Full-stack infrastructure optimization, personalized for your workflow. Your people, your prompts, your agents and MCP tools, your context and your serving stack — all the way down to kernels, memory tiers, and hardware fit.
Cut your AI spend and get more out of every call. Optimizing your prompts and context matters as much as optimizing the kernel — so even if you never touch a GPU, we make your AI cheaper and sharper.
From the prompt to the kernel, memory tier, and accelerator choice. The entire stack, measured.
/01
Once real usage grows, the hard questions show up. Serve more customers without cost blowing up. Go faster without losing quality. Make agents reliable. Add features without rebuilding. Stop being locked to one provider.
Most teams start by guessing: switch the model, add a provider, try a cheaper endpoint, rewrite prompts, add caching, move to open models, buy GPUs, try vLLM, try SGLang, add observability, or start over. Sometimes that helps. A lot of the time, it just moves the bottleneck somewhere else.
Touchdown Labs reasons from the workload first: what customers are trying to do, where quality matters, where latency matters, where cost matters, and what becomes possible if the system were cheaper, faster, and more reliable.
/02 · The capability stack
The waste is rarely in one place. It is split across product design, model choice, serving config, the GPU itself, and the people who operate all of it — which is why fixing one layer just moves the bottleneck. One outcome, more capability per dollar, shown across every layer it actually lives in.
Capability infrastructure includes the people. The hardest layer is rarely the GPU — it is whether your team can operate the stack: engineer context, build reliable agents, make RAG retrieve, write real evals, and read inference cost down to the runtime. We optimize it with you, then teach your engineers to run it, which is where the rest of the stack finally pays off.
Agents that finish more tasks, retrieval that pulls better context, evals that catch real failures, workflows your team can maintain.
The right model for the right job, the right place to run it, and routing that picks correctly under real traffic.
vLLM, SGLang, Dynamo, TensorRT-LLM, LMCache: KV cache reuse, prefix caching, batching, disaggregation, runtime config.
When workload fixes are not enough: GPU memory, attention operators, quantization paths, CUDA and HIP kernel work, CXL and other memory tiers, and accelerator fit across NVIDIA, AMD, and emerging hardware.
Decide what stack actually fits your company: APIs, hosted open models, self-hosted GPUs, managed serving, agents, RAG, evals, routing, cache layers, serving engines, and infrastructure providers. Not what sounds cool. What fits your workload, team, budget, and performance target.
Understand where cost and latency are actually coming from: prompt shape, context length, retrieval payloads, retries, model routes, cache misses, provider choice, serving settings, batching, and GPU utilization. The goal is better cost, better performance, and more capability per useful AI task.
Improve the AI systems customers actually touch: agents that complete more tasks, RAG systems that retrieve better context, evals that catch real failure modes, routing that uses the right model for the right job, and serving paths that stay fast as usage grows.
Build and improve agents, RAG pipelines, eval loops, model integrations, product workflows, and production architecture. The goal is not a demo that works once. The goal is a system your team can maintain and improve.
Some teams need broad architecture help. Some only need a specific layer: KV cache, prefix reuse, routing, batching, serving engines, GPU memory, memory tiers, kernel paths, compiler behavior, interconnect pressure, or hardware-aware optimization. We help your team see deeper and move faster.
/03
There is no one correct AI stack.
A healthcare company may care most about reliability, traceability, and quality. A coding agent company may care about long-context performance, tool loops, KV cache reuse, and latency. A customer support company may care about cost per resolved ticket.
A startup may need the fastest path to something users love. A large enterprise may need something boring, secure, maintainable, and easy for their team to operate.
Touchdown Labs helps you make that tradeoff clearly: cutting edge when it matters, reliable when it matters, simple when it matters.
/04 · Open source & research
We start in real workloads. The reusable parts become open-source tooling and public research, so the evidence is inspectable rather than a pitch. Everything below is either shipping in the open or honestly labeled active research.
Open-source, KV/cache-aware serving health for vLLM and SGLang. It reads your serving metrics (read-only, from Prometheus), detects KV-cache, latency, queue, preemption, and swap anomalies, diagnoses the likely root cause, and recommends incident-scoped actions, then remembers the incident so the next one resolves faster. Advisory-first: it does not touch your production engine.
Repository → Docs →Technical briefings on what actually drives inference cost, latency, and reliability: KV cache, prefix reuse, serving-engine configuration, prefill/decode disaggregation, and hardware-aware work across Hopper, Blackwell, and AMD Instinct. Written from real workloads, not benchmarks-as-marketing.
Read the research →Where we are heading next, in the open: workload-first benchmarking beyond text into image, video, and voice inference (against suites like VBench, GenEval, and the Open ASR Leaderboard), and the people layer, turning real production problems into labs and curriculum that make teams AI-native at context, agents, RAG, and evals. Active research, and moving fast.
