&D platforms. Native model iteration, cluster scale-out, and plug-in extensions for long-term AI digitization.
PanEngine V2 acceleration base — four AI domains, multimodal commercial delivery
Mainstream open text models; DeepSeek and Qwen 7B/14B/72B dense and MoE; high-precision quantization. Document drafting, policy analysis, Q&A, archive search, copywriting, knowledge ops, sentiment — incremental inference and KV cache for stable long-context dialogue.
Open and commercial code models; Java, Python, Go, C++, and more. Generation, completion, vulnerability scan, comments, refactor, doc generation — optimized for high-concurrency coding and long snippets; private enterprise Copilot.
Mature commercial capability: lyrics, composition, arrangement, vocals, stem separation, IP style distillation, rights workflow. Beyond industry duration limits — native long tracks without stitch artifacts; education, tourism, film-TV, artist IP scenarios.
Native video LLM support; advanced video features roll out by version. V2 scheduling plus tiered hardware cuts video inference cost — basic processing on modest hardware, batch HD production on high-end; smart edit, super-resolution, frame repair, subtitles, understanding, style transfer on roadmap.
Reference ranges across mainstream models — formal projects per Compute Assessment + on-site acceptance.
7B dense models on CPU from 32GB RAM; tiered hardware for large params, multi-model, and multimodal heavy loads.
| Dimension | Typical gain |
|---|---|
| 7B dense inference speed | 50% – 120% overall |
| Long-sequence decay | Within 15% |
| System concurrency | 2 – 4× |
| Runtime stability | Fewer faults — acceptance metrics |
| Hardware utilization | 35%+ |
Code, video, and large models — single or multi-GPU clusters optimized.
| Dimension | Typical gain |
|---|---|
| All-model inference speed | 40% – 90% overall |
| High-concurrency throughput | 5 – 10× vs pure CPU |
| GPU compute & VRAM use | 40%+ |
Differentiated capabilities — enterprise AI compute competitiveness
Most AI stacks optimize one model or scene superficially. PanEngine V2 is a true universal base — mainstream open LLMs, DeepSeek, Qwen, code, music, and video models via lightweight plug-ins; shared speed-up, stable frames, lower power, anti-decay — no rebuild, unified uplift.
One enterprise AI acceleration engine for office text, enterprise coding, IP music, and intelligent video — no multiple systems or ops stacks; industry-leading integration for AI content and office empowerment.
7B dense on pure CPU from 32GB — multimodal production without premium GPU clusters. CPU/GPU hybrid scheduling by model size, modality, and load — right-size hardware, avoid GPU sprawl, lower landing and ops cost.
Security across multimodal data, model assets, and generated content — ingest, training, inference, output, rights. Role-based access, offline intranet, fingerprint licensing, and full audit logs — xinchuang, classified, copyright, data security for government and media buyers.
No model lock-in — unified model management and plug-in extensions for new LLMs, modalities, and verticals without rebuild. Multimodal task scheduling and full-chain monitoring for long-term AI refresh and business expansion — a durable enterprise compute base.
Matched to scale, budget, and business scenario
Government & enterprise · Low-cost landing
Coverage: mainstream open text LLMs including DeepSeek, Qwen 7B/14B; basic private code assist and full-chain AI music; lightweight video on roadmap, advanced video on demand — full tiered fit.
Scenarios: classified government intranet, SOE office, routine R&D assist, tourism/education content, campaign materials.
Advantages: pure CPU offline, low hardware/ops cost, xinchuang-ready, short delivery, full data on-prem, compliance — standard projects in 2–4 weeks.
Hardware: 7B dense from 32GB RAM; 14B or multi-model recommend 64GB+; upgrade on demand for heavy loads.
Volume production · High concurrency
Coverage: high-concurrency inference across models, batch code generation, HD video processing, long-form music production, multimodal content at scale.
Scenarios: enterprise R&D centers, tech teams, MCNs, media companies, high-frequency AI content factories.
Advantages: CPU/GPU dual-engine scheduling and load balance — 5–10× throughput vs pure CPU for enterprise-scale production.
Flagship · Project-based
Coverage: large-parameter models including DeepSeek V4-MoE, 72B+ MoE/dense; distributed multimodal clusters and ultra-long sequence processing.
Scenarios: flagship government programs, high-security units, public AI compute bases, large custom AI platforms.
Advantages: distributed clusters, expert-parallel inference, stable ultra-long output — custom development, architecture tuning, full project delivery.
Multimodal AI across government, R&D, entertainment, and media
Private offline loop — document drafting, policy analysis, archive search, intranet Q&A, sentiment review; data never leaves the perimeter; agencies and public-sector compliance.
Private code Copilot — generation, completion, vulnerability scan, refactor, auto docs, knowledge Q&A; efficiency with R&D data secured on-prem.
Commercial AI lyrics/composition, brand copy, IP-style tracks, lightweight short-video assets — batch original content for tourism, education, artist IP iteration.
Smart editing, style transfer, branded content pipelines, rights workflow, scaled operations — advanced video capabilities delivered by version roadmap.
