Optimize Claude & Ollama Tokens

@corpmember29

I CAN TURN 850M DAILY TOKENS INTO A LEANER, FASTER AI CODING WORKFLOW. Hi, I’m Christina from Technorizen Software Solutions, with 11+ years of experience across AI, backend systems, and performance optimization. I’ve worked on similar LLM/code-agent optimization projects involving Claude, Ollama, Qwen, prompt pipelines, caching, context management, and model routing. I understand you don’t just need generic optimization—you need a measurable audit of where tokens and latency are being wasted. I’ll trace prompt/function chains, identify redundant context and calls, implement token guards, prompt refactoring, caching and smarter routing, then tune Ollama/H200 performance with batching, concurrency, quantization and CUDA settings. You’ll receive before/after token metrics, optimized source/configs, and a reproducible local + Vast setup guide. Share your logs and sample prompts, and let’s jump on a quick call to identify the biggest savings first.

@ShaikhAneesa

Hi there, I understand that you need a deep audit of your Claude Code, Skills, Ollama, and Qwen workflow to identify where excessive token usage and performance bottlenecks are occurring across your local and Vast H200 environments. I’m confident I can optimize the prompt chains, function calls, context handling, model configuration, and routing logic to reduce daily token consumption without sacrificing performance. My approach is to first audit the prompt chains, function calls, logs, context windows, model usage, and generation patterns to establish exactly where tokens are being over-allocated. Next, I’ll apply code-level optimizations including prompt refactoring, context trimming, token guards, caching, batching, concurrency controls, and smarter model routing. Then, I’ll optimize the local and H200 environments through appropriate CUDA, quantisation, inference, and resource configuration. Finally, I’ll benchmark before/after token usage and performance, validate the changes, and document the optimized setup. You will receive the hotspot report with before/after token counts, updated source/configuration files, optimization changes, and concise setup guides for both environments. Can you provide the current token-usage logs and representative prompt/function traces so I can identify the highest-impact hotspots before changing the stack? I’m ready to start immediately. Warm Regards, Aneesa.

@carlosd178

Hello, As a result of a detailed review of your requirements, I understand that the priority is not simply speeding up the AI stack, but identifying why Claude Code + Skills and Ollama are consuming close to 850M tokens/day and reducing that usage without hurting development quality or throughput. I have experience with LLM integration, prompt/context optimization, AI workflow automation, inference profiling, Ollama, CUDA/GPU environments, caching, batching, and model routing. In my opinion, the key challenge is separating necessary context from repeated or uncontrolled token growth. I would instrument the current chains first to measure tokens per prompt, tool call, retry, and response, then address duplicated context, oversized histories, unnecessary agent loops, missing token guards, cache opportunities, and inefficient Claude/Ollama routing. For the H200 environment, I can also profile GPU utilization, batching/concurrency, KV-cache behavior, quantization, and inference configuration before recommending changes or alternative models. I have a couple of quick questions. • Do your logs currently record per-request input/output token counts and latency? • Is Claude Code being invoked directly or through an orchestration layer/API wrapper? I would be glad to review sample logs and prompts first and establish a measurable before/after baseline. Best regards, Carlos.

@diektech

Hi — Elias here from Miami. I understand you're looking to optimize the token usage for your Vibe coding project, which is consuming a significant number of tokens daily. Managing such high token consumption is crucial for efficiency and cost control. What usually matters most here is ensuring your token management aligns with the workflows and integrations you're using. A common issue in systems like yours is handling token exhaustion during peak usage, which can lead to interruptions or degraded performance. The tricky part is usually balancing performance while keeping costs in check. My approach would focus on analyzing the existing workflow to identify bottlenecks and inefficiencies. I’d recommend implementing a caching mechanism to reduce unnecessary token calls and optimizing your current API interactions. This way, we can maintain a balance between functionality and resource management. I've worked on similar optimization projects before, specifically in AI-driven environments where token usage was a critical factor. A few questions to better understand the scope: Q1 – What specific workflows or integrations are contributing most to token consumption? Q2 – Are there any peak usage times we should account for in our optimization strategy? Q3 – Do you have a specific target for token reduction that you’re aiming for? Looking forward to hearing from you.