/05
Most benchmarks are too clean. Your product is not clean. Your users have long sessions, your agents call tools, your RAG pipeline sends too much context, your traffic comes in bursts, your retries are hidden, and your cache works until the prompt layout changes.
Measure session degradation, prefix reuse, KV-cache pressure, cold-start vs. warm-cache latency, and long-context cost growth.
Measure file reads, multi-file edits, test loops, code review, tool-call interleaving, context saturation, batch scheduling, and latency under mixed workloads.
Measure retrieval payload cost, prefill-heavy throughput, scheduling fairness, KV-cache churn, retry cost, and tool-loop latency.
Measure burst tolerance, queue depth, overload recovery, capacity cliffs, and cost under realistic concurrency.
If the benchmark does not look like your product, it will not tell you what to fix.
/06 · InferGuard
Raw vLLM and SGLang counters do not tell you what is wrong. InferGuard scrapes your serving metrics read-only and turns them into something actionable: it detects anomalies, diagnoses the likely cause, recommends incident-scoped actions, and remembers the incident. Advisory-first by design, it never mutates your engine.
KV-cache, latency, queue, preemption, and swap anomalies, surfaced from read-only Prometheus metrics.
Likely root cause for each anomaly, with a deterministic fallback when no model is configured.
Incident-scoped advisory actions, plus incident memory so the next occurrence resolves faster.
Watch cache pressure, eviction, and swap events as load grows.
Track TTFT/TPOT drift, queue depth, and the onset of latency cliffs.
Catch preemption storms and batching behavior under real concurrency.
Scrapes metrics only; optional actuation is gated, allowlisted, and off by default.
/07
Lower cost matters. But the bigger point is what lower cost and better performance make possible.
Serve more demand without the AI bill destroying margins.
Make more workflows economically viable.
Complete more tasks, reduce retries, and keep latency under control.
Use stronger models where quality matters and cheaper paths where they are enough.
Move across APIs, GPUs, and hardware as the market changes.
/08
The cost, latency, and margin of a production AI system are decided far below the model — in the runtime, the kernel, the memory hierarchy, the interconnect, and the fit between workload and hardware. Our research direction is to help customers and hardware partners make better buying, migration, and roadmap decisions across many kinds of compute. The business problem is software portability: teams need a CUDA-like operating layer for inference that can move workloads across APIs, GPUs, accelerators, memory systems, and clouds without rewriting the whole operation or trusting vendor benchmarks alone. We build open-source tools where the ecosystem needs shared proof, and internal software where customers need workload-specific adapters, evidence, partner validation, and hardware-fit analysis.
State-placement co-design across HBM, host memory, CXL-attached memory, disk, and remote tiers — cache budgets, prefix reuse, quantization, and eviction behavior under sustained load.
Prefill/decode separation, KV-aware routing, transfer overhead, NIXL movement, and memory-tier behavior across modern serving architectures.
Business-facing research for choosing and adopting different hardware without locking the workload to one vendor. We compare software paths, runtime behavior, memory pressure, accelerator fit, migration cost, and cost per successful request so buyers know which workloads can move, which adapters are needed, and what evidence is still missing.
Throughput and cost per useful task are decided below the serving layer: GPU memory, data movement, kernels, compiler paths, interconnect, and accelerator architecture. This is an active research direction, not a claim that one hardware path already wins. We measure what real workloads need, then translate repeated bottlenecks into software changes, partner hardware validation, and eventual IC design inputs.
Research with hardware partners starts from workload receipts: p50/p95/p99, TTFT, TPOT, quality, bytes moved, cache reuse, GPU stalls, power, topology, and cost per SLO-successful request. The goal is to show when a memory tier, accelerator, appliance, or interconnect helps — and when it does not.
Configuration review for vLLM, SGLang, TensorRT-LLM, and Dynamo across batching, memory, parallelism, and runtime flags.
Benchmarking and profiling for coding agents, RAG pipelines, multi-turn chat, long context, and tool-heavy workloads.
Client-side profiling for hosted model APIs, neocloud endpoints, and hardware-backed deployments when teams cannot access every server-side metric.
/09
Technical briefings, workload analysis, research notes, and selected coverage from the frontier of inference optimization.
A CEO/CFO-readable map of how automated CUDA, Blackwell FP4, kernels, serving engines, and workload evidence can increase revenue per GPU.
Read the post →A verified step-by-step MacBook Pro guide for using MLX, Gemma 4, Qwen3.6 comparisons, OpenClaw, and Hermes to mimic LMCache/vLLM/SGLang-style KV observability on a 16GB M5.
Read guide →vLLM x Mooncake, LMCache MP, LMCache observability, SGLang, Dynamo, VLM encoder disaggregation, Modal cold starts, and what InferGuard is trying to measure without overclaiming.
