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

Qwen 3 235B-A22B (MoE) on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen 3 235B-A22B (MoE) at AWQ 4-bit needs 129.3 GB for its weights and 141.9 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
141.9 GB needed / 288 GB available
Decode speed
378.2
tok/s per stream
Aggregate
4538
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4897
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 GB141.9 GB378.24538513.2 ms$0.4897✓
2× B300576 GB141.9 GB642.915430271.9 ms$0.2880✓
4× B3001152 GB141.9 GB128661719150.9 ms$0.1440✓
8× B3002304 GB141.9 GB257224687790.5 ms$0.0720✓
Concurrency vs context length
On 1× B300 SXM 288GB at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K201807.5 MB✓ fits
8K1001.58 GB✓ fits
33K256.31 GB✓ fits
66K1212.62 GB✓ fits
131K625.23 GB✓ fits
Other GPUs that run Qwen 3 235B-A22B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB8×55300$0.0201
L40S 48GB4×6666$0.1167
A100 80GB SXM42×3857$0.1728
H100 SXM5 80GB2×6461$0.2150
AMD MI300X 192GB1×3007$0.2310
H200 SXM5 141GB2×9258$0.2400
Frequently asked questions

Can Qwen 3 235B-A22B (MoE) run on B300 SXM 288GB?

Yes. Qwen 3 235B-A22B (MoE) at AWQ 4-bit needs 141.9 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen 3 235B-A22B (MoE) on B300 SXM 288GB?

378.2 tokens/sec per stream and 4538 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 513.2 ms, and inter-token latency is 2.6 ms.

What does Qwen 3 235B-A22B (MoE) on B300 SXM 288GB cost per million tokens?

$0.4897 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 235B-A22B (MoE) need on B300 SXM 288GB?

129.3 GB for the weights at AWQ 4-bit, plus 12.6 GB of KV cache for 8 concurrent requests at 8K context — 141.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen 3 235B-A22B (MoE)?

It works. Compare $0.4897 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 5090 32GB at $0.0201, and the fastest is 4× Cerebras CS-3 (WSE-3) at 154298 tok/s.

Adjust the workload yourself

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

Open in calculator