@RusianD

Hello, I’ve carefully reviewed the requirements and believe I’m a strong fit for optimizing your Claude and Ollama workflow. My focus will be on reducing unnecessary token consumption while preserving context quality, code accuracy, and useful model output. I understand you need to analyze how prompts, code context, conversation history, and local Ollama models interact with Claude, then improve the workflow to use tokens more efficiently. My plan is: Audit: I will inspect prompts, context assembly, model calls, logs, and current token usage to identify the main sources of waste. Context: I will reduce redundant code and history while retaining the files and dependencies required for each task. Prompts: I will restructure instructions and outputs to minimize repeated context and unnecessary responses. Routing: I will determine which tasks can run through Ollama locally and reserve Claude for cases that benefit from stronger reasoning. Caching: I will implement reusable summaries or cached context where appropriate to avoid resending unchanged information. Testing: I will compare token usage, output quality, latency, and code correctness before and after optimization. I have experience with Claude, Ollama, LLM APIs, prompt engineering, RAG, Python, and AI agent workflows. I’d welcome a short chat to align the implementation. Best regards, Ruslan

@intelliresponse

Hello, I have experience optimizing AI-assisted development workflows, including prompt engineering, LLM integration, context management, and reducing unnecessary token usage. I can audit your current Claude Code and Ollama workflow to identify where tokens are being wasted and provide practical improvements. I will review prompt chains, function calls, context handling, and model usage patterns, then suggest optimizations such as prompt restructuring, context trimming, caching strategies, better model routing, and local inference configuration improvements. I can also help tune your Vast/H200 setup with quantization, batching, concurrency, and performance settings. The goal will be to significantly reduce daily token consumption while keeping coding quality and speed. I will provide a clear report with findings, before/after comparisons, updated configuration recommendations, and a guide for reproducing the optimized setup. I focus on measurable improvements rather than unnecessary changes.

@Hubready

Hello!, This is James from Hollywood... Your real problem is not just using too many tokens, it’s finding where Claude/Ollama are being wasted, then tightening the workflow so you keep the speed without paying for noise. I’d handle it in 3 quick phases: 1) Token audit to identify the prompts, retries, hidden context, and tool calls causing the burn. 2) Prompt/workflow compression to reduce repeated instructions, slim system context, summarize history, and split tasks so only the needed model sees the needed context. 3) Runtime controls like context limits, caching, prompt templates, and smart routing between Claude and Ollama based on task type and cost. That way you’re not just “optimizing tokens”, you’re cutting waste without breaking output quality. I’ve done similar work in AI workflow automation, LLM integrations, code review pipelines, and high-volume systems where cost control mattered just as much as accuracy. Recent builds included a support copilot, an internal document Q&A tool, and an automation-heavy SaaS workflow engine. A few things I’d want to confirm: - Is the 850M/day coming from one app, one agent flow, or multiple experiments? - Are you trying to reduce Claude usage, Ollama usage, or both while keeping the same quality? - Do you already have logs/traces for prompt length, retries, and tool calls, or should I help add that first? If you want, I can start by mapping the top token leaks and give you a practical fix plan fast.

@shayankhan333

850M tokens a day almost always traces back to unbounded context replay, every Claude Code call resending the full skill definitions and file history instead of a diffed slice. I'll audit your prompt chains and log a hotspot table with before/after token counts, then refactor with prompt caching, context trimming and a small router that sends cheap calls to Qwen or a quantised local model. For the H200 I'll tune batching, concurrency and quantisation so throughput climbs. I can jump in right away. 1. Which Claude models and Ollama versions are in the chain? 2. Can you share a day of raw request logs? Thanks Shayan

@jaroslavcaprata

Hi, I can audit this stack at both the LLM workflow and infrastructure levels. With token usage around 850M/day, I’d focus first on identifying repeated context, oversized prompt chains, unnecessary tool/function outputs, retry loops, and places where Claude Code or Ollama are carrying far more context than required. I can instrument the current workflow to measure token usage per call, then optimize it using context pruning, prompt restructuring, token guards, caching, response limits, model routing, batching, and concurrency controls. I’ll also review the H200 setup for CUDA utilization, quantization, KV-cache behavior, model loading, and inference settings so token savings don’t come at the cost of throughput. You’ll receive a clear before/after breakdown for each hotspot, updated or annotated code/config files, and reproducible setup instructions for both local and Vast environments. I can also evaluate whether some Claude/Ollama calls should be routed to Qwen or smaller models where task complexity doesn’t justify the larger model.

