How do I deploy Llama 3.1 405B for production inference on B200 GPUs, and what's the minimum GPU count?

Teams deploying Llama 3.1 405B on B200 want a minimum GPU count and a production setup that holds up under load. Unlike a mixture-of-experts model, 405B is dense, so every one of its parameters participates in every token, which makes its memory footprint large and predictable. The minimum B200 count for Llama 3.1 405B is its weight footprint at your serving precision plus the KV cache for your concurrency, divided by B200 memory per GPU and rounded up for headroom, and because 405B is a dense model, all 405 billion parameters must be resident, so precision is the single biggest lever on the count. This guide gives you the sizing math and the steps to a production deployment.

Dense means the whole model loads, every token

The most important distinction for 405B is that it is a dense model, not MoE. A dense model uses all its weights for every token, with no expert routing that activates only a subset, so there is no gap between total and active parameters to exploit. The full 405 billion parameters must be resident in memory for serving.

That makes the footprint a direct calculation from precision. A 405-billion-parameter dense model requires roughly two bytes per parameter at FP16, on the order of 810GB for weights alone, which halves to roughly 405GB at FP8 and falls further at lower precision, so your serving precision sets the base memory demand before the KV cache is even added. The minimum GPU count follows from dividing that footprint, plus the KV cache your concurrency needs, by the memory a single B200 provides, then rounding up so the model is not serving at the ragged edge of memory. Because the weight footprint is fixed by the parameter count and your precision, this is a more predictable calculation than an MoE model, where routing complicates the memory picture.

What sets the minimum count for your deployment

Three inputs move the minimum B200 count for 405B. Fix each before you provision:

• Serving precision: FP16 needs roughly double the weight memory of FP8, which can double or halve the GPU count. This is the largest single factor, so choose it deliberately and validate quality if you quantize.
• KV cache size: Production concurrency and context length add cache on top of the 810GB-class weight footprint, and at high concurrency this can be a substantial share of total memory.
• Headroom: Sizing to the exact memory minimum leaves no room for the cache to grow or for stable throughput, so a production minimum is higher than a bare fit.

The takeaway is that a minimum count at FP8 for modest concurrency and a production count at FP16 for high traffic are very different numbers, and only the one matching your precision and load is real. Compute it from your own inputs, then confirm on hardware.

From footprint to a production deployment

Use the frame below to move from the memory math to a running production service.

Step	What to decide	Effect on the deployment
Pick precision	FP16, FP8, or lower	Sets weight footprint and base GPU count
Add KV cache	Concurrency and context budget	Raises total memory above weights
Divide and round up	Footprint over B200 memory, plus headroom	Gives the minimum production GPU count
Configure parallelism	Tensor and pipeline across the GPUs	Determines throughput and latency

The pattern is consistent: precision and concurrency set the memory, the memory sets the minimum GPU count, and the parallelism configuration turns that count into production throughput. A dense 405B model makes the first half of this predictable, but the second half, meeting latency at your target concurrency, still requires measurement.

Deploying 405B on B200 with GMI

Since the count depends on your precision and concurrency, the practical step is using our platform, which carries B200 in quantity so you can size and serve 405B in production. We are an AI-native GPU and inference cloud that publishes dedicated NVIDIA GPU list pricing and carries B200, with the reserved capacity sustained serving needs.

We currently list B200 at from $4.00 per GPU-hour under Limited Availability, so you can deploy Llama 3.1 405B at your chosen precision, measure the real footprint including KV cache, and confirm the minimum GPU count rather than provisioning from an assumed number. Verify the current rate and B200 availability on our GPU infrastructure (https://www.gmicloud.ai/en/gpus) and pricing (https://www.gmicloud.ai/en/pricing), since Blackwell pricing and stock move quickly, and confirm multi-GPU quantity for a 405B deployment. Benchmark at your production precision, concurrency, and context length, because those decide both the count and the throughput. For sustained production serving, Our reserved capacity plans lower the effective per-GPU rate below on-demand, and our Prime Inference (https://www.gmicloud.ai/en/models/prime-inference) provides reserved GPU capacity with single-tenant isolation and warm, weights-preloaded serving, which keeps 405B resident and avoids cold-start reloads of a large model. Start sizing in our console (https://console.gmicloud.ai).

Size from the parameters, deploy for the load

If you provision B200s for Llama 3.1 405B from a number you read rather than sized, you risk under-provisioning at FP16 or overpaying when FP8 would have served your quality bar. Do it the reliable way: because 405B is dense, take roughly two bytes per parameter at your precision, about 810GB at FP16, add the KV cache for your concurrency, divide by B200 memory per GPU, and round up for headroom, then confirm on hardware and tune parallelism for your latency target. The minimum count is predictable for a dense model once you fix the precision, so decide precision first, measure the rest, and provision for the production load rather than the bare fit.