"A lot of really cool applications come directly from developers, so I'm telling the whole company that our focus is developers and small businesses, and we'll work our way up." — Tenstorrent CEO Jim Keller (Dec 9, 2025)

For a strategic developer-first focused Systems & Solutions approach, click here!

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From Open Silicon to Production Systems:Scaling Tenstorrent's Customer Success

A Strategic Roadmap for the Director, Systems & Solutions Role
by Farjad Syed | 15+ Years Scaling AI/ML Infrastructure

⚡
55%
Reduced customer deployment time
Across 200+ Enterprise Implementations
🌍
3 → 50+
Scaled Global Teams
24×7 Operations (US/EMEA/APAC)
🎯
98%
First-Pass Success
150+ Complex System Integrations
View My 90-Day Plan Download 1-Page Executive Brief

Why Tenstorrent Wins—and Where the Battle Is

Understanding the opportunity, identifying the gaps, and proposing strategic solutions

The Opportunity

Strategic Advantages

  • ▸ RISC-V + Open Chiplet Atlas = Sovereign AI enabler
  • ▸ Ethernet-first scaling = Lower TCO vs. InfiniBand
  • ▸ Dual IP+Silicon model = OEM leverage (LG/Hyundai partnerships)
  • ▸ Grendel chiplet architecture = Manufacturing flexibility
  • ▸ Sparsity-aware architecture = Efficiency edge for modern AI workloads

The Gap

Director's Risk Assessment

  • ▸ CUDA ecosystem moat = Long customer migration cycles
  • ▸ Missing: Llama-3 end-to-end training proof at cluster scale
  • ▸ Developer friction: TT-Metal too low-level for data scientists
  • ▸ Limited enterprise observability/MLOps integration
  • ▸ "Day 2 Operations" burden falls on customers
  • ▸ Fragmented go-to-market story: IP vs. systems vs. dev kits

My Solution Lens

Farjad's Strategic Response

→ Hardware is table stakes. Winning requires solutions engineering—where I've reduced deployment time 55% across 200+ complex rollouts.

→ I've built repeatable service packages at 70% margin. Let's productize "Tenstorrent Solutions Blueprints": LLM Serving Stack, Edge Inference Cluster, Automotive ADAS Integration.

→ Scale requires playbooks. I reduced onboarding time 87% through automation—this is how we'll de-risk Tenstorrent adoption.

Tensix Mesh Architecture

Understanding the Core Topology for Optimized System Integration

T1 T2 T3 T4 T5 T6 T7 MAIN T9 T10 T11 T12 T13 T14 T15 Row 0 Row 1 Row 2 Col 0 Col 1 Col 2 Col 3 Col 4
Tensix Core
Individual compute unit
Main Controller
Central coordination node
Mesh Links
High-speed interconnect

My First 90 Days: From Vision to Validated Systems

As Director, Systems & Solutions, my priority is de-risking customer adoption

Days 0-30

Listen & Align

Objective: Map customer friction across key verticals (Auto, Cloud, Sovereign AI)

Key Actions:

  • • Shadow Solutions Engineers on LG/Hyundai/Moreh customer engagements
  • • Audit top 5 customer escalations and migration blockers
  • • Deep-dive TT-Forge/TT-NN roadmap with Engineering and Software teams
  • • Interview 10 customers on deployment pain points

Success Metrics:

→ Deliver 3-page brief: "Top 5 Adoption Barriers + Quick Wins"

→ Build relationships with Product, Engineering, and CS leadership

Days 31-60

Prototype Solutions

Objective: Turn architecture into consumable deployment patterns

Key Actions:

  • • Launch Tenstorrent Reference Stack v1: LLM Serving (vLLM + TT-NN + Prometheus monitoring)
  • • Launch Tenstorrent Reference Stack v1: Edge Inference Cluster (Blackhole + Kubernetes + OTA updates)
  • • Publish TCO model: Tenstorrent vs. H100 ($/token, $/kW comparison)
  • • Create "Deployment Readiness Checklist" for 4-card to 256-card systems

Success Metrics:

→ 2 lighthouse pilot customers signed

→ 40% faster onboarding in test cohort

→ First public reference architecture published

Days 61-90

Scale & Socialize

Objective: Build ecosystem flywheel

Key Actions:

  • • Launch Solutions Partner Enablement Kit (IaC templates, playbooks, CSAT benchmarks)
  • • Co-host "Tenstorrent Solutions Day" with LG/Moreh partners
  • • Execute first public "Hero Run": Train Mistral-7B end-to-end on Galaxy cluster
  • • Establish weekly "Systems Office Hours" for customer technical questions

Success Metrics:

→ 3+ reference architectures publicly available

→ 85% CSAT on first production deployments

→ 5+ ecosystem partners certified on Tenstorrent solutions

Systems at Scale: Mapping Past Wins to Tenstorrent's Needs

Connecting proven leadership achievements to your strategic challenges

🎯

Reduced AI Deployment Time 55% at Augmentry.ai

Challenge:

Customers couldn't operationalize LLMs without dedicated ML engineers on staff.

