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Ollama is the best choice when you want an easy local model manager on each computer and a simple HTTP API, but it is not a documented way to combine several PCs’ RAM or VRAM into one model. Use a router or a tool such as Dollama to send separate requests to different machines. If one model is too large for a single host and must be partitioned across computers, vLLM with Ray is the documented option: tensor parallelism divides work among GPUs and pipeline parallelism extends that work across nodes. Choose llama.cpp when portability, CPU support, GGUF models, or lightweight multi-GPU inference matters more than production-scale batching.
The right answer depends on whether you need request distribution or model parallelism. Those architectures solve different problems and have very different setup, security, and networking requirements.
What does “running a model across multiple PCs” actually mean?
It can mean either sending independent prompts to different computers or splitting one model’s calculation among several computers. Only the second approach combines machines for a model that cannot fit on one host.
Routing independent requests
In a routed setup, every PC runs its own complete copy of a model. A gateway, queue, or client chooses which worker receives each request. If four computers each hold a model, four requests can be processed in parallel, subject to their local memory and compute capacity.
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This arrangement improves aggregate capacity and can keep a single machine from becoming a bottleneck. It does not make the workers’ memory additive: a model that requires more memory than any one worker has still will not load.
Partitioning one model
Model parallelism divides the model’s operations or layers across devices. A request may need to traverse several GPUs or hosts during one generation, so the machines exchange activations over a fast private network. This can make an oversized model runnable, but communication, synchronization, and operational complexity become part of every inference.
These terms are easy to confuse. “Four PCs serving one endpoint” does not prove that one model is split across four PCs; it may simply be a request router.
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Yes, Ollama can be exposed on a network and used as a worker on more than one computer, but its documented behavior does not pool the RAM or VRAM of those computers for one model.
What Ollama does on one host
Ollama binds to 127.0.0.1:11434 by default. The OLLAMA_HOST environment variable changes the bind address, allowing a service to listen on a network interface instead of only the local loopback interface.
Ollama can queue requests when available memory is insufficient; the documented default maximum queue is 512, and OLLAMA_MAX_QUEUE controls that limit. Queueing protects a host from accepting unlimited work, but it does not create additional memory.
For multiple GPUs in one host, Ollama places a model on one GPU when it fits there and spreads it across available GPUs when it does not. That is multi-GPU placement inside one computer, not documented cross-PC memory pooling.
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- Install Ollama on each worker. Put the model required by that worker on its local storage and verify that it can answer requests before introducing a router.
- Set the service bind address. Configure
OLLAMA_HOSTto listen on the worker’s private-LAN address, then restart the Ollama service so the setting takes effect. - Restrict inbound access. Allow the Ollama port only from the private network or from the designated gateway. Do not expose an unauthenticated inference service directly to the public internet.
- Test each worker separately. Send a request to each host and record which models are loaded, how much memory they consume, and how the host behaves when several requests arrive.
- Add a dispatcher. The dispatcher can use round-robin routing, queue depth, model availability, or health checks. Ollama itself provides the local API; the multi-host scheduling policy is a separate layer.
- Plan for a worker failure. Decide whether the dispatcher removes an unavailable host, retries a request, or returns an error. A request already running on a failed worker normally has to be restarted elsewhere.
This pattern is useful when requests are independent, models fit on individual machines, and simplicity matters more than squeezing one very large model into the cluster.
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Does Ollama combine RAM or VRAM from several PCs?
No documented Ollama feature turns the memory of separate networked computers into one shared pool for a single model. Multiple Ollama instances can share workload, while each inference process still relies on the memory of the host running it.
For example, three PCs with enough memory for a seven-billion-parameter model can serve three independent requests at once. They do not automatically become a single worker with three times the memory for a model that cannot fit on any one PC.
A single machine with multiple GPUs is a different case: Ollama can spread a model across GPUs available to that host when the model does not fit on one GPU. The boundary is the host, not the local network.
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Is Dollama better for a cluster of independent Ollama-style workers?
Dollama is aimed at a single local endpoint that dispatches separate requests across computers you own. Its architecture explicitly says that each model runs on one machine and that the cluster shares work between machines; it does not pool their memory to run a larger model.
When Dollama fits
- You want clients to call one endpoint rather than maintain a list of worker addresses.
- Each worker can load the model assigned to it independently.
- Your priority is distributing concurrent requests, not partitioning one model.
- You are prepared to operate health checks, model placement, and network access for the participating PCs.
What Dollama cannot change
Dollama’s endpoint abstraction does not alter the memory requirement of an individual model. If no single machine can load the model, request routing alone will not solve that constraint. You need a model-parallel runtime or a smaller or more heavily quantized model that fits one host.
