Inference OS for Modern AI

Run 5x more models on 5x fewer GPUs. Fast, efficient inference server compiled for your multi-model AI

AI went multi-model

But, Infrastructure stayed behind

But, Infrastructure stayed behind

Modern AI needs many models — agents, pipelines, RAG stacks. But inference platforms still treat each model as its own island.

Today's Reality

One request, Many models

Modern AI chains specialized models — routing, embedding, reranking, generation. A single API call triggers them all.

Many GPUs, 50% idle

Each model gets its own deployment. While one infers, the others wait. You pay for capacity that sits idle.

WITH

Shared, not isolated

Multiple models pack onto the same GPU. Resource flow to active model.

No idling, No slowdown

More inference on fewer GPUs without sacrificing throughput and accuracy.

Zero-bloat Production

Compiles to lean, hardware-native binaries. 6x smaller runtimes.

Building with Open-Source Models?

[ not just calling an API ]

We compile the best-in-class inference server purpose-built for your workflow

Others
Metric
Nvidia RTX 4090
~8900 tok/s
Pre-fill
~9200 tok/s 52 tok/s
Auto-regressive
54 tok/s Spiky
GPU Utilization
Balanced
GPU Util
Memory Usage
VS

State-of-the-Art Single Model Performance

Compiled to your hardware, your tokens/sec never regress with us

Neurafewz compiles models into lean, hardware-native binaries — no framework dependencies, no runtime bloat, no dependency conflicts. Just faster inference in a fraction of the memory footprint, even before multi-model benefits kick in.
Workload: Whisper-Large-V3 → Llama-3-8B-Instruct
Others
Metric
~28GB
Total VRAM
~22GB +45ms
Network Gap
+00ms 50%
Avg. Idling
5%
VS
RTX 3070
WHISPER
Network +45ms
RTX 4090
LLAMA 3
RTX 4090 (Co-Located)
WHISPER → LLAMA 3
Memory Util

Category Defining Multi-Model Performance

Run workloads others can't. On half the hardware.

An example: Whisper + Llama 3.1 8B + Embedding on One consumer GPU. Full voice-to-answer pipeline in under two seconds. Other platforms can't even run this configuration — Neurafewz was built for it.

Not Just Deployed Together. Forged Together

A new class of multi-model runtime — with shared memory, intelligent scheduling, and in-memory orchestration built in.

Neurafewz compiles your entire workflow into a single GPU-native runtime. Models share memory, compute flows to whichever model is active, and orchestration happens in-memory — no containers, no cold swaps, no waste.

Start with a concept or working code

Production-optimized inference engine in minutes — not months

No new frameworks. No new syntax. Just your workflow, compiled

Just a concept in mind?

Build with our Agent Builder

Discuss the painpoint you are tying to solve and let the builder figure out the right pipeline, optimal model combinations and once you are sure compile it into a production-ready inference server.
Ship Fast
Concept to production in minutes, not months
Surgical
Auto-selects the best open-source models for each task
purpose-built
Compiled, optimized, ready to deploy — not a prototype

Built a workflow on a framework?

Your code to high-performance server in minutes

Just connect your LangGraph repository — we analyze your pipeline, compile it to our inference engine, and redeploy. Same logic. Dramatically faster execution.
CI-NATIVE
Push to production triggers automatic recompilation
DROP-IN
Zero-rewrite, peak GPU utilization. 5x throughput on same GPU

Host It Yourself or With Us

Your data is private either way

01YOUR INFRA

Self-Hosted

We build the engine. You own everything else
Your Hardware
Deploy on your GPUs — on-prem, VPC, or bare metal. We compile the engine for your exact hardware.
Full Control
Single-click setup. No ongoing dependency on us — the engine runs independently.
Simple Pricing
Pay per engine build. No metering, no per-request fees, no surprises.
Req/sec:0*Cost/hr:$0

Managed

Scale-to-zero, scale-to-many; we meet you at your scale
Your Encryption
Bring your own keys. Isolated tenancy. No one gets a peek.
Auto-Scale
Traffic spikes handled automatically - scale in seconds without ops burden.
Simple Pricing
Pay per inference-seconds. Scale to zero when idle — no GPUs burning money overnight.
* Demonstrative

