GPU / HBM
Intent interpretation, planning, tool selection, code generation, and final synthesis. This is the token-billed step that requires accelerators and high-bandwidth memory.
AI agent work consumes more than tokens: GPU inference, CPU cycles, RAM, storage, network bandwidth, search indexes, container sessions, and model context.
An AI coding agent feels like one product, but the workflow is a chain of model reasoning, search, retrieval, file I/O, shell execution, browser automation, storage, and final synthesis.
Cloud financial operations respondents managing AI spend. Source: Linux Foundation, 2026.
NVIDIA H200 HBM3e high-bandwidth memory capacity, with 4.8 TB/s bandwidth. Source: NVIDIA.
OpenAI web search preview for reasoning models per 1,000 calls. Source: OpenAI pricing.
OpenAI file-search tool-call rate for the Responses API per 1,000 calls, separate from token charges. Source: OpenAI pricing.
Every AI coding agent and computer-use tool executes a mixed workload: GPU inference for reasoning, CPU for file and code tools, RAM for retrieval and browser state, storage for indexes and cache, network for search and API calls, and containers for shell execution.
Intent interpretation, planning, tool selection, code generation, and final synthesis. This is the token-billed step that requires accelerators and high-bandwidth memory.
File search, grep, syntax-tree parsing, test runners, shell commands, browser APIs, SQL queries, log scanning, and data transforms.
The token line item captures the model's decision to call a tool and the model's reading of the result. Other resource types remain less visible on the invoice.
The product experience is conversational. The execution path is a mixed computing pipeline.
Intent, planning, tool choice, judgment, code generation, and final synthesis. This is the token-heavy GPU/accelerator step.
File search, grep, syntax-tree parsing, web retrieval, shell commands, tests, SQL, browser APIs, and log parsing.
Vector stores, embeddings, search results, tool schemas, repo indexes, build outputs, container sessions, and retrieved snippets.
The engineering question is where each step belongs. LLM inference fits accelerators and high-bandwidth memory. File search, tests, parsing, logs, and browser control mostly fit CPU, RAM, disk, and network.
Bar widths are illustrative relative cost indexes, not measured ratios.
| Provider | Token signal | Tool and platform signal |
|---|---|---|
| OpenAI | GPT-5 and GPT-5.1: $1.25 input, $10 output per 1M. GPT-5.5: $5 input, $30 output per 1M under 272K context. | Web search preview for reasoning models: $10/1k calls. File search: $2.50/1k Responses API tool calls plus storage. Containers $0.03 to $1.92 per 20-minute session equivalent, with eligible sessions billed by minute. |
| Claude | Opus 4.8: $5 input, $25 output. Sonnet 4.6: $3 input, $15 output. Haiku 4.5: $1 input, $5 output. | Tool schemas, tool_use blocks, tool_result blocks, and tool system-instruction tokens count as input. Server tools may add usage charges. |
| Kimi K2.7 Code | $0.19 cache-hit input, $0.95 cache-miss input, $4 output per 1M. HighSpeed lists $0.38, $1.90, and $8. 262,144-token context. | $web_search is $0.005 per successful tool call. Search results count as continuation tokens. |
| DeepSeek | deepseek-chat: $0.27 cache-miss input, $1.10 output. deepseek-reasoner: $0.55 cache-miss input, $2.19 output. | Cache-hit economics are explicit, which rewards repeated context and stable workflows. |
| xAI Grok | Input, reasoning, completion, image, and cached input-token categories. | Server-side tools add invocation economics. File storage $0.025/GiB/day, collection storage $0.10/GiB/day, downloads $0.20/GiB. |
Search results, file chunks, command output, screenshots, and tool schemas often become model input. That is where CPU work turns into token spend.
Paste logs, files, search pages, or browser snapshots directly into long context. Costs rise with little quality gain.
Run deterministic filters first. Return citations, failing lines, stack traces, changed symbols, and ranked snippets.
Reuse repo indexes, embeddings, search results, tool schemas, test outputs, and prior analysis across requests.
AI demand stresses high-bandwidth GPU memory, DRAM, NAND, storage, and device supply chains. The result shows up in cloud capacity, model pricing, cloud finance priorities, and consumer hardware.
NVIDIA frames H200 around larger and faster high-bandwidth memory for generative AI and LLM workloads.
AP tied the increases to a memory chip shortage driven by the AI boom. TechCrunch's examples imply roughly 18% to 25% increases across several Mac models.
AI cost management has moved from emerging concern to mainstream technology value management.
The cost-efficient path starts with deterministic execution, then retrieval, then low-cost models, then frontier reasoning.
Bar widths are illustrative relative cost indexes, not measured ratios.
The system records each step, selects the substrate, and sends compact evidence to models instead of sending whole workspaces.
Tool calls, model calls, context size, output size, cache hits, file bytes, runtime, browser steps, and result quality.
CPU/RAM services first, cached retrieval second, low-cost language work third, frontier reasoning last.
Selected snippets, failing tests, stack traces, citations, structured data, and concise tool outputs.
Expose the cost ledger: tokens, tool calls, storage, CPU minutes, RAM tier, and review effort.
This is a workload placement decision based on quality, risk, latency, data policy, and cost.
Classify, extract, route, normalize, format, translate, and summarize predictable inputs.
Batch enrichment, coding drafts, long-context review, retrieval summaries, and non-critical agent loops.
Hard debugging, architecture tradeoffs, security analysis, ambiguous computer-use steps, and final synthesis.
| Task | Default route | Escalation route | Meter |
|---|---|---|---|
| Find files, symbols, references | Repo index, grep, syntax-tree parser | Model summarizes selected evidence | Files scanned, snippets sent |
| Run tests and builds | Shell or CI container | Model explains failures and writes fixes | Runtime, output bytes |
| Analyze logs and traces | Parser, query engine, anomaly detector | Model builds causal narrative | Rows scanned, anomalies |
| Browse known workflows | Browser API, Playwright, CDP (Chrome DevTools Protocol) | Model handles ambiguous page state | Browser runtime, screenshots |
| Search the public web | Search tool with caching | Model reads top extracts | Search calls, retrieved tokens |
| Make security or architecture calls | Curated evidence plus frontier model | Human review for production risk | Frontier tokens, review time |
Record model calls, tool calls, context size, output size, cache hits, file bytes scanned, test runtime, browser runtime, storage, and result quality.
Move search, parsing, retrieval, tests, browser steps, SQL, and log analysis into CPU/RAM services with compact evidence returned to models.
Define thresholds for small models, open-weight routes, and frontier closed models. Combine cache, batch, and priority controls by urgency and risk.
All numbers in the deck trace to the full report. Provider prices change. Current purchasing depends on live rate cards and negotiated contract terms.
Model tokens are the scarce reasoning budget. CPU, RAM, storage, search, and containers are separate resources. Good architecture exposes them separately.
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