What should I use for a 70B model: GPU cloud or on-prem?

For a 70B model, the right choice depends mostly on how often you’ll run it, your latency/throughput needs, and how much operational hassle you want to own.

Short answer

• Use GPU cloud if you want to start fast, scale elastically, and avoid large upfront costs.
• Use on-prem if you have steady high usage, strong data/control requirements, and can justify the capex + ops burden.

When cloud is the better fit

Choose cloud GPUs if:

• You’re experimenting or still validating the use case
• Workload is bursty or unpredictable
• You need to spin up quickly
• You want access to top-end GPUs without buying hardware
• You don’t want to manage drivers, networking, cooling, failures, etc.

Cloud advantages

• Low upfront cost
• Easy scaling
• Faster deployment
• Good for short-term projects and A/B testing

Cloud downsides

• Can get expensive very fast for always-on inference
• Egress/network costs can matter
• Performance can vary depending on instance availability
• Long-running workloads may end up costing more than owning hardware

When on-prem is the better fit

Choose on-prem if:

• The model will run continuously or near-continuously
• You have predictable demand
• You need data locality, security, or compliance
• You’re okay with hardware procurement and maintenance
• You want lower long-term cost at scale

On-prem advantages

• Better unit economics for 24/7 usage
• More control over the stack
• Stronger data governance
• No cloud vendor lock-in

On-prem downsides

• High upfront capex
• Longer time to deploy
• You own uptime, cooling, power, replacement, and upgrades
• Harder to keep up with newer GPUs over time

A practical rule of thumb

If you expect:

• < 30–40% utilization of a dedicated 70B-serving setup: cloud is often better
• High, steady utilization (especially 24/7): on-prem often wins
• Uncertain demand: start in cloud, then migrate on-prem once usage is proven

Important 70B-specific note

A 70B model usually isn’t “one GPU and done” unless you use:

• aggressive quantization, or
• very high-memory GPUs, or
• tensor/pipeline parallelism across multiple GPUs

So whichever route you choose, budget for:

• multiple GPUs or large-memory GPUs
• fast interconnects if serving at good latency
• enough VRAM for weights + KV cache + concurrency

My recommendation

• Start with cloud if you’re still learning, prototyping, or have variable traffic.
• Move on-prem if the model becomes production-critical with steady demand and the cost model justifies it.

If you want, I can help you decide with a simple cost break-even model based on:

• expected requests/day
• tokens per request
• latency target
• whether it’s training, fine-tuning, or inference