{"slug": "ollama-to-vllm-when-to-migrate-your-local-llm-server", "title": "Ollama to vLLM: When to Migrate Your Local LLM Server", "summary": "Ollama's simplicity can mask when a local experiment becomes a shared inference service, and vLLM offers better scheduling and observability for production workloads. Migration is warranted when multiple users cause unstable latency, concurrency limits are hit, or production metrics are needed, but it involves trading simplicity for control. A staged approach keeps both servers running during validation.", "body_md": "# Ollama to vLLM: When to Migrate Your Local LLM Server\n\nWhen to move from Ollama to vLLM\n\nOllama is one of the easiest ways to run a local language model, but convenience can conceal the moment when a local experiment becomes a shared inference service that needs better scheduling and observability.\n\nThat is where vLLM becomes relevant. Migrating from Ollama to vLLM is not an automatic upgrade, however. It is a trade: you exchange some of Ollama’s simplicity for greater control over batching, memory management, concurrency, distributed inference, and production operations.\n\nThis guide covers the practical signals that indicate migration is warranted, the risks of moving too early, and a staged approach that keeps both servers running side by side during validation. The goal is to help you decide based on measurements rather than feature lists. For the wider landscape of local, self-hosted, and cloud options beyond just these two runtimes, see [LLM Hosting in 2026: Local, Self-Hosted & Cloud Infrastructure Compared](https://www.glukhov.org/llm-hosting/).\n\n## Ollama and vLLM Solve Different Problems\n\nOllama is primarily optimized for convenient model consumption. It gives developers a [concise command-line interface](https://www.glukhov.org/llm-hosting/ollama/ollama-cheatsheet/), a local API, a model library, Modelfiles, and straightforward support for common desktop and workstation configurations.\n\nvLLM is an inference engine and serving platform. Its central concerns are high-throughput request scheduling, efficient KV cache management, continuous batching, model parallelism, and compatibility with applications built for OpenAI-style APIs.\n\nThe distinction matters because the two servers can look similar from the outside. Both can expose a chat API, stream tokens, run quantized models, and serve local applications. Their operating models become visibly different only when the server is placed under sustained or concurrent load.\n\nA useful summary:\n\n| Requirement | Ollama | vLLM |\n|---|---|---|\n| Fast local setup | Excellent | More involved |\n| Curated model downloads | Excellent | Usually Hugging Face based |\n| GGUF workflow | First-class | Supported, not main strength |\n| Single-user chat | Excellent | Often unnecessary |\n| Concurrent API traffic | Limited but configurable | Core use case |\n| Continuous batching | Not the primary model | Core feature |\n| Prefix cache reuse | Limited operational control | Built-in optimization |\n| Multi-GPU model serving | Limited compared with vLLM | Tensor and pipeline parallelism |\n| Production metrics | Basic response timing data | Prometheus metrics endpoint |\n| Deployment tuning | Minimal | Extensive |\n\nThe question is not which server is universally better. It is whether your workload still matches the operating model that makes Ollama attractive. If you want the fuller picture across more than these two runtimes, [our comparison of Ollama, vLLM, LocalAI, Jan, LM Studio and other local LLM tools](https://www.glukhov.org/llm-hosting/comparisons/hosting-llms-ollama-localai-jan-lmstudio-vllm-comparison/) covers the broader field.\n\n## Signs You Have Outgrown Ollama\n\nA slow response does not, by itself, justify a migration. Generation speed is often constrained by model size, quantization, memory bandwidth, prompt length, or GPU capability rather than the serving engine, and the stronger migration signals only appear once workload shape itself starts to matter.\n\n### Multiple Users Cause Unstable Latency\n\nA local LLM server can feel fast during an isolated test and then degrade sharply when several clients connect. Requests begin waiting behind long generations, time to first token becomes inconsistent, and a single large prompt can affect everyone sharing the model.\n\nOllama can process parallel requests, and `OLLAMA_NUM_PARALLEL`\n\ncontrols how many requests a loaded model may handle concurrently — see [how Ollama handles parallel requests](https://www.glukhov.org/llm-performance/ollama/how-ollama-handles-parallel-requests/) for the queuing and memory mechanics behind that setting. That parallelism is not free: memory requirements grow with both the configured parallel request count and context length.