Private LLM hosting vs RunPod
We rent DGX Sparks, so weigh that against everything below. RunPod is a GPU cloud built as a marketplace: it aggregates capacity from RunPod's own data centers and, in at least some pools, from third-party hosts who supply GPUs into the platform, priced per second across a wide range of GPU types down to consumer-grade cards in some tiers. That structure is the real point of comparison against a Spark, not shared-vs-dedicated, since RunPod does sell dedicated pods, but which operator physically holds the hardware underneath you.
Side by side
| RunPod | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| Infrastructure model | A marketplace aggregating GPU capacity across multiple pools and, for at least some tiers, third-party hosts. Check RunPod's own docs for current tier definitions, since exact pools and guarantees have changed over time and GPUwerk did not fetch the current version for this page. | GPUwerk's own fleet, one operator, no marketplace or third-party hosts in the chain. |
| Who operates the physical hardware | Depends on the pool: RunPod's own or partner data centers for some tiers, third-party hosts for others (commonly labelled "community cloud" style pools). GPUwerk did not fetch RunPod's current documentation on exactly which tiers use which hosts. | GPUwerk directly. No sub-hosting layer to check. |
| Tenancy | RunPod offers dedicated pods, one instance allocated to you, on the tiers built for that; check RunPod's current docs for which tiers guarantee dedicated, single-tenant hardware versus shared or interruptible capacity. | Always dedicated: one physical Spark, allocated to you alone for the rental, no tier decision to make. |
| Where data is processed | Whichever data center or host region you select at deploy time, across RunPod's global footprint. | EU-Central (Prague), on one dedicated machine, no region selection needed because there's only the one machine. |
| GPU choice | Wide range, from consumer-grade cards in lower-cost pools up to top-tier data-center GPUs, letting you match spend to workload precisely. | One fixed configuration: a DGX Spark with 128 GB unified memory (or a two-node 256 GB cluster at $1.79/hour). No smaller or cheaper tier to pick for lighter workloads. |
| Speed and concurrency | Varies entirely by the GPU type selected; RunPod does not publish a single fixed tokens/second figure since it depends on hardware choice. | Measured on the fixed Spark hardware: gpt-oss-120b decodes at 33.5 tok/s single-stream and reaches 862.8 tok/s aggregate at 256 concurrent requests; Qwen3-Coder 30B-A3B AWQ decodes at 80.9 tok/s single-stream. Full methodology on our benchmarks page. |
| Pricing model | Per second, across many GPU types and price points; some lower-end GPU pools price well under a Spark's hourly rate. Check runpod.io/pricing for current rates, GPUwerk did not fetch a current figure to avoid quoting a stale one. | Per hour, billed per minute, flat regardless of load: $0.79/hour on-demand, $0.59/hour for a customer-requested stop, per pricing. |
| Who can see your data | Depends on the pool and host; a third-party host in a marketplace tier has physical access to the machine your workload runs on. GPUwerk did not fetch RunPod's current security documentation on data handling across tiers. | Nobody at GPUwerk. Our DPA states GPUwerk "hosts the machine but does not access, read, copy, index, or analyse the content of the controller's instance." |
| What you operate yourself | Root access to your pod; container and model serving stack are yours to manage either way. | Root SSH access to your own container: the model server, any RAG or agent layer, monitoring, and backups. GPUwerk keeps only a recovery copy of /workspace for hardware failures, refreshed roughly every six hours; it is not a backup service, so that responsibility is entirely yours. |
The marketplace question, stated plainly
RunPod's strength is choice: dozens of GPU types at a spread of price points, billed per second, so you can match spend to a workload precisely instead of paying a flat rate regardless of how small the job is. That's a genuine advantage over a Spark's single fixed configuration, and for workloads that fit comfortably on a cheaper card, RunPod can be meaningfully less expensive.
The trade-off is in who actually holds the hardware. RunPod is a marketplace, and at least some of its pools draw capacity from third-party hosts who supply GPUs into the platform rather than owning and operating everything itself end to end. GPUwerk has not fetched RunPod's current documentation describing exactly which tiers use which hosts, tier definitions and guarantees change, so check RunPod's own docs for the current answer before assuming a given deployment is single-operator custody. A GPUwerk Spark skips that question entirely: GPUwerk owns and operates the machine, full stop, there is no host layer to check.
