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

Qwen 3 32B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen 3 32B at FP8 (E4M3) needs 32.0 GB for its weights and 49.2 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
49.2 GB needed / 288 GB available
Decode speed
243.8
tok/s per stream
Aggregate
2925
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7597
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 GB49.2 GB243.82925733.2 ms$0.7597✓
2× B300576 GB49.2 GB414.49945381.9 ms$0.4469✓
4× B3001152 GB49.2 GB828.839780206.0 ms$0.2235✓
8× B3002304 GB49.2 GB1658159120118.0 ms$0.1117✓
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?
4K2381.07 GB✓ fits
8K1192.15 GB✓ fits
33K298.59 GB✓ fits
66K1417.18 GB✓ fits
131K734.36 GB✓ fits
Other GPUs that run Qwen 3 32B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2228$0.1247
AMD MI300X 192GB1×1938$0.3584
L40S 48GB2×1074$0.3621
AMD MI325X 288GB1×2194$0.4432
A100 80GB SXM41×731.3$0.4558
Intel Gaudi 3 128GB1×1207$0.4604
Frequently asked questions

Can Qwen 3 32B run on B300 SXM 288GB?

Yes. Qwen 3 32B at FP8 (E4M3) needs 49.2 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen 3 32B on B300 SXM 288GB?

243.8 tokens/sec per stream and 2925 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 733.2 ms, and inter-token latency is 4.1 ms.

What does Qwen 3 32B on B300 SXM 288GB cost per million tokens?

$0.7597 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 32B need on B300 SXM 288GB?

32.0 GB for the weights at FP8 (E4M3), plus 17.2 GB of KV cache for 8 concurrent requests at 8K context — 49.2 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen 3 32B?

It works. Compare $0.7597 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0294, and the fastest is 2× Cerebras CS-3 (WSE-3) at 24863 tok/s.

Adjust the workload yourself

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

Open in calculator