\n\nThis is often the first practical warning. A configuration that works for one 8K conversation may become impossible when four clients each reserve a much larger context.\n\nvLLM is designed to combine work from active requests through continuous batching. Instead of treating each request as an isolated inference job, it continuously updates the batch as sequences arrive, generate tokens, and finish — a scheduling model that generally becomes more valuable as concurrency increases.\n\n### GPU Utilization Is Low While Requests Are Queued\n\nA queue does not necessarily mean that the GPU is fully used. In a simple serving arrangement, work may be serialized even though additional requests could have contributed useful computation to the current decode step.\n\nvLLM’s scheduler is designed to keep more useful work in flight. PagedAttention manages KV cache memory in blocks, while continuous batching allows active sequences to enter and leave the execution batch dynamically.\n\nThe result is not guaranteed to be lower latency for every individual request. Under load, however, it can produce substantially better aggregate throughput and more predictable resource utilization.\n\n### Long Prompts Dominate Time to First Token\n\nLong-context coding assistants, RAG pipelines, and agent sessions can repeatedly send large system prompts or shared document prefixes. Processing those input tokens is the prefill stage, and it can dominate time to first token.\n\nvLLM supports chunked prefill and automatic prefix caching. Prefix caching allows later requests to reuse KV cache blocks when their initial token sequence matches an already processed prefix.\n\nThis is particularly useful when requests share:\n\n- A long system prompt\n- The same tool definitions\n- A stable repository summary\n- Repeated few-shot examples\n- A common RAG document prefix\n- A shared conversation history\n\nPrefix caching does not make output generation faster. It reduces repeated prompt computation, so its benefit depends on whether requests actually contain identical reusable prefixes.\n\n### You Need More Than One GPU\n\nA model that does not fit on one GPU is a strong reason to consider vLLM. It supports tensor parallelism across GPUs and pipeline parallelism across multiple nodes or devices.\n\nThis does not make multi-GPU inference effortless. GPU interconnect bandwidth, PCIe topology, model architecture, container shared memory, and communication overhead still affect performance.\n\nNevertheless, vLLM provides a deliberate path for distributed inference. Ollama is usually a better match for a single desktop or workstation where the chosen model already fits comfortably.\n\n### You Need Production-Level Observability\n\nOllama API responses expose useful timing fields such as model load duration, prompt evaluation duration, generated token count, and generation duration. These values are enough for local benchmarking and application-level logging.\n\nvLLM exposes Prometheus-compatible metrics through its `/metrics`\n\nendpoint. That makes it easier to track request volume, queueing, time to first token, inter-token latency, cache usage, preemptions, throughput, and request outcomes over time.\n\nOnce users depend on the service, observability stops being optional. Without queue, cache, and latency metrics, it is difficult to distinguish an undersized GPU from an oversized context limit, poor scheduling, cold model loading, or simply too many simultaneous requests.\n\n## Where vLLM Actually Wins\n\nvLLM’s most important advantage is not that it can produce one response faster than Ollama on every machine. The meaningful advantage is that it gives the operator more mechanisms for using expensive accelerator memory and compute efficiently across many requests.\n\n### Continuous Batching\n\nTraditional static batching works best when requests have similar input and output lengths. Interactive LLM traffic rarely behaves that way: one user asks for a short classification, another submits a 20K-token prompt, and a third generates several thousand tokens of code.\n\nContinuous batching changes the active batch as requests progress. Completed sequences leave, new sequences enter, and the engine attempts to avoid wasting batch capacity on requests that have already finished.\n\nThis improves throughput when traffic is concurrent and uneven. It provides little benefit when a single user sends one request at a time.\n\n### Paged KV Cache Management\n\nDuring generation, the server stores attention keys and values for previously processed tokens. This KV cache can consume a large amount of GPU memory, especially with long contexts and multiple active sequences.\n\nvLLM manages this cache in blocks instead of requiring each sequence to reserve one large contiguous allocation. The approach reduces memory fragmentation and allows available cache capacity to be used more flexibly.\n\nThe practical value is higher concurrency within the same memory budget. It does not remove the underlying cost of long context, but it reduces avoidable waste around that cost.\n\n### Prefix Caching\n\nMany production requests share a substantial beginning. Tool-enabled agents may send identical function schemas, support bots may use the same policy documents, and coding assistants may repeatedly include the same repository instructions.\n\nAutomatic prefix caching can reuse the computed cache for matching prefixes. It is especially useful when a stable, large prefix is followed by a relatively small request-specific suffix.\n\nIt is less useful when templates, timestamps, document ordering, or dynamically generated metadata change near the beginning of every prompt. Small differences in tokenization can prevent the prefix from matching.\n\n### Parallel and Distributed Inference\n\nvLLM supports several forms of parallelism, including tensor, pipeline, data, expert, and context parallelism. Not every deployment needs these modes, but their availability matters when a service grows beyond one GPU.\n\nFor a workstation with two suitable GPUs, tensor parallelism may allow a larger model to run across both devices. For a replicated service, data parallelism can create multiple engine replicas for additional throughput.\n\nThese features introduce operational complexity. They should be adopted because measurements demonstrate a capacity problem, not because distributed inference appears more sophisticated.\n\n### Broader Production Controls\n\nvLLM exposes controls for GPU memory utilization, maximum model length, maximum active sequences, quantization, cache data types, [speculative decoding](https://www.glukhov.org/llm-performance/optimization/speculative-decoding/), tool calling, structured output, model aliases, authentication keys, and distributed execution.\n\nThat flexibility makes the server easier to tune for a particular workload, but it also creates more opportunities for an invalid or inefficient configuration. Migrating to vLLM means taking responsibility for those decisions.\n\n## Where Ollama Still Wins\n\nA migration guide should not treat Ollama as an inferior preliminary tool. For many local deployments, it remains the better server.\n\n### Personal Workstations\n\nFor one developer using a chat interface, code assistant, or occasional local API, the operational advantages of vLLM may never compensate for its additional setup.\n\nOllama installs quickly, downloads models through a simple registry, and hides many model-specific details. It is well suited to experimentation and private desktop use.\n\n### GGUF Model Collections\n\nOllama has a natural workflow around GGUF models and Modelfiles. Existing users may have curated quantizations, adapters, templates, system prompts, and parameters that work reliably with their hardware.\n\nvLLM supports GGUF, but its strongest path is generally through supported Hugging Face model repositories and quantization formats such as AWQ, GPTQ, BitsAndBytes, FP8, or vendor-specific formats. Moving an existing GGUF deployment to vLLM without evaluating a more native checkpoint format can preserve the inconvenience of migration while missing some of the performance advantages.\n\n### Mixed CPU and GPU Offloading\n\nDesktop inference sometimes relies on partial GPU offloading because the entire model does not fit in VRAM. This can be practical for occasional use, particularly when latency is not critical.\n\nvLLM is generally most compelling when the model and required KV cache capacity can be served effectively by the available accelerator configuration. A workload that depends heavily on system RAM and CPU offloading may be better suited to Ollama or llama.cpp.\n\n### Rapid Model Switching\n\nOllama makes it easy to pull, run, stop, and switch among many local models. That is useful for evaluation, writing, coding, embeddings, vision, and ad hoc experimentation.\n\nA vLLM deployment is more commonly built around a deliberately selected model that remains loaded as a service. Multi-model deployment is possible, but it requires more explicit resource planning.\n\n### Minimal Administration\n\nOllama is intentionally opinionated. That can be a limitation under load, but it is an advantage when nobody wants to maintain an inference platform, and if the local server has one user, acceptable latency, and no meaningful queue, migration is likely to create work rather than remove it.\n\n## Do Not Migrate Based on Tokens per Second Alone\n\nSingle-request token generation speed is an incomplete benchmark. Two servers may produce similar decode throughput for one sequence while behaving very differently with eight concurrent clients.