One-stop landing, ongoing ops — acceptance with confidence
Deploy checklists, environment verification, runbooks, and ops guides — business teams onboard quickly; clear standard delivery path for zero-barrier go-live.
On-site review of concurrency, model sizes, and multimodal mix — hardware recommendations, cost model, and scale path; avoid GPU over-provisioning and idle capacity.
Performance, function, and stability verified on site — traceable process and results; contract item-by-item acceptance for government and enterprise projects.
New mainstream models, modalities, and vertical capabilities per roadmap; engine performance and compatibility upgrades within contract scope.
Remote diagnosis, troubleshooting, patches, and tuning — combined with full-chain monitoring alerts for faster root cause; stable long-term private operation.
Custom model fit, feature development, and integration with OA, ERP, R&D platforms — unified API/SSE for multimodal output across differentiated scenarios.
Throughput, task queues, node health, license and call audit — dashboards and alert policies; traceable ops data for acceptance and daily inspection.
Launch enterprise private AI — acceptance-ready compute infrastructure
🔥 Anchor partner program open — free remote demo + custom private plan 🔥
CPU/GPU sizing advice
More than a single AI tool — enterprise digital compute infrastructure
One engine, every model · Full-stack offline acceptance
Built on our proprietary V2 universal inference engine, PanEngine is an enterprise-grade AI compute foundation. Break free from single-model, single-scenario limits — support mainstream text, code, music, and video models; compatible with the DeepSeek and Qwen ecosystems; CPU/GPU dual-engine adaptive scheduling; with full-scope access control, unified API output, model management, and multimodal task scheduling for government, classified, xinchuang, entertainment, R&D and more — high performance, lower cost, private, commercial, end-to-end.
Key blockers to enterprise private AI adoption
Most private AI offerings are narrow: one model, one scene, weak multimodal fit, poor extensibility. Many products optimize only music or a single vertical — not full LLM coverage — forcing buyers into multiple systems, ops stacks, high cost, hard integration, and unstable delivery.
Lightweight plug-ins for DeepSeek/Qwen/code/music/video and more — performance gains across the board.
One runtime for text, code, music, and video — no more fragmented multi-system deploys.
7B models on CPU from 32GB RAM — tiered compute to avoid overspending on premium hardware.
Role-based access, call permissions, and resource isolation — plus hardware fingerprint licensing and full operation audit for classified, xinchuang, and SOE acceptance.
One place for model onboarding, version switch, quantization, and multi-model ops; cross-modality queues, priority scheduling, and load balance.
Standard API/SSE gateway for multimodal capabilities — plug into OA, ERP, R&D platforms and more with lower integration cost.
PanEngine V2 universal inference — the private AI moat
V2 is a universal inference base — mainstream open LLMs, DeepSeek and Qwen families, code, AI music, and video models; 7B/14B/72B and large MoE; one platform for office, coding, entertainment, and video production.
Eight proprietary capabilities — full-model incremental inference, universal KV cache reuse, intelligent batching, multimodal orchestration, fault tolerance, full-chain monitoring, and more — applied to every model. Text, code, music, or video: faster inference, lower power, stable long sequences; no more slowdown, decay, or waste.
CPU/GPU dual-engine architecture: 7B dense models on pure CPU from 32GB RAM; tiered hardware for large params, multi-model, and video workloads; GPU scale on demand for high concurrency — light deploy and high-volume production in balance.
Rare text + code + music + video enterprise AI acceleration engine in one architecture — multimodal orchestration engine chains cross-modality pipelines; smart office, R&D, entertainment, and video without separate vertical systems; lower integration, ops, and procurement cost.
Full intranet offline operation; weights, data, and outputs 100% on-prem. Hardware fingerprint licensing, model encryption, full audit trail — xinchuang, classified, copyright, and data-security requirements met.
Unified model management, multimodal task scheduling hub, and standard API/SSE output — with full-scope access control, license audit, full-chain monitoring and alerts; deep integration with OA, ERP, and R&D platforms. Native model iteration, cluster scale-out, and plug-in extensions for long-term AI digitization.
PanEngine V2 acceleration base — four AI domains, multimodal commercial delivery
Mainstream open text models; DeepSeek and Qwen 7B/14B/72B dense and MoE; high-precision quantization. Document drafting, policy analysis, Q&A, archive search, copywriting, knowledge ops, sentiment — incremental inference and KV cache for stable long-context dialogue.
Open and commercial code models; Java, Python, Go, C++, and more. Generation, completion, vulnerability scan, comments, refactor, doc generation — optimized for high-concurrency coding and long snippets; private enterprise Copilot.
Mature commercial capability: lyrics, composition, arrangement, vocals, stem separation, IP style distillation, rights workflow. Beyond industry duration limits — native long tracks without stitch artifacts; education, tourism, film-TV, artist IP scenarios.
Native video LLM support; advanced video features roll out by version. V2 scheduling plus tiered hardware cuts video inference cost — basic processing on modest hardware, batch HD production on high-end; smart edit, super-resolution, frame repair, subtitles, understanding, style transfer on roadmap.
