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For a small, quantized 7B or 8B Ollama model, budget about $5–$15 per month for a CPU-only VPS with 8–16GB of RAM. That is the economical starting point, but it usually means several seconds per response and is best for development, personal use, or low traffic.
A GPU VPS is much faster but is normally billed by the hour. Quoted rates for 24–48GB GPUs range from $0.26 to $0.65 per hour, equivalent to roughly $190–$470 per month if left running continuously for 730 hours. Most occasional users pay far less by starting the GPU only when needed. The exact price depends primarily on model size, quantization, RAM or VRAM, traffic, and whether the instance runs 24/7.
What determines the cost of an Ollama VPS?
Ollama runs model weights in memory. As a result, available RAM is usually the first capacity constraint; additional CPU cores improve responsiveness but cannot compensate for insufficient memory. Quantization reduces the size of the weights, yet the model still needs overhead for the runtime, context window, and concurrent requests.
The following figures are practical planning thresholds from an Ollama model-library entry. They are not guarantees for every model or quantization, so leave headroom rather than choosing a plan that exactly matches the number.
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| Model size | Minimum RAM guidance | Typical VPS implication |
|---|---|---|
| 7B | At least 8GB | An 8GB plan is the practical floor; 1–2GB plans generally run out of memory. |
| 13B | At least 16GB | Choose 16GB or more, with extra capacity for context and other services. |
| 70B | At least 64GB on CPU | Usually requires a high-memory server or a GPU with substantial VRAM. |
A separate estimate puts a 7B Q4 model’s weights at about 4GB and a 70B Q4 model at about 40GB. Weight size is not the same as total memory use: the Ollama process, operating system, key-value cache, context, and parallel requests can push actual consumption higher.
CPU-only VPS pricing
The economical 7B/8B setup
Aggregator guides commonly quote entry VPS plans at $4–$8 per month, but the smallest plans are generally unsuitable for a 7B model. Their practical recommendation is 8GB of RAM at roughly $7–$15 per month. This is an aggregator-level estimate; no current official provider tariff was published.
At this tier, expect a flat monthly bill and no GPU. A modern virtual CPU can run a quantized 7B model for development, automation, and a small number of users. One guide reports approximately 3–10 seconds per response on a modern VPS CPU. Another unverified benchmark reports 12–18 tokens per second for Llama 3.1 8B on a Hetzner CPU. Because the benchmark method was not documented, treat it as an indicative range rather than a service-level expectation.
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Illustrative 8–32GB plan quotes
The following prices are listed by comparison guides and conflict with one another. They are useful for budgeting only; check the provider’s checkout page, region, billing period, VAT treatment, and bandwidth limits before ordering.
| Provider or plan description | RAM and CPU description | Quoted price | How to interpret it |
|---|---|---|---|
| Hetzner general-purpose VPS | 4 vCPU, 8GB | $7.49/month in one guide; about €8.30/month in another | Conflicting aggregator figures; not a verified current price. |
| Hetzner general-purpose VPS | 8 vCPU, 16GB | $14.49/month in one guide | Suitable on paper for a 13B model, subject to runtime overhead. |
| Contabo VPS | 8GB | $5–$8/month | Guides disagree; confirm the exact plan and renewal price. |
| Contabo VPS | 16GB | About $9/month | Aggregator quote only. |
| Contabo VPS | 32GB | About $15/month | May provide useful headroom for larger 13B workloads. |
| Vultr cloud VPS | 16GB | From $24/month | Higher quoted price for a general cloud plan. |
| DigitalOcean memory-optimized VPS | 16GB | $84/month | Shows how much a managed, higher-tier memory plan can cost. |
A June 2026 price-adjustment notice lists Hetzner CX23 (2 vCPU, 4GB) at €5.49, CPX42 (8 vCPU, 16GB) at €69.49, and CCX13 (dedicated vCPU) at €42.99, excluding VAT. The notice says the new prices apply to new orders and rescaling from 15 June 2026. These amounts conflict sharply with the older guide figures, and no current official pricing page was published to confirm them, so do not use either set as a guaranteed quote.
GPU VPS costs and hourly billing
GPU capacity is usually rented separately from a flat-rate CPU VPS. Providers and GPU marketplaces commonly charge by the hour, sometimes with a monthly spending cap. The bill is therefore determined by runtime, not only by the size of the machine.
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| GPU example | Quoted hourly rate | Approximate 730-hour month | Qualification |
|---|---|---|---|
| RTX 4090 | About $0.39/hour | About $285/month | Arithmetic using 730 hours; marketplace quote. |
| RunPod A6000, 48GB | $0.44/hour secure; $0.26/hour community | About $321 or $190/month | Arithmetic from the two quoted rates; availability and service type differ. |
| Vultr A40, 48GB | About $0.65/hour | About $475/month | Arithmetic from the quoted hourly rate. |
| Vultr fixed GPU plan | Not hourly in the cited quote | $90/month | Quoted for Llama 3.1 8B; verify what GPU, region, limits, and availability are included. |
The monthly equivalents are calculations, not published monthly prices. A $0.39 hourly instance costs about $9.36 for a full 24-hour day and about $285 for 730 hours. Running it for two hours on 20 weekdays would be about 40 hours, or roughly $16 at that rate, before storage or other charges. This is why an on-demand GPU can be economical for occasional batch jobs but expensive when kept online for a public service.
Do you need a GPU VPS?
CPU is adequate when
- You are developing prompts, testing integrations, or serving one person.