If your workload is cost-sensitive and the exact operator of the box matters less than price and GPU flexibility, RunPod's model is built for that. If knowing exactly one named operator holds the hardware, with nothing subcontracted underneath, matters more than picking the cheapest GPU tier, a Spark gives you that answer without needing to read tier documentation to confirm it.
The worked cost example
Take a workload generating 500 million output tokens a month.
RunPod. Pricing depends entirely on the GPU type and pool selected, and rates for comparable throughput vary widely across RunPod's catalogue. GPUwerk did not fetch current per-second rates for this page. Check runpod.io/pricing for a GPU type with throughput comparable to a Spark and run the arithmetic against your own volume.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per our benchmarks page. That's 862.8 × 3,600 = 3,106,080 tokens per hour at full saturation. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
Because RunPod's per-second billing and wide GPU selection mean the true comparison depends on which card you'd actually pick, this is not a fixed-number comparison the way a single-model API is. A cheaper, lower-throughput GPU on RunPod might need many more hours to hit 500 million tokens; a faster one might beat the Spark figure outright. Run the specific GPU type you'd choose against $127.19 before deciding.
Migration path
Both a Spark and a RunPod pod give you root access to a container, so a model served through vLLM on either exposes an OpenAI-compatible chat completions shape with no vendor-specific rework needed. Model weights are portable in both directions since both run open models you choose. What doesn't move directly is anything tuned to a specific GPU's memory size or batch configuration, since RunPod's flexible GPU choice and a Spark's fixed 128 GB profile aren't identical targets.
When RunPod is the right choice
- Your workload fits comfortably on a lower-cost GPU tier and per-second billing beats a flat hourly rate for your usage pattern.
- You want to try multiple GPU types for the same workload and pick the cheapest one that performs well enough.
- You're comfortable checking RunPod's current tier documentation to confirm the isolation and hosting guarantees you need for a given pool.
When a dedicated Spark is the right choice
- You want one named operator holding the hardware, with no marketplace or third-party host layer to verify.
- 128 GB of unified memory covers your model comfortably and a flat hourly rate is simpler to plan around than per-GPU-type pricing.
- You want an EU-Central operator with no US parent, independent of the tenancy question.
FAQ
Is RunPod's community cloud the same as a dedicated machine?
Not necessarily. RunPod operates multiple tiers, including a community cloud pool of GPUs from third-party hosts and a secure cloud tier in RunPod's own data centers, and the exact isolation and hardware guarantees differ by tier and can change over time. Check RunPod's own current documentation for what each tier guarantees before assuming it matches a single-tenant, vendor-operated machine.
Who operates the physical hardware behind a RunPod instance?
It depends on the pool. RunPod's community cloud draws capacity from third-party hosts who supply GPUs to RunPod's marketplace, while other tiers run in RunPod's own or partner data centers. GPUwerk did not fetch RunPod's current documentation describing exactly which tiers use which hosts; check RunPod's own docs for the current breakdown.
Is a DGX Spark more private than RunPod?
For a secure cloud, dedicated GPU pod on RunPod, tenancy is comparable to a Spark: one instance allocated to you. The difference is the ownership chain: a Spark runs on GPUwerk's own hardware with one operator, while RunPod's marketplace model can mean the physical host is a third party RunPod contracts with, depending on the pool and tier you select. Check which tier you're actually using before assuming single-operator custody.
Is a Spark cheaper than RunPod?
RunPod bills per second across a wide range of GPU types and price points, some well below a Spark's $0.79/hour for lower-end GPUs. It depends entirely on which GPU type and tier you compare against a Spark's fixed 128 GB unified memory specs. See the worked example on this page and RunPod's own current pricing page for a direct comparison.
GPUwerk did not fetch RunPod's current pricing, tier definitions, or security documentation for this page; every RunPod-specific claim above is explicitly hedged since these details are known to change. Check runpod.io directly for current terms. Spark figures come from our benchmarks page and our pricing page.