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

Qwen 3 30B-A3B (MoE) on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen 3 30B-A3B (MoE) at FP8 (E4M3) needs 30.0 GB for its weights and 36.4 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
36.4 GB needed / 288 GB available
Decode speed
2600
tok/s per stream
Aggregate
31200
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0712
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 GB36.4 GB26003120095.9 ms$0.0712✓
2× B300576 GB36.4 GB442010608063.0 ms$0.0419✓
4× B3001152 GB36.4 GB884042432046.5 ms$0.0209✓
8× B3002304 GB36.4 GB17680169728038.2 ms$0.0105✓
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?
4K640412.3 MB✓ fits
8K320824.6 MB✓ fits
33K803.22 GB✓ fits
66K406.44 GB✓ fits
131K2012.88 GB✓ fits
Other GPUs that run Qwen 3 30B-A3B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×23762$0.0117
AMD MI300X 192GB1×20670$0.0336
A100 40GB SXM41×6045$0.0414
AMD MI325X 288GB1×23400$0.0415
A100 80GB SXM41×7800$0.0427
Intel Gaudi 3 128GB1×12870$0.0432
Frequently asked questions

Can Qwen 3 30B-A3B (MoE) run on B300 SXM 288GB?

Yes. Qwen 3 30B-A3B (MoE) at FP8 (E4M3) needs 36.4 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen 3 30B-A3B (MoE) on B300 SXM 288GB?

2600 tokens/sec per stream and 31200 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 95.9 ms, and inter-token latency is 384.6 µs.

What does Qwen 3 30B-A3B (MoE) on B300 SXM 288GB cost per million tokens?

$0.0712 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 30B-A3B (MoE) need on B300 SXM 288GB?

30.0 GB for the weights at FP8 (E4M3), plus 6.4 GB of KV cache for 8 concurrent requests at 8K context — 36.4 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen 3 30B-A3B (MoE)?

It works. Compare $0.0712 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.005513, and the fastest is 1× Cerebras CS-3 (WSE-3) at 78000 tok/s.

Adjust the workload yourself

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

Open in calculator