\n\nA useful evaluation should measure at least:\n\n- Time to first token\n- Inter-token latency\n- End-to-end request latency\n- Prompt processing throughput\n- Output token throughput\n- Requests completed per minute\n- Queue wait time\n- GPU memory consumption\n- GPU utilization\n- Failure and timeout rate\n\nRun the same model family, precision, context length, prompt set, output limit, and concurrency level on both servers. Otherwise, the test is more likely to compare model packaging and configuration than serving engines.\n\nThe most useful comparison is a small load test that represents your real traffic. For a shared coding assistant, that might include long system prompts, repeated prefixes, streaming responses, and two to eight simultaneous sessions.\n\n## Plan the Model Migration First\n\nOllama model names do not automatically map to equivalent vLLM model identifiers. An Ollama package may contain a particular GGUF quantization, prompt template, stop-token configuration, and default parameters.\n\nBefore changing the server, identify:\n\n- The original model family and version\n- Whether it is a base or instruction-tuned model\n- The current quantization and effective precision\n- The prompt or chat template\n- The configured context length\n- Stop tokens and generation defaults\n- Tool-calling or structured-output requirements\n- Any LoRA adapters or custom system prompts\n\nThen choose a vLLM-supported checkpoint that matches the intended behavior. Do not assume that an AWQ or FP8 checkpoint will behave identically to the GGUF build previously used in Ollama — the model migration is often more significant than the API migration.\n\n## Check VRAM Before Starting vLLM\n\nA model fitting into GPU memory does not mean that it can serve the required workload. VRAM must cover more than model weights.\n\nThe practical memory budget includes:\n\n```\nmodel weights\n+ KV cache\n+ CUDA graphs and runtime allocations\n+ temporary workspace\n+ multimodal processor caches, if used\n+ safety margin\n```\n\nLong contexts and concurrent sequences primarily expand the KV cache requirement. Increasing the maximum context length therefore reduces the number of simultaneous requests that can fit, even if most requests never use the full limit.\n\nStart with a realistic `--max-model-len`\n\nrather than the largest value advertised by the model, and avoid setting GPU memory utilization so aggressively that minor workload variation causes out-of-memory failures. A stable service with slightly less theoretical capacity is more useful than one that fails at its first traffic spike.\n\n## A Minimal vLLM Docker Compose Deployment\n\nThe following example starts an OpenAI-compatible vLLM server on port 8000:\n\n```\nservices:\n  vllm:\n    image: vllm/vllm-openai:latest\n    container_name: vllm\n    restart: unless-stopped\n    ports:\n      - \"8000:8000\"\n    ipc: host\n    gpus: all\n    volumes:\n      - ${HOME}/.cache/huggingface:/root/.cache/huggingface\n    environment:\n      HF_TOKEN: ${HF_TOKEN:-}\n    command:\n      - --model\n      - Qwen/Qwen3-8B\n      - --served-model-name\n      - local-model\n      - --max-model-len\n      - \"16384\"\n      - --gpu-memory-utilization\n      - \"0.90\"\n      - --api-key\n      - ${VLLM_API_KEY:-change-me}\n```\n\nCreate an environment file:\n\n```\ncat > .env <<'EOF'\nHF_TOKEN=\nVLLM_API_KEY=replace-with-a-long-random-value\nEOF\n```\n\nStart the server:\n\n```\ndocker compose up -d\n```\n\nCheck the logs:\n\n```\ndocker compose logs -f vllm\n```\n\nTest the models endpoint:\n\n```\ncurl http://localhost:8000/v1/models \\\n  -H \"Authorization: Bearer replace-with-a-long-random-value\"\n```\n\nSend a chat request:\n\n```\ncurl http://localhost:8000/v1/chat/completions \\\n  -H \"Authorization: Bearer replace-with-a-long-random-value\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"local-model\",\n    \"messages\": [\n      {\n        \"role\": \"user\",\n        \"content\": \"Explain continuous batching in two paragraphs.\"\n      }\n    ],\n    \"temperature\": 0.2,\n    \"max_tokens\": 300,\n    \"stream\": false\n  }'\n```\n\nFor a maintained deployment, pin the image to a tested vLLM release instead of leaving it on `latest`\n\n. Review release notes before upgrading because command-line options, model implementations, metrics, and engine behavior can evolve. This Compose file is intentionally minimal; for the fuller setup guide — OpenAI API compatibility, PagedAttention tuning, and a deeper vLLM-vs-Ollama comparison — see the [vLLM Quickstart](https://www.glukhov.org/llm-hosting/vllm/vllm-quickstart/).\n\n## OpenAI API Compatibility Is Not Complete Interchangeability\n\nBoth Ollama and vLLM provide OpenAI-compatible endpoints, which can make the application migration relatively small. In many clients, changing the base URL, API key, and model name is enough to establish a connection.