Read briefing →Agentic coding workloads are changing the CPU:GPU contract. TokenSpeed is one runtime signal. Dynamo is one distributed-serving signal. Vera Rubin is part of the hardware roadmap.
Read briefing →Why performance does not always degrade smoothly. Sometimes cost, latency, and reliability collapse after a workload crosses the true serving limit.
Request briefing →/10
The goal is not to chase every new AI tool or overclaim a hardware roadmap. The goal is to help companies build AI architecture that compounds, while turning repeated workload evidence into research-grade hardware requirements.
/11
Our wedge is practical: we help companies make production AI cheaper, faster, and more reliable — inference cost, latency, reliability, and the workload bottlenecks underneath them. That is the work we do today.
The bigger company is a bet on one insight: the bottleneck in AI is no longer just compute. It is the rate at which humans, companies, and institutions can understand and operate the compute. AI infrastructure is no longer just APIs and GPUs. It is serving engines, schedulers, prompts, context, KV cache, memory movement, routing, multimodal workloads, memory tiers, accelerators, and edge deployment, and the complexity is compounding faster than human capital can keep up.
That knowledge is fragmented. Engineers self-teach from papers, GitHub issues, Discord threads, and vendor docs; traditional education is too slow and disconnected from real production systems. We close that gap company-first: real customer problems become open-source tooling, research, and curriculum. Research dictates what we teach, so every bottleneck we find becomes a lab, every repeated failure mode becomes a playbook, and every strong engineer becomes talent for our customers, partners, and ecosystem.
This is the loop: customers create the problems, the evidence layer captures what works, research extracts the patterns, education scales the knowledge, and talent expands the ecosystem — which brings more problems back in. Long term, the same workload evidence can guide infrastructure design across cloud, edge, and local environments, toward hardware and software shaped by what real workloads actually need. That hardware work is a research direction: partner validation first, IC design inputs only when the evidence supports them.
We call it capability infrastructure: compute, software, context, evidence, people, and education. The technical stack, and the human stack that makes it work.
Production AI optimization today: inference cost, latency, reliability, and the workload bottlenecks underneath them, across APIs, hosted models, and self-hosted GPUs.
The repeatable diagnostics become open-source tooling and, over time, a platform that captures what actually works under real load.
Patterns from real systems become benchmarks, open-source tools, and curriculum that stays current, because it is fed by live production problems, not last year's syllabus.
As workload evidence accumulates, it can guide infrastructure design across cloud, edge, local, accelerator, and memory-tier environments, toward hardware and software shaped by real workloads.
/12
We work with teams that are serious about making AI work in production: founders, CTOs, engineering teams, infrastructure teams, finance and operations leaders, schools, and enterprises that need practical AI infrastructure education.
/13
Start with the business, map the workload, measure the system, design the architecture, build and optimize, teach the team, then standardize what works.
Users, workflows, revenue model, quality bar, latency needs, reliability needs, cost constraints, and what your team can realistically operate.
Prompts, retrieval, agents, tools, model calls, evals, retries, routing, serving paths, cost drivers, and latency targets.
Find whether the bottleneck is product design, prompts, retrieval, retries, evals, routing, batching, KV cache, runtime config, GPU pressure, or infrastructure choice.
Choose the right path: APIs, hosted models, self-hosting, managed serving, serving engines, cache layers, routing, evals, or deeper infrastructure work.
Implement or improve agents, RAG pipelines, evals, model integrations, serving paths, reliability controls, infrastructure architecture, and optimization work.
Train your team, then turn repeated patterns into traces, workload receipts, tools, benchmarks, playbooks, and courses.
/14 · What we offer
From your people and how they prompt, build agents, and wire up MCPs, through models, routing, serving engines, KV cache, GPUs, kernels, memory tiers, and accelerator fit, we optimize and teach the complete thing. Hardware and custom-IC work is treated as a research direction: measured workload evidence first, partner validation next, architecture requirements only where the data supports them.
Most teams only know one slice of the stack. We teach yours the whole thing on your own workflow: how to prompt, build reliable agents, wire up MCPs, use coding tools like Claude Code, write real evals, and read inference cost. Broader upskilling and next-generation talent run through partners.
The layer your users touch: prompts, agents, MCPs, context, tool loops, and the evals that catch real failures before customers do.
Which model, run where: APIs, hosted open models, or self-hosted GPUs, with routing that picks correctly under real traffic and a real budget.
vLLM and SGLang configuration, KV cache and prefix reuse, batching, prefill/decode disaggregation, and runtime flags.
When workload fixes are not enough: GPU memory, quantization paths, attention operators, CUDA / HIP kernel work, CXL and other memory tiers, accelerator fit, and research-grade hardware/software co-design driven by workload evidence.
An inference diagnostic that shows where cost, latency, and reliability actually come from, plus InferGuard, our open-source serving-health tooling, so your team keeps the evidence after we leave.
InferGuard docs →