My Solution:

Built comprehensive Deployment Playbook including:

  • IaC templates (Terraform modules for GPU/accelerator clusters)
  • Observability templates (Grafana dashboards per model type)
  • Self-service onboarding portal (reduced SE touchpoints by 65%)
  • Automated validation suite catching 80% of misconfigurations pre-deployment

Measurable Impact:

✓ Time-to-value: 60 days → 27 days (55% improvement)
✓ Customer self-sufficiency: 85% building autonomous workflows
✓ SE capacity freed: 40% to focus on complex enterprise deals

Parallel for Tenstorrent:

Build "TT-Deploy"—standardized, open IaC for Wormhole/Blackhole clusters with PyTorch-native monitoring. Make "pip install tenstorrent-deploy" the easiest path to production.

🎯

Turned $300K Loss → Profitability in Global Operations (LogicMonitor)

Challenge:

Fragmented regional teams, inconsistent SLAs, 75% escalation rate, operational losses.

My Solution:

Created Global Solutions Engineering Framework:

  • Unified KPIs across US/EMEA/APAC teams
  • 3-tier escalation path with automated routing
  • Shared enablement curriculum reducing onboarding from 90 to 30 days
  • Weekly cross-regional retrospectives feeding product roadmap

Measurable Impact:

✓ P&L: ($300K) loss → profitable within 12 months
✓ Escalations: Reduced 75% through proactive capacity planning
✓ Team utilization: Increased to 125% without burnout
✓ Forecast accuracy: 92% across all regions

Parallel for Tenstorrent:

Unify IP-licensing SEs, hardware SEs, and cloud partners under "Tenstorrent Solutions Guild"—shared playbooks, certification program, direct feedback loop to IP roadmap.

🎯

Scaled Infrastructure to 5M+ Monthly AI Transactions (Augmentry.ai)

Challenge:

Rapid growth straining infrastructure; 40% of compute resources wasted; unpredictable performance.

My Solution:

Architected ML-optimized infrastructure:

  • Linux kernel tuning for AI workloads (CPU affinity, memory allocation)
  • Intelligent resource allocation reducing cloud costs 85%
  • Real-time monitoring with automated scaling policies
  • Performance optimization increasing throughput 40%

Measurable Impact:

✓ Cost reduction: 85% in infrastructure spend
✓ Performance: 40% increase in effective throughput
✓ Reliability: 99.7% uptime SLA achieved
✓ Scale: Supported 5M+ monthly AI transactions

Parallel for Tenstorrent:

This IS the Tenstorrent playbook—optimizing Linux for Tensix cores, building cost-effective scale-out architectures, and making Blackhole/Grendel systems production-grade for customer workloads.

Hands-On Mastery: The Technical Foundation

Deep systems expertise directly applicable to Tenstorrent's challenges

Hardware & Systems

AI Accelerators & Architecture

  • Deep experience with GPU/accelerator deployment (NVIDIA, AMD, custom ASICs)
  • Server architecture: Dell PowerEdge, Cisco UCS, blade systems
  • Thermal management & power optimization for high-density compute
  • Cabling & networking for multi-accelerator topologies
  • Capacity planning and hardware validation

Directly Applicable to Tenstorrent:

  • → Validating Wormhole/Blackhole thermal profiles
  • → Designing reference rack configurations for Galaxy servers
  • → Ethernet mesh topology optimization for 100Gb/400Gb scale-out

Linux & Performance

Operating Systems & Optimization

  • Linux administration (RHEL, Ubuntu, CentOS) at scale
  • Kernel tuning for AI workloads: CPU affinity, NUMA optimization
  • Performance profiling and bottleneck analysis
  • Driver debugging and firmware management
  • WireGuard/VPN mesh networking (relevant to Tenstorrent's Ethernet approach)

Directly Applicable to Tenstorrent:

  • → Optimizing Linux for Tensix cores and RISC-V integration
  • → Building TT-specific performance monitoring dashboards
  • → Debugging TT-Metalium runtime issues in customer environments

Automation & IaC

DevOps & Deployment Engineering

  • Infrastructure as Code: Terraform, Ansible, Puppet, Chef
  • Container orchestration: Kubernetes, Docker, OpenShift
  • CI/CD pipelines for hardware validation
  • Python/Bash automation for system provisioning
  • GitOps workflows for repeatable deployments

Directly Applicable to Tenstorrent:

  • → Creating "tenstorrent-terraform-modules" for cluster deployment
  • → Kubernetes operators for TT-NN workload orchestration
  • → Automated validation suites for multi-card configurations