@PrateekP3003

With over a decade of experience in building and optimizing complex AI systems like Claude and Ollama, I am confident that I can help you significantly reduce your token consumption while maintaining or even enhancing system performance. Your project's emphasis on enhancing AI system efficiency rather than just applying quick patches resonates strongly with my approach to work. I have a proven track record of building, refining, and deploying AI models at scale that deliver exemplary results even with unstructured, real-world data. I am well versed in AI development languages including OpenAI, Claude, Gemini, Llama and can leverage other powerful tools like LangChain and LlamaIndex to optimize your current workflow. My expertise goes beyond just the code level; I understand the importance of comprehensive configuration tweaks to improve the system's overall performance. Additionally, my proficiency in deploying systems using AWS and GCP aligns well with your need for replication of the optimized environment both locally and on the Vast server. Throughout my projects, I prioritize delivering working and integrated systems. For this reason, you can expect not only pinpointed parts of your codebase that needs attention but also documented recommendations on how to restructure your code along with any additional tools or models that might help minimize token consumption further.

@muneeb6767

I can audit your Claude Code/Ollama prompt chains and function calls to identify token hotspots, then optimize context trimming, prompt design, caching, token guards, routing, and H200/local model settings for lower cost and better latency. I’ll provide before/after token benchmarks, ready-to-use code/config changes, and a reproducible setup guide for both environments.

@rbtech1984

Hello!! The Claude Code, Ollama, and Qwen workflow will be audited to identify the main causes of excessive token usage and slow H200 performance, then optimized with measurable before and after results. * Can you share a sample of your token usage logs? * Which part of the workflow currently uses the most tokens? * Is the existing code ready for optimization? The work will cover prompt and function-call analysis, context trimming, token limits, caching, model routing, and performance tuning for both local and Vast H200 environments. Relevant AI, LLM, Python, and automation optimization projects have been completed before, reducing unnecessary processing while keeping output quality and performance strong. Let us review your current workflow in chat and find the biggest savings first. Best regards Farhin B

@miguelcen64

Hello, your Claude Code and Ollama token burn is really about controlling runaway context growth and expensive routing decisions without slowing the workflow. I work regularly with AI Development, AI Automation, and AI Training Data, which fits closely with what you’re trying to accomplish. I’d start by reviewing your current prompt chains, logs, and repo setup, then handle prompt refactoring and token-length guards in a structured way, testing local and H200 server behavior as I go rather than leaving validation until the end. The goal would be to deliver a leaner stack that is measurable, stable, and cost-efficient. I can begin immediately and should have this completed within 20 hours. One thing I’d like to confirm before starting: which model-routing rules or agent layers are currently allowed to change? Best regards, Miguel

@AhmadXpert2410

Hi there, I can audit your Claude Code + Skills workflow, Ollama/Qwen calls, prompt chains, and function execution to identify exactly where the ~850M daily token usage is coming from. I’ll trace the request flow, context accumulation, repeated tool calls, oversized prompts, unnecessary model invocations, and response lengths, then establish a before/after baseline so the optimizations are measurable rather than theoretical. I can implement code-level reductions through prompt restructuring, context trimming, token guards, selective history, caching, deduplication, model routing, and smarter tool/function invocation. On the H200/Vast side, I’ll also review CUDA/runtime configuration, quantization, batching, concurrency, KV-cache usage, and inference settings to address both token consumption and the slowdown you’re experiencing. Where appropriate, I’ll evaluate whether Claude, Ollama, Qwen, or another model should handle specific workloads instead of sending everything through the most expensive path. You’ll receive a focused hotspot report with before/after token metrics, annotated source/config changes ready for your repo, and a concise local + Vast H200 setup guide. I’ll prioritize measurable reductions in total token usage while preserving or improving throughput, and can work directly from your logs, prompts, traces, and existing configuration to avoid unnecessary rewrites. Regards, Ahmad