When should you use vLLM with Ray?
Use vLLM with Ray when one model is too large for one node or when production serving throughput is the main objective. vLLM documents tensor parallelism within a node and pipeline parallelism across nodes, with Ray providing the multi-node execution backend.
Choose the parallelism mode
| Situation | Documented approach | What it means |
|---|---|---|
| The model fits on one GPU | No distributed inference | Keep execution local and avoid unnecessary communication. |
| The model fits on one node but needs several GPUs | Tensor parallelism | Divide the model’s computation among GPUs in that node. |
| The model is larger than one node | Tensor plus pipeline parallelism | Use multiple GPUs per node and distribute pipeline stages across nodes. |
The guidance to avoid distribution when one GPU is sufficient is important. A distributed deployment introduces synchronization and network traffic, so adding machines is not automatically faster or cheaper for a model that already fits locally.
Prepare identical nodes
vLLM’s multi-node guidance requires identical execution environments, model paths, and Python packages on every node. Differences in driver, runtime, package, or filesystem layout can prevent workers from starting consistently or produce hard-to-diagnose failures.
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Keep the model available at the same path on each node, or use a storage arrangement that presents an equivalent path everywhere. Treat the environment as a reproducible image rather than configuring nodes independently by hand.
Secure the Ray and vLLM network
The documented multi-node traffic is unencrypted. Keep VLLM_HOST_IP on a private network segment and prevent untrusted parties from reaching the cluster interfaces. Use firewall rules, network isolation, and administrative access controls appropriate to the sensitivity of prompts and generated data.
Do not confuse a private address with authentication or encryption. A private segment reduces exposure; it does not make an otherwise open service safe on every network.
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Where does llama.cpp fit?
llama.cpp is a portable C/C++ inference runtime suited to CPU or mixed hardware, GGUF models, and lightweight multi-GPU execution. It is a strong choice when broad hardware support and local control matter more than a production scheduler designed for maximum batching.
Use llama.cpp for portability
Its cross-platform and CPU-capable design makes it practical for heterogeneous PCs, including systems without a CUDA-oriented accelerator stack. GGUF quantization lets you choose a model representation that matches the memory and performance limits of the target machine.
Use its multi-GPU features within a host
The llama.cpp multi-GPU guidance recommends multiple GPUs when a model does not fit on one GPU or when higher throughput is desired. Split mode and interconnect speed affect the result, so two GPUs connected by a fast link can behave very differently from two GPUs connected through a slower path.
The runtime can expose an optional OpenAI-compatible server, but that server does not by itself provide cross-PC model partitioning. For a fleet of independent llama.cpp workers, add a separate request router; for one model spread across nodes, use a runtime that documents that topology.
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The most useful comparison is architectural rather than a single speed score. No comparable publisher-owned benchmark establishes a universal winner across different GPUs, networks, quantization levels, and workloads.
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| Tool or design | Independent-request routing | One-model partitioning across PCs | Formats and hardware | Throughput and batching | Operational burden | Security considerations |
|---|---|---|---|---|---|---|
| Ollama on several PCs | Yes, when paired with a dispatcher or client-side routing | Not documented for pooling memory across hosts | Local model manager; host GPU behavior can span multiple GPUs in one machine | Queues requests locally; default maximum queue documented as 512 | Low per host; separate routing and health management required | Bind address and firewall must be configured when exposing the API |
| Dollama | Yes; one endpoint can choose a machine for each request | No; each model runs on one machine | Depends on the underlying worker runtime | Designed to share work among machines; no universal benchmark stated | Moderate; central endpoint plus worker administration | Protect the endpoint and worker network |
| vLLM plus Ray | Possible through the serving layer | Yes; tensor parallelism within nodes and pipeline parallelism across nodes | Linux x86_64 and CUDA-oriented in the cited comparison | Optimized for high-throughput serving and batching | High; identical environments, model paths, packages, and cluster coordination required | Multi-node traffic is unencrypted; isolate the private cluster and protect VLLM_HOST_IP |
| llama.cpp | Yes, with a separate router in front of workers | Cross-PC partitioning not established in the cited guidance | Cross-platform, CPU-capable, mixed hardware, and GGUF-friendly | Lightweight runtime; throughput depends strongly on split mode and interconnect | Low to moderate; more manual assembly than a full serving platform | Secure any optional HTTP server and the network path to it |
Which architecture should you choose?
Start with the model’s fit on one machine, then decide whether requests are independent. The following cases map directly to the documented capabilities.
You need the simplest local API
Choose Ollama on one machine. Its local model management and HTTP interface reduce setup work, and its queue handles bursts within the host’s memory limits. If demand grows, add workers and a dispatcher rather than assuming their memory will combine.