Observability & Ops

Monitoring & Production Excellence

  • Prometheus/Grafana for infrastructure monitoring
  • ELK Stack, Splunk, Datadog for log aggregation
  • Custom dashboards for AI workload visibility
  • Incident response and on-call operations
  • SLA/SLO definition and tracking

Directly Applicable to Tenstorrent:

  • → Building "TT-Observe": unified monitoring for Blackhole clusters
  • → Creating customer-facing dashboards showing utilization, cost/performance
  • → Establishing 24×7 global support model for Tenstorrent deployments

Community & DevRel

Developer Experience Engineering

  • Discord/Slack community management at scale
  • Technical content creation (tutorials, videos, blog posts)
  • Developer advocacy and conference speaking
  • Open-source community governance
  • Bounty programs and contributor management

Directly Applicable to Tenstorrent:

  • → Building Tenstorrent's Discord to 1,000+ active developers
  • → Creating video tutorials that reduce onboarding friction
  • → Managing developer champion programs and contribution workflows

Technical Expertise Radar

Skills Mapped to Tenstorrent's Core Challenges

Hardware & AI Advanced Linux Perf Expert IaC & Auto Expert Observability Expert Cloud Native Advanced DevOps Expert Sys Design Expert Leadership Advanced Basic Intermediate Advanced Expert
Expert Level

15+ years hands-on experience, proven at scale

Advanced Level

10+ years experience, deployed in production

Proficient Level

Strong working knowledge, team leadership

Proven Impact: Developer Onboarding Acceleration

Real results from enabling 2,000+ developers across complex infrastructure

BEFORE
3-4
Weeks
Traditional Developer Onboarding
Manual setup
2 weeks
Trial & error
1 week
First success
1 week
Manual configuration steps
Scattered documentation
Email-based support
High abandonment rate
AFTER
2-3
Days
Optimized Developer Onboarding
Automated setup
2 hours
Guided tutorials
1 day
First success
4 hours
One-command environment setup
Interactive learning paths
Real-time Discord support
85% self-sufficiency rate
87%
Time Reduction
5x
Developer Velocity
92%
Success Rate

What I'll Ship in My First 180 Days

Concrete, open-source contributions addressing developer friction

🚀
❌ Gap: Blackhole setup takes 8+ hours, high failure rate

TT-QuickStart: Zero-to-Running in Under 1 Hour

What I'll Build:

  • One-command installer for Ubuntu/RHEL/Docker
  • Automated driver installation and validation
  • Pre-configured Jupyter environment with TT-NN examples
  • Health check script that catches 90% of common issues
  • Video walkthrough for first-time users
✓ GitHub repo + Docker image + 15-min tutorial video View Prototype →
📚
❌ Gap: TT-NN has 70 models vs. PyTorch's 1,000+

Model Zoo: 20 Popular Models Ported to TT-NN

What I'll Build:

  • Llama-2-7B, Stable Diffusion 1.5, Whisper, BERT, ResNet-50
  • Each with: inference script, performance benchmarks, optimization guide
  • Automated CI testing ensuring models stay working
  • Community contribution guidelines for adding new models
  • Performance comparison: TT-NN vs. PyTorch on same hardware
✓ Public model repository + benchmark dashboard View Roadmap →
📖
❌ Gap: No community knowledge base—developers repeat same mistakes

TT-Docs Overhaul + Interactive Learning Platform

What I'll Build:

  • Complete documentation restructure: Getting Started → Advanced Optimization
  • 30+ code examples with copy-paste snippets
  • Interactive Jupyter notebooks for hands-on learning
  • "Common Errors" database with solutions
  • Community-contributed guides (with attribution)
✓ docs.tenstorrent.com revamp + JupyterHub sandbox View Demo →
👥
❌ Gap: Developers stuck without real-time support

Tenstorrent Developer Community Hub

What I'll Build:

  • Discord server with structured channels (beginners/advanced/showcase)
  • Weekly "Office Hours" with live Q&A and screen sharing
  • Developer Champions program recognizing top contributors
  • Bounty board: Get paid to solve community issues
  • Monthly virtual meetups with demos and AMAs
✓ Active Discord (target: 250 developers by Day 90) Join Discord →

Let's Build the Solution Layer—Together

I'm not just applying for a role—I'm proposing a partnership to turn Tenstorrent's architectural leadership into market dominance.

I've spent 15 years making complex infrastructure consumable. At Tenstorrent, I won't just deploy systems—I'll build the bridge from Jim Keller's vision to production Kubernetes clusters at scale.

Let's discuss how we operationalize the open AI future.

📧
Email
farjad.syed@proton.me
📞
Phone
(317) 690-0074
💼
LinkedIn
linkedin.com/in/farjadsyed
📍
Location
Austin, TX
(50%+ travel ready)
Schedule 30-Min Strategy Discussion Download Full Executive Brief

📥 Free Resource

5 Tenstorrent Adoption Risks—and How Solutions Engineering De-Risks Them