Private LLM hosting vs Groq
We rent DGX Sparks, so weigh that against everything below. Groq deserves a fair opening: it built custom LPU hardware specifically to minimize inference latency, and by most public accounts that architecture delivers real, sometimes dramatic, speed advantages over general-purpose GPU inference for supported open-weight models. If your product lives or dies on time-to-first-token, that's not a marketing claim to wave away. What Groq's API doesn't change is the underlying trust model: it's still a shared, multi-tenant API that Groq operates, billed per token, and a dedicated Spark is a machine only you touch. This page is about that trade, not about whether Groq is fast, it is.
Side by side
| Groq API | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| Hardware | Custom LPU (Language Processing Unit) chips, purpose-built for low-latency token generation, not general-purpose GPUs. | NVIDIA GB10 Grace Blackwell Superchip in a DGX Spark, general-purpose AI hardware, 128GB unified memory. |
| Tenancy | Shared multi-tenant API; your requests run on Groq's pooled LPU capacity alongside other customers' traffic. | Single-tenant: one physical Spark, one SSH key set, nobody else's workload runs on it. |
| Inference speed | GPUwerk did not benchmark Groq directly for this page. Groq's LPU architecture is widely reported to deliver very low per-token latency; check Groq's own published benchmarks for current figures on your model of interest. | Measured, not estimated: 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. |
| Model choice | A curated set of open-weight models Groq has ported to run on its LPU hardware, growing over time but narrower than the full open-weight ecosystem. | Any open-weight model you choose that fits in 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and others, with no porting dependency on a vendor. |
| Who can see your prompts | Groq, as operator of the API. GPUwerk did not fetch Groq's current data-usage and training policy for this page; check Groq's own privacy policy and API terms for what is retained and who can access it. | 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." |
| Pricing model | Per token, priced separately by model. Check Groq's own pricing page for current rates, GPUwerk did not fetch a current figure to avoid quoting a stale price. | Per hour, billed per minute: $0.79/hour on-demand, $0.59/hour for a customer-requested stop. One rate regardless of model or request volume, per pricing. |
| What you operate yourself | Nothing at the infrastructure layer; Groq manages capacity and model serving on its own chips. | 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. |
Where the honest difference actually is
If speed is the metric that matters most to you, this is a short comparison: Groq built hardware for exactly that, and a general-purpose GPU box, Spark included, is not going to out-race purpose-built silicon on single-stream latency. We don't have a fair head-to-head number to offer here, and we're not going to imply one by putting an unrelated figure next to a Groq claim.
What doesn't change no matter how fast the chip is: Groq's API is still shared infrastructure that a third party operates. Every request goes onto Groq's pooled capacity, processed by Groq's systems, subject to whatever retention and access policy Groq currently publishes. A dedicated Spark removes the pooling. It's slower per token in most cases, but it's a machine that runs your workload and nothing else's, with GPUwerk's stated commitment not to look at what's on it.
Put plainly: if the requirement is "fastest possible response," look at Groq's own numbers. If the requirement is "nobody else's traffic touches this hardware, and I know exactly which machine is running it," that's what a Spark is for.
The worked cost example
Take a workload generating 500 million output tokens a month.
Groq API. GPUwerk did not fetch current per-token pricing for a specific Groq-hosted model at the time of writing. Run your own volume and model choice through Groq's pricing page for a number you can trust.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per the concurrency benchmark on our benchmarks page. That's 862.8 × 3,600 = 3,106,080 tokens per hour if the node stays saturated at that concurrency throughout. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
That figure assumes constant saturation for the full 161 hours, and it says nothing about per-request latency, only aggregate throughput over a month. A workload where users are waiting on individual responses in real time cares about a different number than this one; that's where Groq's architecture is built to win regardless of what this cost comparison shows.
Migration path
Groq's API and a Spark serving a model through vLLM both typically expose an OpenAI-compatible chat completions shape, so client code that only calls that endpoint moves with a base URL and API key change. What doesn't move automatically is latency-sensitive product behavior tuned around Groq's response times, streaming UI patterns built assuming near-instant first tokens may need rework against a slower baseline.
When Groq is the right choice
- Your product's user experience depends on very low per-token latency, and that's worth more than data control or model choice.
- Groq's supported model list already covers what you need, so you're not blocked on model availability.
- You're comfortable with a shared multi-tenant API and don't have a hard requirement for single-tenant hardware.
When a dedicated Spark is the right choice
- Data residency or single-tenant hardware is a requirement, not a preference, and you can live with GPU-class rather than LPU-class latency.
- You want any open-weight model, not just the ones a vendor has ported to its chip.
- Your traffic is steady enough that hourly billing on a dedicated machine works out cheaper than per-token pricing at your volume.
FAQ
Is Groq faster than a DGX Spark?
For single-stream token generation, Groq's LPU architecture is built specifically to minimize latency and is widely reported to outperform typical GPU inference on that metric. GPUwerk has not run a head-to-head benchmark against Groq and will not claim a Spark matches or beats it on speed. Our own measured numbers are on the benchmarks page; compare them against Groq's published figures for your model of interest.
Does Groq's API see my prompts?
GPUwerk did not fetch Groq's current data-retention or training policy for this page. Check Groq's own privacy policy and API terms for what is logged, for how long, and whether staff or sub-processors can access prompt content.
Is a Spark more private than Groq's API?
On the question of who else can read a given prompt, yes: a Spark is one dedicated machine under your own SSH keys, and GPUwerk's terms state GPUwerk does not access, read, copy, index, or analyse instance content. Groq's API runs on shared infrastructure that Groq operates, whatever their stated retention and training policy is.
Why would I pick a slower Spark over Groq?
If raw latency is what your product needs, you probably shouldn't, Groq built hardware specifically for that. A Spark is the better fit when the deciding factor is dedicated single-tenant hardware, data residency, or wanting the actual model weights under your own control, not speed.
GPUwerk did not fetch Groq's current pricing, data-usage policy, or speed benchmarks for this page; every Groq-specific claim above is either general public knowledge about the product's architecture or explicitly hedged. Check groq.com directly for current terms and benchmarks. Spark figures come from our benchmarks page and our pricing page.