You have several modest PCs and many independent users
Run a complete model on each suitable PC and route requests across them. Ollama provides the worker API; Dollama is designed to present one endpoint for this type of distribution. Keep model versions and prompts consistent if users should receive comparable results.
The model cannot fit on any single PC
Use vLLM with Ray and plan a true distributed-inference deployment. Select tensor parallelism for several GPUs in one node and combine it with pipeline parallelism when the model exceeds one node. Verify that the network and node environments meet the documented requirements before moving production traffic.
You have mixed operating systems or CPU-heavy machines
Evaluate llama.cpp first. Its cross-platform, CPU-capable runtime and GGUF support are better aligned with heterogeneous hardware than a Linux/CUDA-focused serving stack. Use multiple GPUs inside one host when needed, and add a router only if requests are independent.
One workstation already fits the model
Keep it on that workstation unless testing proves a distributed design provides a benefit you need. vLLM’s own guidance favors avoiding distributed inference when a single GPU is enough because network communication and coordination add cost and failure points.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you measure before adding more PCs?
Measure the constraint you are actually trying to remove. Adding workers helps a queue of independent requests; it does not fix a model that cannot fit on one worker.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Model fit: Confirm that the selected model format and quantization load within one worker’s available memory, or document the exact GPUs that will participate in a split.
- Concurrency: Count simultaneous requests and observe queue growth. A larger fleet helps only when the dispatcher can place work on workers with free capacity.
- Network path: For model parallelism, measure latency and bandwidth between nodes because activations must cross the network during inference. For request routing, network overhead is usually paid once per request instead.
- Consistency: Keep model files, runtime versions, drivers, and configuration aligned where a serving framework requires identical environments.
- Failure behavior: Test a worker shutdown, a lost network link, and a model-load failure. Decide whether requests retry, fail fast, or wait in a queue.
- Security: Inventory every listening address and port, then restrict access to the private interfaces and administrators who need them.
What are the common mistakes?
Calling routing “pooled memory”
A single endpoint can hide which worker handled a request, but it cannot change where the model’s weights reside. Check the architecture statement for the tool: if each model runs on one machine, it is request distribution, not memory pooling.
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Distributing a model that already fits
Extra nodes add synchronization and network traffic. Keep a model local when one GPU or one node can hold it unless your measured workload requires higher aggregate throughput.
Exposing a development server publicly
Changing a bind address makes a service reachable; it does not automatically add authentication, encryption, or authorization. Use a private segment and firewall policy, especially for vLLM and Ray, whose documented multi-node traffic is unencrypted.
Assuming heterogeneous nodes are interchangeable
vLLM’s multi-node guidance calls for identical execution environments, model paths, and Python packages. A mixed fleet may be appropriate for independent workers, but it complicates true model parallelism.
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Expecting a benchmark to transfer between setups
Generation speed depends on GPU model, memory, quantization, batch size, prompt length, network, and software versions. The canonical comparisons provide qualitative guidance and configuration defaults, not a cross-tool benchmark that applies to every cluster.
What is the practical verdict?
Use Ollama when ease of installation, local model management, and a straightforward API are the priority. Use Ollama workers or Dollama when you need to distribute separate requests across PCs. Use vLLM with Ray when the model itself must span nodes or when a controlled Linux/CUDA serving environment needs high-throughput batching. Use llama.cpp for portable, CPU-capable, GGUF-based inference and lightweight multi-GPU work.
The decisive question is not “Which tool uses the most computers?” It is “Am I routing independent requests, or partitioning one model?” Choose the simplest architecture that satisfies that requirement, and keep distributed traffic on a controlled private network.
Frequently Asked Questions
Can I add the RAM of two ordinary PCs to make one larger Ollama model?
No. Separate Ollama processes keep their model memory on their own hosts. To run a model that exceeds every individual machine’s capacity, use a runtime with documented multi-node model parallelism or select a model and quantization that fit one host.
Does sending requests to more Ollama workers always make responses faster?
It can increase total concurrent capacity, but an individual generation still runs on the worker that receives it. Router overhead, queue policy, model loading, and the slowest worker can limit the benefit.
What operating environment is most suitable for vLLM and Ray?
The cited comparison describes vLLM as Linux x86_64 and CUDA-oriented. Multi-node deployments also require matching execution environments, model paths, and Python packages across nodes.
How many computers can a Dollama cluster contain?
The cited architecture description does not state a universal machine limit. Practical capacity depends on the endpoint, network, worker resources, and the routing and health policies you configure.
What happens to a request when a distributed worker goes offline?
The result depends on the serving or routing layer. A request already executing on a failed host may need to be retried, while a dispatcher can remove unhealthy workers from new assignments if it has health checks.
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