The AI Runtime
Built to Last.
High-performance distributed inference engine with a first-party transformer core, ONNX model compiler, cloud control plane, and multi-language SDK suite — open source, production-ready.
Built with open standards
Every layer of the
inference stack.
From hardware GPU scheduling to cloud-native deployment — EverestQ owns the full stack so you don't have to.
First-Party Transformer Execution
EverestQ's native inference core runs transformer models directly in Go/C++ without intermediaries — implementing Grouped-Query Attention, Paged KV Cache with 16-token page blocks, SwiGLU MLP operators, and RoPE rotary embeddings.
- Grouped-Query Attention (GQA)
- Paged KV Cache allocator
- FP32/FP16/INT8/INT4/Q4_K_M
- SIMD CPU · CUDA · Metal backends
// EverestQ Native Core — GQA Forward Pass
func (e *Executor) GQAAttention(q, k, v *Tensor) *Tensor {
// Scale queries
scale := 1.0 / math.Sqrt(float64(e.HeadDim))
q.Scale(scale)
// Paged KV Cache lookup
k = e.KVCache.AppendKey(k)
v = e.KVCache.AppendValue(v)
// Attention scores
scores := MatMul(q, k.Transpose())
scores = Softmax(scores)
return MatMul(scores, v)
}Engineered from
first principles.
Every component is purpose-built. The CLI talks directly to the Engine Daemon, which dispatches through the hardware-aware Scheduler into the Native Runtime executing on CUDA, Metal, or CPU backends.
Performance results.
All benchmark data is derived from actual hardware executions. We do not publish illustrative numbers. Results marked “not yet measured” require infrastructure setup to reproduce.
View Benchmark MethodologyStart building today.
Download the EverestQ CLI, serve your first model in under 60 seconds, and deploy to a distributed GPU cluster when you're ready.