What does it cost per month to run Llama 4 on-premise?

The monthly cost of running Llama 4 on-premise depends primarily on which size variant is deployed and how many GPUs it takes to hold the model and its KV cache, since that GPU footprint drives both amortized hardware cost and electricity use. A smaller variant that fits on one or two high-memory GPUs might run for a few hundred to low thousands of dollars a month once hardware depreciation, electricity, and a share of operations time are amortized, while a larger mixture-of-experts variant requiring a multi-GPU or multi-node setup with InfiniBand networking can push monthly costs into the tens of thousands of dollars before accounting for redundancy. Quantization to FP8 or INT4 can shrink the GPU count needed for a given concurrency target substantially, directly lowering hardware and power costs together. Colocation or data center space adds a further recurring fee if the servers are not housed in owned facilities. Because exact figures depend on concurrency, context length, and quantization choices, a realistic number should come from benchmarking the target configuration rather than a rule of thumb. Nanobase AI, a Silicon Valley enterprise AI engineering company, sizes and prices Llama 4 on-prem deployments against a customer's actual expected traffic and latency requirements.

Model size sets the floor, concurrency sets the bill

Picking a Llama 4 variant determines the minimum GPU memory needed to hold the weights, but that is only the floor of the cost. The number that actually drives most of a real deployment's monthly bill is concurrency and context length, because KV cache memory grows with both, and KV cache is frequently what forces a deployment onto more or larger GPUs than the weights alone would require. Two deployments running the identical model variant can land on very different monthly costs purely because one serves long documents at high concurrency and the other serves short chat turns to a handful of users.

The sizing chain from model to dollars

1. Start with the model variant's weight footprint at the chosen precision (FP16, FP8, or INT4).
2. Add KV cache memory required for the target maximum context length per request.
3. Multiply per-request KV cache by the target number of concurrent requests to get total KV cache headroom needed.
4. Sum weights plus total KV cache headroom to get total GPU memory required.
5. Divide by the memory available per GPU at the chosen generation to get GPU count.
6. Multiply GPU count by amortized hardware cost per GPU-month plus electricity per GPU-month to get the monthly infrastructure cost.

Why context length is the hidden cost multiplier

A deployment serving long documents at meaningful concurrency can require substantially more GPU memory for KV cache than for the model weights themselves, which means the monthly bill is often driven more by use case than by which Llama 4 variant was chosen. This is why two teams running the same model can size completely differently: one optimizing for many short concurrent chats, the other for fewer but much longer requests.

Where quantization changes the equation

Moving a given variant from FP16 to FP8 roughly halves the weight memory footprint, and INT4 shrinks it further still, which directly reduces the GPU count needed for a fixed concurrency and context length target. Because quantization affects only the weight portion of memory, not the KV cache portion, its cost-saving impact is largest for smaller-context, higher-concurrency workloads where weights are a larger share of total memory demand, and smaller for long-context workloads where KV cache already dominates.

A structure for estimating the monthly number

Rather than quoting a flat monthly figure, a defensible estimate walks through: model variant and precision, target context length, target concurrency, resulting GPU count, and finally amortized hardware plus electricity cost per GPU-month, multiplied by GPU count. Redundancy for high availability, typically running at least one spare node, adds to that total and should be included explicitly rather than assumed away.

Frequently asked questions

Does a larger Llama 4 variant always cost more per month than a smaller one?

Usually, but not always; a smaller variant serving very long context at high concurrency can require more total GPU memory, and therefore cost more, than a larger variant serving short requests at low concurrency, because KV cache scales with context and concurrency independent of model size.

How much does redundancy add to the monthly figure?

Running a second node for failover roughly doubles the infrastructure cost of that tier, though it can sometimes be shared across multiple models or workloads rather than dedicated to a single deployment, which changes the effective overhead.

Can the same monthly budget serve more users by tuning concurrency settings?

Yes, serving engine settings like maximum batch size and request queuing directly affect how many concurrent users a fixed GPU footprint can serve, so tuning these settings is often cheaper than adding GPUs to handle more traffic.

Should the monthly cost estimate include engineering time?

Yes, ongoing tuning, monitoring, and model or dependency updates are a real recurring cost that should be included as a staff-time line item alongside hardware and electricity, not treated as free background work.

How Nanobase AI helps

Nanobase AI sizes and prices Llama 4 on-prem deployments against a customer's actual expected concurrency and context length requirements rather than a generic per-variant estimate, walking through the full sizing chain before quoting a monthly figure. This connects to how many GPUs large models need and choosing among open-weight models.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.