@PINESCRIPTERR

Hi, I can audit the full agent execution path and distinguish useful model work from repeated context, oversized tool output, recursive retries, duplicated system prompts, and unbounded completion budgets. I’ll instrument each model and tool call with input, cached-input, output, latency, retry, and cost metrics while redacting credentials and sensitive repository content. Code-level improvements may include task-specific context packs, repository indexing, diff-only tool responses, strict token budgets, structured outputs, semantic and deterministic caching, history summarisation, loop detection, bounded retries, and model routing by task complexity. I’ll validate each change against a fixed workload so reductions do not quietly damage correctness. For Ollama/Qwen on the H200, I’ll profile GPU utilisation, KV-cache pressure, context length, batching, parallelism, quantisation, CPU offload, storage, and network overhead before changing settings. I’ll also compare Ollama with suitable high-throughput inference engines where migration offers measurable value. Deliverables will include hotspot findings, reproducible before/after benchmarks, annotated code/configuration, and separate local/Vast setup instructions. I won’t promise an arbitrary reduction before establishing a measured baseline. Regards, Houssame

@rajeshrolen

Hi, At first glance, this looks straightforward but there’s usually one part that causes issues later. Happy to share a quick plan if you're open to it. Regards, Rajesh

@longngoc2307

==== Hi - Truong here ==== "CLAUDE & OLLAMA TOKEN OPTIMIZATION" — you need to find where 850M daily tokens are being wasted and reduce usage without making the workflow slower. I’d trace the prompt/function chains first, measure token usage per stage, then target repeated context, oversized tool outputs, unnecessary retries, and missing caching. For Ollama/H200, I’d separately check batching, concurrency, quantization, CUDA settings, and model routing so token savings don’t create another performance bottleneck. Can you provide the token-usage logs and a representative prompt/function chain showing the highest daily usage? Looking forward to work with you

@husainahmed2000

Hi There!!! i understand you need to drastically reduce token consumption and latency across your Claude Code and Ollama pipelines while optimizing execution on your local machine and remote H200 server. I HAVE EXTENSIVELY AUDITED LLM WORKFLOWS, IMPLEMENTED CONTEXT TRUNCATION AND PROMPT CACHING, AND TUNED GPU INFERENCE ON SIMILAR PROJECTS. Token audit of prompt chains and tool calling loops to eliminate redundant context Prompt refactoring with structured caching and strict context window management Inference optimization on H200 including quantization, batching, and CUDA parameter tuning Intelligent routing between local small models and remote APIs to reduce API token volume Concise implementation guide and before-and-after token efficiency benchmarks Would you be open to a quick chat to review your prompt logs and pipeline configuration? Best Regards, Hussain Ahmed

@shailender18

Hello, I’m an AI development and optimization professional with 5+ years of experience in AI integrations, LLM workflows, automation, code review, API pipelines, and performance optimisation. I can audit your Claude Code + Skills, Ollama and Qwen workflow to identify where the 850M daily tokens are being unnecessarily consumed. I’ll trace prompt chains, function calls, context propagation and repeated model requests, then apply practical optimisations including prompt refactoring, context-window trimming, token guards, response limits, caching, deduplication, batching and intelligent model routing. I’ll also review Claude/Ollama usage patterns to determine where smaller or local models can replace expensive calls without reducing output quality. For the Vast H200 environment, I’ll review CUDA/runtime configuration, quantisation, batching, concurrency, GPU utilisation and inference settings to address both cost and latency. Local and remote configurations will be documented for reproducible deployment. You’ll receive a hotspot report with before/after token metrics, updated source/config snippets, and a concise setup guide. I’ll use your logs and representative prompts to measure actual improvements rather than relying on assumptions. Best Regards, Shailender