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For a CPU-only Ollama VPS, plan memory above the model file size: a 16 GB RAM VPS with 4 vCPUs and NVMe storage is the practical starting point for an 8B model such as Llama 3.1. Small 1–3B models can work in roughly 8 GB, while 13–14B models are more comfortable with 24 GB. A 70B model generally needs 48–96 GB and is usually too slow for interactive CPU-only use.
These are planning estimates rather than an official Ollama hardware specification. The exact quantization, context length, number of simultaneous requests and operating-system overhead determine the result.
What sets Ollama’s VPS requirement?
Ollama itself uses comparatively little memory. The model you load determines most of the requirement. A model has to fit in RAM, and the process also needs memory for the operating system, runtime buffers and the key-value (KV) cache used to track conversation context.
The downloadable file is therefore a useful floor, not a safe VPS size. A machine with exactly the same RAM as the file may fail to load it or begin swapping as soon as a prompt is processed.
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Model parameters and quantization
Parameter count is the first sizing signal. Quantization stores weights with fewer bits, reducing both disk and RAM use at some cost in precision. A common rule of thumb for a 4-bit build is about 0.6 GB of memory per billion parameters, followed by additional headroom. That calculation is only an estimate; the model’s actual tag is authoritative for its file size.
The Ollama library listing captured for this guidance shows a default 4-bit Llama 3.1 8B tag at 4.9 GB, a 70B tag at 43 GB and a 405B tag at 243 GB. The same listing shows an 8B floating-point (fp16) build at about 31 GB. Choosing fp16 instead of a 4-bit tag can therefore move an 8B deployment from a small VPS into a much larger memory class.
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Longer context consumes additional memory while the model is running. A tag advertised with a 128K context limit does not mean a small VPS can use all 128K tokens comfortably; the cache grows with the amount of context actually retained. If your application sends large documents, keeps long chat histories or uses retrieval results, reserve more RAM than a short-prompt test suggests.
Operating-system and workload headroom
Leave roughly 2–4 GB beyond the model’s memory footprint as a practical margin for the OS and KV cache on a lightly used server. Add more when several requests can arrive together. Swap on NVMe can prevent an immediate crash, but it does not turn insufficient RAM into an interactive system: generation speed can collapse when model pages are repeatedly moved between disk and memory.
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RAM estimates by model size
The following ranges combine commonly cited third-party VPS estimates. They are for CPU-only inference and should be treated as planning bands, not guarantees. “Q4” means a typical 4-bit quantized tag; another quantization or context setting can change the result.
| Model size | Typical RAM cited | Practical interpretation |
|---|---|---|
| 1–3B | About 6–8 GB; 8 GB is a comfortable target | Suitable for lightweight personal assistants and short prompts |
| 7–8B | 8–16 GB; 12 GB is often cited for Llama 3.1 8B | 16 GB is the common VPS target, especially with longer context |
| 13–14B | 16–24 GB | 24 GB gives useful room for the OS and cache |
| 27–34B | 24–32 GB | Use the upper end when context or concurrency is significant |
| 70B | 48–96 GB | Estimates vary widely; CPU-only generation is very slow |
The wide 70B range reflects disagreement among third-party guides about how much cache and system headroom to count. Check the exact Ollama tag before ordering a server. For the 405B file listed at 243 GB, no dependable VPS RAM figure is established here; a machine would need substantially more memory than the file itself and would not be a normal single-tenant CPU VPS choice.
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Recommended VPS sizes for common workloads
| Workload | Suggested starting point | Why |
|---|---|---|
| Personal use with a 3B–7B model | 4 vCPU, 16 GB RAM, NVMe storage | Leaves room for the OS, cache and occasional longer prompts |
| Personal use with an 8B model | 4 vCPU, 16 GB RAM, NVMe storage | The frequently cited sweet spot for an 8B CPU deployment |
| 13–14B model | At least 4 vCPU and 24 GB RAM | Reduces swapping and accommodates a larger runtime footprint |
| Five concurrent users on a 7B model | 8 vCPU, 32 GB RAM | A March 2026 third-party estimate; quoted at roughly $35–60 per month |
| 70B model | 48–96 GB RAM, with many more CPU cores if available | Memory is the minimum challenge, while CPU throughput remains poor |
The five-user price is a third-party March 2026 estimate, not a universal VPS rate. Provider location, bandwidth, storage and billing terms can change it substantially. Concurrent requests also vary in length; five short prompts are easier than five long generations running at once.
CPU-only speed: what to expect
CPU throughput is usually the limiting factor after the model fits in memory. A 2026 third-party comparison estimates roughly 8–20 tokens per second for 7B models and about 0.8–3 tokens per second for 70B models. Actual results depend on the CPU generation, thread allocation, quantization, context length and whether other processes share the VPS.
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Those figures describe generation speed, not time to first token. Loading a model after it has been evicted can add noticeable delay, and a long prompt must be processed before new tokens are produced. A 70B model may technically run while still feeling unusable for an interactive chat on CPU alone.
