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

Qwen 3 14B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen 3 14B at FP16 needs 28.0 GB for its weights and 38.7 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
38.7 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 GB38.7 GB185.72229341.2 ms$0.9972✓
2× B300576 GB38.7 GB315.77577186.1 ms$0.5866✓
4× B3001152 GB38.7 GB631.430309108.0 ms$0.2933✓
8× B3002304 GB38.7 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?
4K387687.2 MB✓ fits
8K1931.34 GB✓ fits
33K485.37 GB✓ fits
66K2410.74 GB✓ fits
131K1221.47 GB✓ fits
Other GPUs that run Qwen 3 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
A100 40GB SXM41×431.8$0.5790
AMD MI325X 288GB1×1671$0.5817
A100 80GB SXM41×557.1$0.5983
Intel Gaudi 3 128GB1×919.3$0.6043
Frequently asked questions

Can Qwen 3 14B run on B300 SXM 288GB?

Yes. Qwen 3 14B at FP16 needs 38.7 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen 3 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 3 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 3 14B need on B300 SXM 288GB?

28.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 38.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen 3 14B?

It works. Compare $0.9972 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0772, and the fastest is 1× Cerebras CS-3 (WSE-3) at 5571 tok/s.

Adjust the workload yourself

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

Open in calculator