\n\nFor example:\n\n``` python\nfrom openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"http://localhost:8000/v1\",\n    api_key=\"replace-with-a-long-random-value\",\n)\n\nresponse = client.chat.completions.create(\n    model=\"local-model\",\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What should I monitor on an LLM server?\",\n        }\n    ],\n    temperature=0.2,\n)\n\nprint(response.choices[0].message.content)\n```\n\nCompatibility should still be tested at the feature level. Examine:\n\n- Streaming event behavior\n- Supported request parameters\n- Chat template selection\n- Tool-call parsing\n- Reasoning output handling\n- JSON or schema-constrained output\n- Embeddings endpoints\n- Multimodal inputs\n- Token usage reporting\n- Error response formats\n- Model name discovery\n- Context-length enforcement\n\nA client that only sends ordinary chat completions will usually be easier to migrate than an agent framework that depends on a particular tool-call parser or nonstandard extension.\n\n## Chat Templates Are a Common Migration Failure\n\nInstruction-tuned models expect conversations to be serialized using a specific chat template. The template inserts role markers, separators, control tokens, and generation prompts in the format used during training.\n\nOllama packages much of this behavior inside its model definition. With vLLM, the template is normally obtained from the model tokenizer configuration, although an operator can provide one explicitly.\n\nA server may start successfully even when the selected template is wrong. The symptoms appear in model behavior:\n\n- The model repeats role labels\n- Responses contain special tokens\n- System instructions are ignored\n- Tool calls are malformed\n- The model continues the user message\n- Output quality is much worse than expected\n\nBefore blaming the inference engine, compare the fully rendered prompt used by each deployment.\n\n## Use a Staged Migration\n\nReplacing a working local server in one step creates unnecessary risk. Ollama and vLLM can run side by side on different ports while you validate the new deployment.\n\n### Stage 1: Reproduce One Model\n\nChoose the model responsible for most API traffic and match its instruction tuning, context requirement, generation parameters, and chat behavior as closely as possible. Do not begin by moving every experimental model.\n\n### Stage 2: Validate API Behavior\n\nRun existing integration tests against the vLLM endpoint, including streaming, cancellation, timeouts, tool calls, malformed requests, context overflow, and concurrent access. Record behavioral differences rather than hiding them behind client retries.\n\n### Stage 3: Establish a Baseline\n\nMeasure one-request performance first. This confirms that the model is loaded correctly and provides a reference for later tests.\n\nRecord prompt tokens per second, output tokens per second, time to first token, total latency, and GPU memory usage.\n\n### Stage 4: Add Realistic Concurrency\n\nTest the number of simultaneous requests expected in normal operation and during a plausible peak, using representative prompt and output lengths rather than identical synthetic requests. Watch queueing, cache use, preemptions, time to first token, and tail latency.\n\n### Stage 5: Move One Client\n\nRoute a noncritical application or a small percentage of traffic to vLLM. Keep Ollama available as a fallback until the new server has operated reliably under real use.\n\n### Stage 6: Tune From Measurements\n\nAdjust model length, memory utilization, maximum active sequences, prefix caching, parallelism, and quantization only after identifying a measured constraint. Changing several parameters at once makes performance regressions difficult to explain.\n\n## A Practical Migration Checklist\n\nBefore switching clients, verify the following:\n\n```\n[ ] The target model is supported by vLLM\n[ ] The selected checkpoint and quantization fit in VRAM\n[ ] Enough VRAM remains for the required KV cache\n[ ] The maximum context length reflects real usage\n[ ] The correct chat template is available\n[ ] Stop tokens and generation defaults are tested\n[ ] Streaming works with existing clients\n[ ] Tool calls and structured output are validated\n[ ] The public model alias remains stable\n[ ] Authentication is enabled\n[ ] The server is not exposed directly to the internet\n[ ] Prometheus metrics are collected\n[ ] GPU metrics are collected separately\n[ ] Load tests include realistic concurrency\n[ ] Timeouts and cancellations are handled\n[ ] A rollback path to Ollama exists\n```\n\nThis list is deliberately operational. Installing vLLM is usually easier than proving that it behaves correctly for an existing application.