How vCPUs affect users
More vCPUs help requests run in parallel and can improve throughput for one request, but they do not compensate for missing RAM. For a single personal 7B session, four vCPUs is a reasonable baseline. For several users, eight vCPUs and 32 GB RAM is a more credible starting point; monitor load and latency rather than assuming a linear speed increase.
Disk capacity and storage planning
Use the model file size as the first disk calculation, then add the operating system, Ollama files, logs, updates and any additional tags. The captured library values are 4.9 GB for the default Llama 3.1 8B tag, 43 GB for 70B and 243 GB for 405B. Keeping more than one quantization or model multiplies that requirement.
NVMe storage is preferred because model downloads, loading and swap activity are faster than on slower disks. Do not fill the volume to its limit: leave free space for a replacement tag and temporary download data. Disk capacity does not replace RAM; a 100 GB disk does not make a 70 GB model safe to run in 32 GB of memory.
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Check a VPS before downloading a model
Run these checks after provisioning the server and before pulling a large tag:
- Confirm memory: run
free -h. Compare available memory, not just total memory, with your planned runtime footprint. - Count processors: run
nproc. Record the vCPU count for deciding whether concurrent requests are realistic. - Check the volume: run
df -h /. Ensure there is room for the model, the OS and at least one additional download if you may test another tag. - Inspect service health: after installation, run
systemctl status ollamawhen your distribution uses a system service. A running service does not prove that a large model will fit.
Install, pull and verify a model
The exact installation method depends on the Linux image and package supplied by your VPS provider. Once Ollama is installed, use a small, observable sequence:
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- Start the service if it is not already running:
ollama serve. - Download the intended quantized tag, for example:
ollama pull llama3.1:8b. - Start an interactive test:
ollama run llama3.1:8b. - While it generates, watch memory with
free -hand CPU load withtoporhtop. - After the test, list loaded models with
ollama ps. Confirm that the expected tag, rather than a larger or different variant, is resident.
Test with the longest prompt and the largest expected output, not only a one-line question. A model that loads successfully with an empty context can still exhaust memory when your application sends long histories.
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Reduce the model or choose a smaller quantization
Move from a 13B or 70B tag to a 7B–8B tag when latency matters more than model capacity. Within the same family, a 4-bit tag generally needs far less memory than fp16. Record the exact tag in your deployment notes so a future pull does not silently change the sizing assumption.
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Limit context deliberately
Set a context limit appropriate to the application instead of accepting an unnecessarily large maximum. Shorter histories reduce KV-cache growth and make memory use more predictable. If you need long documents, process them in chunks or summarize earlier turns before sending a new request.
Control concurrency
Queue requests or cap simultaneous generations when memory is close to full. Running several copies of a model, or allowing each request to retain a large context, can consume the remaining headroom even when one-user tests look safe.
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Upgrade RAM before adding swap
If the kernel reports out-of-memory events or the VPS spends most of its time in disk wait, add RAM first. Swap is a safety valve for brief spikes, not a performance feature for sustained inference.
Common failure symptoms and fixes
| Symptom | Likely cause | Action |
|---|---|---|
| Model fails to load immediately | Available RAM is below the model and runtime footprint | Use a smaller or more heavily quantized tag, or move to a larger RAM tier |
| It loads, then the server becomes unresponsive | Context growth or concurrent requests consumed the remaining headroom | Shorten context, reduce concurrency and inspect free -h |
| Generation is extremely slow with high disk activity | Memory pressure is forcing swap | Stop extra workloads and add RAM; do not rely on swap for normal operation |
| Responses are acceptable for one user but queue for others | Insufficient vCPU capacity or a single model instance is saturated | Increase vCPUs, cap request length or move to a smaller model |
| Disk fills during an upgrade | Old and new model tags coexist during download | Remove unused tags after verifying the replacement and retain free capacity |
How to choose between a larger model and a larger VPS
Start with the quality and latency requirement, then size the infrastructure. A 7B–8B model on 16 GB RAM is usually a better interactive experience than forcing a 70B model into a barely adequate machine. Choose 13B–34B when the task benefits materially from additional capacity and you can accept higher latency. Choose 70B only when its quality is essential and your users can tolerate CPU-only speeds, or when you have an appropriate accelerator and validated memory budget.
Recheck the live model tag before deployment. Ollama tags can change, and the figures above were captured on October 14, 2026 from a library page that may not reflect later revisions. Treat the file size, quantization and context advertised for the exact tag as the final inputs to your calculation.
Bottom line
For most self-hosted Ollama users, order a 4-vCPU, 16 GB RAM NVMe VPS for a 7B–8B 4-bit model. Move to 24 GB for 13B–14B, 32 GB for heavier 27B–34B use or several 7B users, and at least 48 GB for 70B—with the expectation that CPU-only generation will be slow. Always leave memory above the model file size, account for context and concurrency, and verify the exact tag before committing to a plan.
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