Reference ranges across mainstream models — formal projects per Compute Assessment + on-site acceptance.
7B dense models on CPU from 32GB RAM; tiered hardware for large params, multi-model, and multimodal heavy loads.
| Dimension | Typical gain |
|---|---|
| 7B dense inference speed | 50% – 120% overall |
| Long-sequence decay | Within 15% |
| System concurrency | 2 – 4× |
| Runtime stability | Fewer faults — acceptance metrics |
| Hardware utilization | 35%+ |
Code, video, and large models — single or multi-GPU clusters optimized.
| Dimension | Typical gain |
|---|---|
| All-model inference speed | 40% – 90% overall |
| High-concurrency throughput | 5 – 10× vs pure CPU |
| GPU compute & VRAM use | 40%+ |
Differentiated capabilities — enterprise AI compute competitiveness
Most AI stacks optimize one model or scene superficially. PanEngine V2 is a true universal base — mainstream open LLMs, DeepSeek, Qwen, code, music, and video models via lightweight plug-ins; shared speed-up, stable frames, lower power, anti-decay — no rebuild, unified uplift.
One enterprise AI acceleration engine for office text, enterprise coding, IP music, and intelligent video — no multiple systems or ops stacks; industry-leading integration for AI content and office empowerment.
7B dense on pure CPU from 32GB — multimodal production without premium GPU clusters. CPU/GPU hybrid scheduling by model size, modality, and load — right-size hardware, avoid GPU sprawl, lower landing and ops cost.
Security across multimodal data, model assets, and generated content — ingest, training, inference, output, rights. Role-based access, offline intranet, fingerprint licensing, and full audit logs — xinchuang, classified, copyright, data security for government and media buyers.
No model lock-in — unified model management and plug-in extensions for new LLMs, modalities, and verticals without rebuild. Multimodal task scheduling and full-chain monitoring for long-term AI refresh and business expansion — a durable enterprise compute base.
Matched to scale, budget, and business scenario
Government & enterprise · Low-cost landing
Coverage: mainstream open text LLMs including DeepSeek, Qwen 7B/14B; basic private code assist and full-chain AI music; lightweight video on roadmap, advanced video on demand — full tiered fit.
Scenarios: classified government intranet, SOE office, routine R&D assist, tourism/education content, campaign materials.
Advantages: pure CPU offline, low hardware/ops cost, xinchuang-ready, short delivery, full data on-prem, compliance — standard projects in 2–4 weeks.
Hardware: 7B dense from 32GB RAM; 14B or multi-model recommend 64GB+; upgrade on demand for heavy loads.
Volume production · High concurrency
Coverage: high-concurrency inference across models, batch code generation, HD video processing, long-form music production, multimodal content at scale.
Scenarios: enterprise R&D centers, tech teams, MCNs, media companies, high-frequency AI content factories.
Advantages: CPU/GPU dual-engine scheduling and load balance — 5–10× throughput vs pure CPU for enterprise-scale production.
Flagship · Project-based
Coverage: large-parameter models including DeepSeek V4-MoE, 72B+ MoE/dense; distributed multimodal clusters and ultra-long sequence processing.
Scenarios: flagship government programs, high-security units, public AI compute bases, large custom AI platforms.
Advantages: distributed clusters, expert-parallel inference, stable ultra-long output — custom development, architecture tuning, full project delivery.
Multimodal AI across government, R&D, entertainment, and media
Private offline loop — document drafting, policy analysis, archive search, intranet Q&A, sentiment review; data never leaves the perimeter; agencies and public-sector compliance.
Private code Copilot — generation, completion, vulnerability scan, refactor, auto docs, knowledge Q&A; efficiency with R&D data secured on-prem.
Commercial AI lyrics/composition, brand copy, IP-style tracks, lightweight short-video assets — batch original content for tourism, education, artist IP iteration.
Smart editing, style transfer, branded content pipelines, rights workflow, scaled operations — advanced video capabilities delivered by version roadmap.
One-stop landing, ongoing ops — acceptance with confidence
Deploy checklists, environment verification, runbooks, and ops guides — business teams onboard quickly; clear standard delivery path for zero-barrier go-live.
On-site review of concurrency, model sizes, and multimodal mix — hardware recommendations, cost model, and scale path; avoid GPU over-provisioning and idle capacity.
Performance, function, and stability verified on site — traceable process and results; contract item-by-item acceptance for government and enterprise projects.
New mainstream models, modalities, and vertical capabilities per roadmap; engine performance and compatibility upgrades within contract scope.
Remote diagnosis, troubleshooting, patches, and tuning — combined with full-chain monitoring alerts for faster root cause; stable long-term private operation.
Custom model fit, feature development, and integration with OA, ERP, R&D platforms — unified API/SSE for multimodal output across differentiated scenarios.
Throughput, task queues, node health, license and call audit — dashboards and alert policies; traceable ops data for acceptance and daily inspection.
Launch enterprise private AI — acceptance-ready compute infrastructure
🔥 Anchor partner program open — free remote demo + custom private plan 🔥
CPU/GPU sizing advice
