ACCELERATED WITH NVIDIA TENSORRT • HOPPER & BLACKWELL READY

Enterprise-Grade Distributed AI Inference Infrastructure

TFM Neural Labs engineers ultra-low latency inference engines and scalable multi-GPU orchestration pipelines for real-time generative models, vision systems, and autonomous agents.

Explore Developer API
// PIPELINE: TFM-ENGINE-RUNTIME-v1.4 ● CLUSTER STATUS: OPERATIONAL (ANTIBES / SOPHIA ANTIPOLIS)
01. Model Ingestion

TensorRT Compiler

Automated FP8/INT4 graph compilation and layer fusion optimized for Hopper & Blackwell silicon.

02. Execution Engine

Triton Orchestrator

Dynamic concurrent model scheduling with sub-millisecond dispatch and tensor parallelism.

03. Memory Bus

Unified KV-Cache

Paged memory allocation minimizing VRAM fragmentation across distributed GPU clusters.

04. Edge Gateway

Zero-Latency API

gRPC and WebSocket streaming endpoints delivering sub-15ms Time-To-First-Token (TTFT).

BUILT FOR NVIDIA HARDWARE & ACCELERATED COMPUTING ECOSYSTEM

NVIDIA CUDA 12.4+
NVIDIA TensorRT-LLM
Triton Inference Server
PyTorch 2.4 Fused Kernels
NVIDIA NIM Microservices

Developer-First Architecture

Drop-in replacement for OpenAI endpoints with 4x the speed and native TensorRT execution.

from tfm_engine import Client, QuantizationMode # Initialize connection to TFM TensorRT accelerated cluster client = Client( api_key="tfm_live_sec_06160_antibes", cluster_endpoint="https://api.tudorflorinmarian.com/v1" ) # Execute sub-10ms stream generation with fused Hopper kernels response = client.chat.completions.create( model="llama-3-70b-instruct", precision=QuantizationMode.FP8, stream=True, messages=[ {"role": "system", "content": "You are an autonomous AI systems engineer."}, {"role": "user", "content": "Optimize CUDA kernel stream allocation."} ] ) for chunk in response: print(chunk.choices[0].delta.content, end="")
curl -X POST https://api.tudorflorinmarian.com/v1/chat/completions \ -H "Authorization: Bearer tfm_live_sec_06160_antibes" \ -H "Content-Type: application/json" \ -d '{ "model": "llama-3-70b-instruct", "precision": "fp8", "temperature": 0.2, "max_tokens": 512, "stream": true, "messages": [ {"role": "user", "content": "Stream inference metrics across NVIDIA H100 SXM5."} ] }'
import { TFMEngine } from '@tfm/sdk'; const tfm = new TFMEngine({ apiKey: process.env.TFM_API_KEY, endpoint: 'https://api.tudorflorinmarian.com/v1' }); async function runInference() { const stream = await tfm.chat.stream({ model: 'llama-3-70b-instruct', acceleration: 'tensorrt-fused', prompt: 'Generate high-frequency agent actions.' }); for await (const token of stream) { process.stdout.write(token.text); } } runInference();

Engineered for Extreme Throughput

Unlocking peak performance from modern tensor core architectures.

🚀

Sub-5ms TTFT Latency

By bypassing standard framework overhead and executing custom CUDA kernels with PagedAttention, TFM cuts inference latency by up to 65%.

⚡

Dynamic Model Quantization

Seamlessly quantize 70B+ parameter checkpoints to FP8 and AWQ INT4 without accuracy degradation, drastically cutting hardware costs.

🛡️

Private Enterprise VPC

Deploy directly into air-gapped enterprise environments or sovereign GPU clouds with end-to-end data privacy and zero data retention.

Benchmarked Across Modern GPU Silicon

Real-world throughput metrics on Llama-3 70B and Mistral-Large models.

Hardware Setup Execution Engine Precision Throughput (Tokens/s) TTFT Latency
NVIDIA H100 (80GB SXM5) TFM Engine + TensorRT FP8 Optimized 4,820 tokens/sec 8.4 ms
NVIDIA H100 (80GB SXM5) Standard HuggingFace / PyTorch FP16 1,150 tokens/sec 38.2 ms
NVIDIA L40S (48GB) TFM Engine + TensorRT INT4 AWQ 2,310 tokens/sec 14.1 ms
NVIDIA L40S (48GB) Standard vLLM Baseline FP16 840 tokens/sec 46.5 ms

Frequently Asked Questions

Key architectural and enterprise deployment details.

How does TFM achieve 4x throughput over standard vLLM? +
TFM compiles neural weights directly into native NVIDIA TensorRT execution graphs, fusing multi-head attention and layernorm operations into single-pass CUDA streams. This eliminates memory round-trips and fully saturates Hopper and Blackwell Tensor Cores.
Can TFM be deployed in an air-gapped or private cloud VPC? +
Yes. We offer pre-built enterprise container appliances for on-premise DGX SuperPODs, private Kubernetes clusters, and sovereign EU/French cloud regions with zero external data telemetry.
Is TFM compliant with EU GDPR and French data sovereignty regulations? +
Fully compliant. As an entity operating within France and the European Union, all operations respect strict European data protection standards with zero customer data retention on inference endpoints.
FRENCH TECH CÔTE D'AZUR • SOPHIA ANTIPOLIS ECOSYSTEM

Accelerating the Autonomous AI Frontier

Founded by Florin-Marian Tudor, TFM Neural Labs engineers the world's most efficient execution runtime for foundation AI models and intelligent agents.

As an early-stage deep-tech startup, we actively integrate with the NVIDIA Inception Program to benchmark our runtime on bleeding-edge DGX Cloud clusters and bring enterprise-grade accelerated computing to developers worldwide.

Startup & Legal Overview

  • Commercial Brand: TFM Neural Labs
  • Legal Entity: TUDOR FLORIN-MARIAN (EI)
  • Legal Form: Independent Technology Enterprise
  • Registered Office: Antibes (Sophia Antipolis Ecosystem), France
  • Founder & Principal: Florin-Marian Tudor
  • Domain: tudorflorinmarian.com
  • Focus: GPU-Accelerated Inference & Distributed AI
  • Contact: florin@tudorflorinmarian.com