\n\n## Security and Network Exposure\n\nNeither a local Ollama endpoint nor a vLLM endpoint should be casually exposed to the public internet. An unauthenticated inference server can consume expensive GPU capacity, reveal model behavior, and become a route for denial-of-service attacks through very long prompts or outputs.\n\nvLLM can require an API key for its OpenAI-compatible endpoints, but an API key is not a complete security boundary. For shared or remote access, place the service behind a reverse proxy or API gateway that provides TLS, network restrictions, request-size limits, rate limits, access logging, and appropriate authentication — the same pattern covered in [Ollama behind a reverse proxy with Caddy or Nginx](https://www.glukhov.org/llm-hosting/ollama/ollama-behind-reverse-proxy/) applies just as well in front of vLLM.\n\nAlso consider model-specific risks. Multimodal URL loading, custom model code, remote files, and unrestricted tool execution can expand the attack surface beyond ordinary text generation.\n\n## When Not to Migrate\n\nStay with Ollama when:\n\n- One or two users access the server\n- Requests are mostly sequential\n- The model already delivers acceptable latency\n- Easy GGUF management is important\n- CPU or partial GPU offloading is required\n- Models are changed frequently\n- Nobody wants to operate additional infrastructure\n- There is no measured concurrency or throughput problem\n\nA move to vLLM should solve a concrete limitation. “Production” is not a magic threshold that invalidates Ollama, especially for an internal service with modest traffic.\n\nConversely, do not preserve Ollama merely because it was easier to install. If users regularly wait in a queue, repeated prefixes consume significant prefill time, or a larger model must be distributed across GPUs, the simpler server may have become the more expensive choice operationally.\n\n## Keep Ollama for Development and Add vLLM for Shared Serving\n\nThe most practical architecture is often not a complete replacement. Developers can keep [Ollama running in Docker Compose](https://www.glukhov.org/llm-hosting/ollama/ollama-in-docker-compose/) on their workstations for model exploration, GGUF testing, and private interactive use while a shared vLLM instance serves a stable model to applications and teams. That split also matters for [AI sovereignty](https://www.glukhov.org/llm-hosting/self-hosting/llm-selfhosting-and-ai-sovereignty/) — keeping both runtimes self-hosted means prompts, weights, and inference logs stay under your control regardless of which server handles a given request.\n\nThis separates two different workflows:\n\n``` php\nOllama:\nexperimentation -> model switching -> personal tools -> local chat\n\nvLLM:\nselected model -> shared endpoint -> concurrent traffic -> monitoring\n```\n\nThe arrangement also lowers migration risk. Models can be tested locally before a suitable checkpoint is promoted to the shared vLLM deployment.\n\n## Migration Decision Flow\n\nThe following diagram summarizes the key decision points:\n\nwith unstable latency?} B -->|No| C[Stay with Ollama] B -->|Yes| D{Long shared\n\nprefixes?} D -->|Yes| E[Strong vLLM signal] D -->|No| F{Need multi-GPU\n\nor observability?} F -->|Yes| E F -->|No| G{Measured concurrency\n\nproblem?} G -->|No| C G -->|Yes| E E --> H[Plan staged migration] H --> I[Validate side by side] I --> J[Switch clients gradually]\n\n## Conclusion\n\nOllama is difficult to beat as a local model runner. It removes enough packaging and configuration work that developers can concentrate on the model and application rather than the inference stack.\n\nvLLM becomes the stronger choice when the server itself is the problem to be engineered. Concurrent traffic, queueing, repeated long prefixes, multi-GPU models, capacity planning, and production observability are the migration signals that matter.\n\nDo not migrate because vLLM has a longer feature list. Migrate when measurements show that Ollama’s simpler operating model no longer matches the workload. Until that point, simplicity is not a technical weakness; it is an optimization.", "url": "https://wpnews.pro/news/ollama-to-vllm-when-to-migrate-your-local-llm-server", "canonical_source": "https://www.glukhov.org/llm-hosting/comparisons/ollama-to-vllm-migration/", "published_at": "2026-07-31 00:00:00+00:00", "updated_at": "2026-07-31 13:34:51.625905+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["Ollama", "vLLM", "Hugging Face", "Prometheus"], "alternates": {"html": "https://wpnews.pro/news/ollama-to-vllm-when-to-migrate-your-local-llm-server", "markdown": "https://wpnews.pro/news/ollama-to-vllm-when-to-migrate-your-local-llm-server.md", "text": "https://wpnews.pro/news/ollama-to-vllm-when-to-migrate-your-local-llm-server.txt", "jsonld": "https://wpnews.pro/news/ollama-to-vllm-when-to-migrate-your-local-llm-server.jsonld"}}