- A response taking several seconds is acceptable.
- You are using a quantized 7B or 8B model and can allocate at least 8GB of RAM.
- Your workload is intermittent and a predictable flat monthly bill matters more than latency.
A GPU is justified when
- Users expect interactive, low-latency responses or several requests must run at once.
- You need a 13B, 70B, or other model whose CPU memory requirement would make a high-RAM VPS uneconomical.
- You can stop the instance between jobs or use a provider’s hourly cap.
- Your application benefits from sustained token throughput rather than occasional testing.
A GPU does not remove memory planning. A 70B model is estimated to need roughly 40GB of VRAM at Q4 and roughly 70GB at FP8. A 24GB card therefore cannot be treated as a general 70B solution simply because it is a GPU.
Context length changes the hardware requirement
Ollama’s documented default context allocation varies with available VRAM: 4,000 tokens below 24GiB, 32,000 tokens at 24–48GiB, and 256,000 tokens at 48GiB or more. Ollama recommends at least 64,000 tokens for agent-style tasks. Larger contexts consume additional memory, so a plan that fits the model weights may still fail when a long conversation, retrieved documents, or multiple requests are loaded.
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For a small 7B deployment, an 8GB VPS may start successfully but leave little room for a long context or other services. Moving to 16GB can cost more than the model’s weight size alone would suggest, yet it can prevent memory pressure and swapping. On GPU instances, select VRAM with context headroom instead of matching the quantized file size exactly.
How to calculate your own monthly budget
- Identify the largest model you will actually run. Use 8GB RAM as the planning floor for 7B, 16GB for 13B, and about 64GB for 70B on CPU. Add capacity for context and concurrent requests.
- Choose the billing model. A CPU VPS is normally a fixed monthly charge. A GPU is commonly hourly, with a monthly cap or a separate persistent-storage charge.
- Estimate runtime. Multiply the hourly GPU rate by expected hours, not by 730, unless the service must be online continuously. For example, 100 hours at $0.44/hour is $44.
- Add non-compute charges. Check attached storage, snapshots, public IPv4 addresses, outbound bandwidth, taxes, and minimum allocation periods. The quoted figures above do not establish those additional fees.
- Reserve a safety margin. Prices, model files, context settings, and provider catalogs change. A plan with 20–30% unused memory is less likely to require an emergency resize, although the exact margin depends on your workload.
Cost-control practices that avoid surprise bills
- Stop or destroy an hourly GPU when no requests are pending; confirm whether stopped instances continue charging for compute or storage.
- Use a monthly spending cap or billing alert where the provider offers one.
- Keep model data and persistent storage separate from disposable compute so you can scale CPU or GPU capacity without repeatedly downloading models.
- Set a maximum context length appropriate to the application. An unnecessarily large context increases memory use and can force a more expensive GPU tier.
- Measure concurrent requests, response latency, and memory consumption before upgrading. A single-user CPU workload may not justify a GPU.
- Check regional availability and VAT. The quoted Hetzner adjustment, for example, explicitly excludes VAT.
Common failure modes
“Model requires more system memory”
Ollama can refuse to load a model when its estimated requirement exceeds available memory. One reported error required 164.8GiB while only 13.4GiB was available. The remedy is to use a smaller or more heavily quantized model, reduce context and concurrency, add RAM, or move to a suitable GPU; restarting alone does not create capacity.
GPU is detected but Ollama uses the CPU
Ollama’s Nvidia support requires compatible hardware and drivers. Its GPU documentation specifies compute capability 5.0 or newer and driver version 550 or newer for Nvidia cards. Verify the driver inside the VPS, confirm that the rented image exposes the GPU, and then check utilization while a model is generating. A nominal GPU hourly rate provides no benefit if the device is unavailable to the runtime.
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Responses are slower than expected
CPU inference can take 3–10 seconds per response according to one guide, while an unverified benchmark reported 12–18 tokens per second for Llama 3.1 8B on a Hetzner CPU. Actual results vary with quantization, CPU contention, context length, and simultaneous users. First reduce concurrency and context, then compare a dedicated-CPU or GPU option; do not infer performance from vCPU count alone.
Practical recommendations by workload
| Workload | Starting configuration | Expected cost approach |
|---|---|---|
| Personal experiments with a 7B/8B model | CPU VPS with 8GB RAM | Plan on roughly $7–$15/month from aggregator estimates. |
| Small internal tool using 7B/8B | 16GB CPU VPS or an on-demand 24GB GPU | Choose flat pricing for always-on, hourly GPU for bursts. |
| 13B model with moderate context | At least 16GB RAM, preferably with headroom | General VPS quotes range from about $9 to $84/month depending on provider tier. |
| 70B model | About 64GB CPU RAM, or roughly 40GB VRAM at Q4 | Expect a high-memory server or GPU rental; a small VPS is not sufficient. |
| Public, latency-sensitive service | GPU sized for the model, context, and concurrency | Hourly rates of $0.26–$0.65 can become roughly $190–$470/month when continuous. |
What to verify before ordering
All prices in this article are planning figures from comparison guides or quoted marketplace examples, and several conflict. Current official prices for the named VPS and GPU providers were not published. Before committing, verify the exact plan’s RAM or VRAM, CPU type, GPU model, driver image, storage and bandwidth charges, region, VAT, minimum billing period, availability, and whether stopped instances continue to incur fees. Also test the exact Ollama model and quantization you intend to deploy; model releases and provider catalogs change.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
