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

Qwen 2.5 14B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen 2.5 14B at FP16 needs 28.0 GB for its weights and 40.9 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
40.9 GB needed / 288 GB available
Decode speed
185.7
tok/s per stream
Aggregate
2229
tok/s @ 8 concurrent
Cost / 1M tokens
$0.9972
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 GB40.9 GB185.72229341.2 ms$0.9972✓
2× B300576 GB40.9 GB315.77577186.1 ms$0.5866✓
4× B3001152 GB40.9 GB631.430309108.0 ms$0.2933✓
8× B3002304 GB40.9 GB126312123469.0 ms$0.1466✓
Concurrency vs context length
On 1× B300 SXM 288GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K322824.6 MB✓ fits
8K1611.61 GB✓ fits
33K406.44 GB✓ fits
66K2012.88 GB✓ fits
131K1025.77 GB✓ fits
Other GPUs that run Qwen 2.5 14B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1697$0.1637
AMD MI300X 192GB1×1476$0.4704
AMD MI325X 288GB1×1671$0.5817
A100 80GB SXM41×557.1$0.5983
Intel Gaudi 3 128GB1×919.3$0.6043
H100 SXM5 80GB1×933.2$0.7441
Frequently asked questions

Can Qwen 2.5 14B run on B300 SXM 288GB?

Yes. Qwen 2.5 14B at FP16 needs 40.9 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen 2.5 14B on B300 SXM 288GB?

185.7 tokens/sec per stream and 2229 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 341.2 ms, and inter-token latency is 5.4 ms.

What does Qwen 2.5 14B on B300 SXM 288GB cost per million tokens?

$0.9972 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 Qwen 2.5 14B need on B300 SXM 288GB?

28.0 GB for the weights at FP16, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 40.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen 2.5 14B?

It works. Compare $0.9972 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0386, and the fastest is 8× RTX 3080 10GB at 11517 tok/s.

Adjust the workload yourself

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

Open in calculator