Inference engine optimization
Coordinate continuous batching, parallelism and execution around real traffic.
Lower wait · raise useful throughputGiúp mỗi mô hình tạo token hiệu quả hơn.
Uptech API unifies inference engines, tiered KV cache and GPU scheduling in one production pipeline—continuously improving wait time, throughput and resource utilization while keeping endpoints stable.
The moat is not owning GPUs. It is making every GPU work better.
Token Factory brings request paths, cache tiers, compute capacity and runtime feedback into one continuously optimized control plane.
Tiered KV cache
Reuse eligible prefixes across GPU, CPU and NVMe to reduce recompute and memory pressure.
Improve reuse · free GPU memoryGPU compute scheduling
Match model scale, context and concurrency to the right heterogeneous GPU pool.
Reduce idle time · avoid hotspotsRuntime observability
Continuously tune routing and capacity with latency, throughput, cache and utilization signals.
Continuously tune · serve reliablyMarket research context: hardware represents roughly 80% of token production cost. Actual cost and optimization gains depend on the model, hardware and traffic profile; this is not a performance, pricing or SLA commitment.
China-built open models · Deployment guide
Match model scale to the right compute path.
Verified against official open-weight releases on Aug 18, 2026. These configurations guide early capacity planning, not live availability or performance guarantees.
Qwen · Max-class
Qwen3.8-2.4T-A95B
2.4T total · 95B active256K native / 1M extended
Compute guideFP8 16×B300 · BF16 24×B300
Qwen3.8-Max adds managed features on top of these open weights.
Z.ai
GLM-5.2
753B total · MoE1M context
Compute guideEvaluate from 16×80GB-class GPUs
FP8 weights; reserve KV cache separately for full context.
DeepSeek
DeepSeek-V4-Pro-0813
1.6T total · 49B active1M context
Compute guideOfficial example: single node with 4×GB300
Scale to multiple nodes for long context or production throughput.
Moonshot AI
Kimi-K3
2.8T total · 104B active1M context
Compute guideOfficial guidance: supernode with 64+ accelerators
MXFP4 weights and MXFP8 activations; plan for multiple nodes.
MiniMax
MiniMax-M3
428B total · 23B active1M context
Compute guideEvaluate from 16×80GB-class GPUs
Native BF16 weights; production concurrency needs more headroom.
Serving topology
Not every parameter runs the same way.
For MoE models, active parameters shape per-token compute, while total weights, KV cache, precision and concurrency determine real memory needs.
Reference nodes
Frontier high-memory nodes
Low-precision frontier MoE weights can start on a high-memory node.
Qwen3.8 · DeepSeek-V4-Pro-0813Capacity plan
Multi-GPU capacity planning
Mixed precision reduces weight memory; production loads still need validation.
MiniMax-M3Cluster
Multi-node cluster
For large total weights, fast interconnects and planned capacity.
GLM-5.2 · Kimi-K3Capacity estimate, not an SLA. Minimum loadability is not stable serving; validate precision, inference engine, context, concurrency and network topology for your workload.
See what one-click deployment could feel like.
A look at the experience we are building: choose a model, configure compute, let Uptech API deploy it, then connect the endpoint to your app.
Pick a model
Browse leading open models and choose the right fit.
We deploy and operate it
We provision, optimize and manage your dedicated compute.
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Integrate via OpenAI-compatible APIs and start building.