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One-click model deployment on cloud GPUs with context overflow protection, automatic failover, VRAM management, and OpenAI-compatible inference. Your model. Your GPU. No DevOps.
Same code as OpenAI. Your model, your GPU. Change one URL.
from openai import OpenAI
client = OpenAI(base_url="https://gateway.aitherium.com/v1", api_key="aither_sk_...")
response = client.chat.completions.create(model="gemma-4-32b", messages=[...])Works with any OpenAI-compatible client: LangChain, LlamaIndex, Cline, Continue, Cursor.
Send too many tokens and the GPU runs out of KV cache memory. Process crashes. Machine bricks.
GPU crashes require manual SSH, process kill, and re-provisioning. Nobody is watching at 3 AM.
When things go wrong, the entire system stops. No fallback, no slower-but-working alternative.
IDE agents expect OpenAI endpoints. Raw vLLM speaks a slightly different dialect. Things break.
Every request passes through six independent safety layers. Any single layer prevents a GPU crash. Together, they make context overflow mathematically impossible.
max_tokens is clamped to 60% of the context window on every request. No exceptions, no bypass.
hard_cap = max(256, int(context_window * 0.60))
Input tokens are estimated and compared against the available budget. Oversized system prompts are trimmed, preserving the user's actual message.
input_budget = context_window - max_output_tokens - 512
Instead of cutting text at a boundary, lines are scored by importance and the highest-scoring content is kept. No LLM call, pure heuristic, runs in microseconds.
Headers +5.0, code sigs +3.0, key-values +2.0, query overlap +2.0/term
Continuously monitors GPU memory. When VRAM drops below threshold, low-priority models offload to cloud. When VRAM recovers, they come back. 15-second evaluation cycle.
P0: orchestrator (never offload) > P1: vision > P2: coding > P3: reasoning
Every backend has a defined fallback. If vLLM crashes, requests route to cloud. If cloud fails, Ollama on CPU. The chain always terminates.
vllm_swap > vllm_cloud > vllm > ollama (CPU, never OOMs)
Health checks detect GPU failures within 30 seconds. A replacement is provisioned automatically. No SSH, no manual intervention.
Local (prio 1) > Remote mesh (prio 2) > Cloud on-demand (prio 3)
Standard FP16 KV cache uses 512 bytes per token per layer. TurboQuant compresses to ~34 KB per token using 4-bit vector quantization with fused Triton kernels.
Less than 0.3% perplexity increase vs FP16. Undetectable in real conversations. Published as aither-kvcache on PyPI.
Multi-priority queue with per-backend concurrency limits. User requests preempt agent requests, which preempt background tasks. No thundering herd.
Point at any HuggingFace model ID. We handle VRAM sizing, vLLM configuration, GPU provisioning, health monitoring, and the OpenAI-compatible endpoint.
Choose from the catalog or paste any HuggingFace model ID. The system calculates VRAM requirements and finds the cheapest GPU that fits.
One click provisions the GPU, installs vLLM, loads the model, configures TurboQuant, and registers the OpenAI-compatible endpoint. SSE progress streaming shows every step.
Point your OpenAI client at the gateway URL. All 6 safety layers are active. Monitor usage, costs, and health from the dashboard.
Read the full engineering breakdown of every safety layer, with code excerpts from the actual production system.
Read the blog post