In the Wild West of AI Workloads, Which Metrics Should You Trust?
Part 2 of a three-part series
In Part 1, we established a simple principle for your AI strategy and approach: it starts with training, but inference and agentic is the function driving the results. If we learned anything from GTC conference this week, it’s that CPU’s are back in demand for AI workloads (ssshh – they always were in demand). Most enterprises are quickly realizing that using GPUs as general-purpose AI engines is not cost-effective.
Matching equipment (in this case, CPU/GPU/LPU) to workload requirements is our focus in part 2 of this blog series. And getting it right bends the cost curve in your favor before scale becomes a problem (given AI is designed for scale).
The Workload Routing Problem
Most enterprise AI environments default to GPUs for inference. But this approach (as shared in part 1) is equivalent to routing every welding job through a very expensive $2M robotic cell—when a cheaper $200K CNC station handles the job just fine.
The solution doesn’t come down to “CPU instead of GPU.” The alternative is intentional placement, routing each workload to the equipment that matches its actual requirements.
A Primary Driver: Latency Tolerance
We are still very much in the wild west of AI, with no standard benchmark for AI Inference. So, we look at other factors, and one driver dominates AI workload placement decisions: latency tolerance.
Does this workload require sub-second response times? Or can it tolerate seconds, or even minutes, of delay? This single question does more to determine correct equipment placement than model size, parameter count, or marketing claims.
Secondary factors refine the decision:
- Interaction pattern: Is a human waiting on a streaming response, or is this background processing where aggregate throughput matters more than individual response time?
- Concurrency at target SLA: High concurrent demand with strict latency favors accelerators and GPUs. Moderate concurrency with relaxed latency often does not.
- Optimization flexibility: Can the model be quantized, pruned, batched, or otherwise optimized to meet targets on flexible equipment?
When you map real enterprise workloads against these factors, a consistent pattern emerges.
Workloads Suited for Flexible Equipment
Most enterprise AI value comes from workloads that are latency-tolerant and throughput-oriented. These jobs require reliability, throughput, and cost efficiency far more than low latency.
- Batch classification and tagging
- Document summarization at scale
- Embedding generation for retrieval
- RAG pipeline preprocessing
- Data validation and quality checks
- Scheduled content generation
- Optimized model inference where latency targets allow
Workloads the Need Very Fast Response
Some workloads genuinely require GPUs, but these jobs rarely represent the majority of enterprise AI work today.
- Real-time generation with sub-second latency requirements
- Interactive applications with streaming responses
- High-concurrency inference at strict latency SLAs
- Complex reasoning chains where response time is critical
Another way to map the workload, business process, latency and your CPU/GPU ratio that affects your AI stack is below.
| Workload | Business Process | Latency Tolerance | GPU/CPU Ratio |
| Batch classification & tagging | Tagging 50,000 support tickets overnight for routing and reporting | High — seconds to minutes | CPU-first: Because throughput matters, but response time is flexible. Standard optimization handles load economically. |
| Document summarization at scale | Summarizing vendor contracts ahead of quarterly legal review | High — offline or scheduled | CPU-first: Jobs run in background pipelines. Accelerators add cost without improving business outcome. |
| Embedding generation for RAG | Indexing a product knowledge base for a customer self-service portal | Medium — pipeline-dependent | CPU-capable: Embedding models are well-suited to quantization; GPU justified only at extreme concurrency. |
| RAG pipeline preprocessing | Nightly chunking and re-ranking of updated policy documents for HR search | High — background processing | CPU-first: Chunking, ranking, and retrieval prep are throughput jobs, not latency-sensitive tasks. |
| Scheduled content generation | Generating weekly SKU descriptions for a retail product catalog | High — no human waiting | CPU-first: Batch scheduling absorbs latency variation; cost-per-output is the primary metric. |
| Interactive chatbot/copilot | Sales rep copilot surfacing deal history and next-best-action during a live call | Low — sub-second response expected | GPU-required: Human-in-the-loop with streaming response; TTFT and TPOT directly affect perceived quality. |
| Complex reasoning chains (agents) | Autonomous procurement agent evaluating supplier bids across multiple criteria | Low — response time is critical | GPU-required: Multi-step chains compound latency; accelerators prevent cascade delays. |
| High-concurrency inference at strict SLA | Patient triage assistant handling simultaneous queries across a hospital network | Low — latency non-negotiable | GPU-required: Goodput targets at scale can only be met with accelerator throughput. |
Beyond Latency: The Metrics that Matter
Enterprise AI metrics often fixate on raw throughput values like tokens per second or inference calls per minute. Without context, those numbers are meaningless. And what on earth is goodput? - See below
| Metric | What it means | When does it matter? |
| Time to First Token (TTFT) | How quickly the system starts responding after a request is made. | Critical for interactive workloads. Irrelevant for batch processing. Example - A sales rep asks, “Summarize this customer’s open issues.” If the first token takes 2–3 seconds to appear, the experience feels broken—even if the full answer is accurate. |
| Time per Output Token (TPOT) | How smoothly and quickly do tokens stream after the response starts? | Matters for streaming interfaces. Less relevant for offline jobs. Example: Live agent reasoning display An operations analyst watches a system “think through” steps. Long pauses between tokens reduce trust and usability. |
| Throughput | How much work gets done per unit of time? | Matters for batch workloads and background processing. Example: Batch ticket classification Classifying 100,000 support tickets overnight for routing and analytics. The goal is to finish before morning, not to respond instantly. |
| Goodput (Yield-like) | The number of requests successfully served within defined SLO budgets and meets SLA. | Enterprise chatbot with SLAs Out of 10,000 daily queries, how many responses arrive within the agreed latency and accuracy targets? How many queries were routed to “slow-response” vs “fast-response" to optimize goodput? |
Why Workload Placement Changes the Cost Curve
When workloads are placed correctly:
- Flexible equipment handles the majority of inference economically
- GPUs are reserved for work that actually needs them
- Utilization stabilizes instead of spiking and crashing
- Cost-per-output drops without changing hardware
None of this requires faster models or larger clusters. It requires discipline in how work is routed.
The Enterprise Takeaway
The question isn’t “CPU or GPU?”
The question is: "Which jobs require the high-speed line, and which don’t?"
Enterprises that answer this early avoid overbuilding, reduce operational drag, and reach sustainable AI economics sooner.
Next in Part 3: how organizations operationalize workload placement decisions, turning operational intent into repeatable practice without adding complexity.
Notices and Disclaimers
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