Trusted by AI Native Teams & Partners

M37 Labs IconVikmo IconEinstein Labs IconZunderdog IconAdept Global IconTangentia IconNVIDIA Inception Nasscom AI Icon

Models reimagined to work together

State-of-the-art research baked in. Production-ready out of the box

LLaMA OpenAI Whisper Gemma Gemma Qwen Phi-2 DeepSeek Mistral

Efficiency, Accuracy, Sovereignty

Every model re-implemented for peak individual performance and native multi-model resource sharing. Not optimizations on top — a new foundation underneath

Zero-compromise Throughput

We build the engine. You own everything else
Flash Attention Speculative Decoding Continuous Batching Prefill Caching

Making Every GPU Cycle Count

Minimize Idling, Eliminate wasted vRAM
Paged Attention Partial Quantization KV Cache Optimization Dynamic Memory Pooling

Batteries Included

The full stack, not just the model
Text Splitters Embedding Models Rerankers Vector Search Guardrails Structured Output Parsing

Numbers speak a thousand words

The full pipeline. A fraction of the footprint.

Deployment Size
Num. GPUs
GPU Specs
Memory Used
Latency
Pipeline
Q&A over Voice
Flow
Speech-to-text → Text Embedding → Vector Store Lookup → Answer Generation
Models
LLaMA 3.1 8B + Whisper Large-v3 + Qwen Embed 600M
Data
~11s audio · 1124 Prompt + input tokens · 132 output tokens
vLLM + FasterWhisper + vLLM
~10GB
2
Nvidia RTX 4090 + Nvidia RTX 3080
29GB
~2.1sec (± 1.4%)
~300MB
1
Nvidia RTX 4090
22GB
~1.7sec (± 1.0%)

Individual Model Performance

Baselines run as exclusive GPU tenants. Neurafewz results measured with Llama 3.1 8B and Whisper Large-v3 co-resident on a single 24 GB RTX 4090.

Llama 3.1 8B
Prefill (tok/s)
Generation (tok/s)
vLLM
10,900
57.44
llama.cpp
8,900
55.70
9,700
57.80

BF16 · ~16 GB full, ~12 GB resident with 8/32 layers offloaded · 1024 input, 512 generated · continuous batching · 3 warmup / 10-run mean. vLLM and llama.cpp run as sole GPU tenants; Neurafewz co-resides with Whisper Large-v3 (~3 GB).

Whisper Large-v3
Proc. Time
Speed
Batch
openai-whisper
60.30s
13× real-time
1 OOM >1
faster-whisper
12.70s
62× real-time
8
9.71s
81× real-time
8

FP16 · Audio: 13.13 min · beam_size=5 · Speed = audio_duration / processing_time. faster-whisper: FP16, CUDA 12. Neurafewz co-resides with Llama 3.1 8B (~16 GB). 1.31× faster than faster-whisper via fused encoder kernels.

Select Kernel Performance

Wall-clock (mean)
Throughput (TFLOPS)
% of Peak
Speedup
Type
Compute-Bound
Kernel
Fused RMSNorm + QKV Projection + RoPE + KV Cache Write
Config
M=1024, K=4096, N=[4096,1024,1024] · BF16 · 3 warmup / 100-run mean
PyTorch
0.4657ms
110.8
67.0%
baseline
Triton
0.4154ms
124.2
75.2%
1.12×
Neurafewz
0.3473ms
148.5
89.9%
1.34×
Type
Memory-Bound
Kernel
Fused Conv1d + GELU + Positional Encoding + LayerNorm Stats
Config
M=10500, K=384, N=1280 · FP16 (FP32 epilogue) · 3 warmup / 10-run mean
PyTorch
1.987ms
5.28
3.20%
baseline
Triton
1.126ms
9.39
5.68%
1.76×
0.958ms
10.97
6.64%
2.07×
Type
Unfused Baseline
Kernel
Attention — Compile-Time Selection Only
Config
SDPA batch=8, heads=20, seq=1500, head_dim=64 · FP16 · 3 warmup / 100-run mean
PyTorch (SDPA)
0.6754ms
138.6
83.9%
baseline
0.6371ms
146.9
89.0%
1.06×