Can I run Qwen3 and other large open-source models on a single B200, or do I need a GB200 rack for the biggest ones?

Teams adopting open-source models like Qwen3 want to know where the single-GPU line is: which sizes fit on one B200, and which force a jump to a GB200 rack. The answer is a threshold, not a yes or no, because open model families span a wide range of sizes and the fit depends on the model's footprint at your precision. Many open-source models, including smaller and mid-size Qwen3 variants, run on a single B200 when their weights plus KV cache fit its memory at your serving precision, while the largest variants, especially very large dense or MoE models, exceed one GPU and need a multi-GPU cluster or a GB200 rack. This guide gives you the size threshold and how precision moves it.

The single-GPU line is a memory threshold

Whether a model fits one B200 is a direct comparison: its weight footprint plus KV cache against the memory of a single B200. Open model families like Qwen3 ship in a range of sizes, so some variants sit well within one GPU and others do not, and the same family can span both sides of the line.

Precision moves the threshold as much as model size does. A model fits a single B200 when its weights at your precision plus the KV cache stay within the GPU's memory, so a variant that overflows at FP16 may fit at FP8, and precision choice can move a model across the single-GPU line without changing the model. As a rough guide, using two bytes per parameter at FP16, small and mid-size models fit one B200 comfortably, models in the tens of billions fit depending on precision and cache, and models past roughly a single GPU's memory at your chosen precision require sharding across more than one. The exact cutoff depends on the specific B200 memory and your KV cache, so calculate the footprint rather than guessing from the parameter count alone.

When you need more than one GPU

The largest open models cross the single-GPU line for two reasons, and both point to a multi-GPU setup or a rack.

Very large dense models simply have too many weights to hold on one GPU at usable precision. A model whose FP16 or even FP8 footprint exceeds a single B200's memory must be sharded across multiple GPUs, and once it spans several cards you are running a cluster whether or not it is a formal rack. Very large MoE models add the expert-pool problem: the full set of experts must be resident, so total memory demand grows well beyond the active parameter count, pushing the biggest MoE variants past one GPU even when each token only activates a fraction.

The rack question arrives when the model is large enough that a multi-B200 cluster would span nodes. At that point a GB200 NVL72 rack, with its pooled 72-GPU NVLink domain, keeps the model in one high-bandwidth space and avoids the inter-node communication penalty a networked cluster pays. So the progression is single B200 for models that fit, a B200 cluster for larger ones that still fit within a node or a few, and a GB200 rack for the biggest models that would otherwise cross node boundaries.

Matching open model size to the setup

Use the frame below to place your model on the single-GPU-to-rack spectrum.

Model size and type	Setup	Why
Small to mid-size, fits one GPU	Single B200	Weights plus cache within one card
Tens of billions, precision-dependent	Single B200 or small cluster	FP8 may fit; FP16 may need sharding
Very large dense, exceeds one GPU	B200 cluster	Weights must shard across cards
Very large MoE, big expert pool	B200 cluster or GB200 rack	Resident experts push past one GPU
Largest models spanning nodes	GB200 NVL72	Pooled domain avoids inter-node cost

The pattern is consistent: single-GPU feasibility is a footprint calculation, and the jump to a cluster or rack happens when the model, at your precision, exceeds what one B200 holds. Precision is the lever that decides borderline cases, so choose it before you conclude a model needs more hardware.

Running open models on GMI

Since the single-GPU question is a footprint calculation, the practical step is using our platform, which carries both single B200s and GB200 racks so you can match hardware to model size. We are an AI-native GPU and inference cloud that publishes dedicated NVIDIA GPU list pricing and lists both.

We currently list B200 at from $4.00 per GPU-hour under Limited Availability and GB200 NVL72 at from $8.00 per GPU-hour Available Now, so you can run smaller and mid-size open models like Qwen3 variants on a single B200 and move to a cluster or rack only for the largest models that exceed one GPU at your precision. Verify current rates and 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. Calculate your model's footprint at production precision and concurrency before provisioning, because that is what decides single-GPU versus multi-GPU. When the workload is sustained serving, our Prime Inference (https://www.gmicloud.ai/en/models/prime-inference) provides reserved GPU capacity with single-tenant isolation and warm serving on whichever setup your model size calls for. Start in our console (https://console.gmicloud.ai).

Calculate the footprint, then pick the hardware

If you assume an open model needs a rack because it is large, or that it fits one GPU because a smaller sibling does, you can over- or under-provision. Do the calculation: take the model's weights at your precision, add the KV cache for your concurrency, and compare against a single B200's memory. Smaller and mid-size open models like many Qwen3 variants fit one B200, the largest dense and MoE models need a cluster, and only the biggest that would span nodes require a GB200 rack. Precision decides the borderline cases, so calculate the footprint first and let it, not the parameter count alone, pick the hardware.