NVIDIA288 GB VRAM8,000 GB/s$8/hr

Qwen2 72B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen2 72B at FP8 (E4M3) needs 72.7 GB for its weights and 94.2 GB in total at 8K context with 8 concurrent users. That means it fits on a single B300 SXM 288GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
94.2 GB needed / 288 GB available
Decode speed
107.3
tok/s per stream
Aggregate
1287
tok/s @ 8 concurrent
Cost / 1M tokens
$1.73
1× B300 SXM 288GB
What changes as you add B300 SXM 288GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× B300288 GB94.2 GB107.312871.63 s$1.73✓
2× B300576 GB94.2 GB182.44377829.6 ms$1.02✓
4× B3001152 GB94.2 GB364.817510429.8 ms$0.5077✓
8× B3002304 GB94.2 GB729.670039229.9 ms$0.2538✓
Concurrency vs context length
On 1× B300 SXM 288GB at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K1601.34 GB✓ fits
8K802.68 GB✓ fits
33K2010.74 GB✓ fits
66K1021.47 GB✓ fits
131K542.95 GB✓ fits
Other GPUs that run Qwen2 72B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×3922$0.1416
A100 80GB SXM42×1094$0.6092
H100 SXM5 80GB2×1833$0.7577
AMD MI300X 192GB1×853.0$0.8142
L40S 48GB2×472.8$0.8226
AMD MI325X 288GB1×965.6$1.01
Frequently asked questions

Can Qwen2 72B run on B300 SXM 288GB?

Yes. Qwen2 72B at FP8 (E4M3) needs 94.2 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen2 72B on B300 SXM 288GB?

107.3 tokens/sec per stream and 1287 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.63 s, and inter-token latency is 9.3 ms.

What does Qwen2 72B on B300 SXM 288GB cost per million tokens?

$1.73 per 1M output tokens at 1× B300 SXM 288GB ($8.00/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen2 72B need on B300 SXM 288GB?

72.7 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 94.2 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen2 72B?

It works. Compare $1.73 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1062, and the fastest is 4× Cerebras CS-3 (WSE-3) at 43774 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator