Boson AI's flagship speech models are now live on ScitiX Model Inference. bosonai/tts for expressive, conversational text-to-speech, and bosonai/asr for accurate multilingual transcription — a complete voice loop behind one OpenAI-compatible API. Listening and speaking, on infrastructure built to be simple, fast, and secure.

DeepSeek-V4.1-Flash-1017 is now available on ScitiX. PD-separated serving for stable latency under concurrent load. One endpoint alongside GLM, Qwen, Kimi, MiniMax, and 20+ other models — your architecture stays the same when model requirements change. Zero data retention. 99.9% uptime. T+3 availability. scitix.ai/inference

Live cluster monitoring, updated in real time. Active jobs, GPU utilization, token throughput, node-level breakdowns, job logs, inference throughput curves—all moving as your workload runs. Not a snapshot. Not a 30-second refresh. Live. Four commands from install to this dashboard. pip install scitix-cli and you're in. → scitix.ai

Your AI infra expert, inside Slack, Teams, and your API. Check GPU utilization. Pull price quotes. Set alerts. Trigger autoscale. Troubleshoot a failing job. Manage billing. All without leaving your workspace—natural language or code, whichever fits the moment. No tickets. No portal. Just ask. Connect your workspace: scitix.ai

Different tasks need different models. ScitiX Model Advisor shows you why. Complex Reasoning → GLM-5 (744B MoE, long-horizon agentic tasks) Low-Latency RAG → gpt-oss-20b (18ms TTFT, fastest open-weight option) Code, Batch, Vision, Agents & more—describe your use case, get a recommendation with reasoning attached. scitix.ai/inference

In MLPerf Inference v6.1, ScitiX recorded 59,668 tok/s Server and 60,410 tok/s Offline for DeepSeek-R1 on 8×SZ002-SXM-180GB. Same hardware, same model—but two different optimization problems. Offline maximizes steady-state throughput with no latency constraint. Server has to protect TTFT and TPOT p99 under continuous request arrival. The scheduler parameters that maximize one can break the other. We tuned them separately. If you're running inference at this scale, that distinction matters more than the headline number.

Weight movement isn’t solved for trillion-scale models. FlashBoot turns layout, transfer, and replication into one data path—at the bandwidth ceiling. NVL72 · DeepSeek-V4-Pro · TP=4 Load: 20.1s → 0.40s (50× vs InstantTensor) 8 replicas: 87s → 0.32s (270×+ vs SGLang R-Fork)

FlashBoot co-designs layout, transfer, and replication as one data path—and drives each to hardware limits. Not NVL72-specific: covers C2C/PCIe and NVLink/RDMA, validated on GLM-5.2, DeepSeek-V4, and Kimi-K3. Trillion-parameter elasticity starts here.

This was not unconstrained peak throughput. The Server run remained within the applicable latency limits, with p99 TTFT of 1.719s and p99 TPOT of 70.43ms. The same system also reached 60,410.90 output tokens/s in DeepSeek-R1 Offline (Closed), 3.12% above v6.0.

Something we keep running into with agent inference: the tool-call idle window. Local tools (file I/O, shell) come back in milliseconds. Remote tools (search, APIs) can take 10+ seconds. And during that wait? GPU's just sitting there. KV cache too — fully loaded in HBM, doing absolutely nothing. So at @Scitix we've been playing with agent-aware KV eviction: · Kick the KV out while the tool call is still running, pull it back right before the result comes in. · Turns dead time into free memory. No latency cost to the user. · Chat serving never had to deal with this. Agents do. Still early, but there's clearly more here.

Fast models still feel slow when the infrastructure isn't right. What we obsess over at ScitiX: — 1s average TTFT — 93.9% KV-cache hit rate — 99.9% uptime — 72% average cost savings Built on ScitiX's own GPU clusters with session-aware orchestration and 3-tier failover. This is what production inference should feel like. Get Started → scitix.ai/inference