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

Qwen3.8 27B (VLM) on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Qwen3.8 27B (VLM) at FP8 (E4M3) needs 27.8 GB for its weights and 33.3 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
33.3 GB needed / 288 GB available
Decode speed
280.8
tok/s per stream
Aggregate
3369
tok/s @ 8 concurrent
Cost / 1M tokens
$0.6596
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 GB33.3 GB280.83369640.4 ms$0.6596✓
2× B300576 GB33.3 GB477.311455335.5 ms$0.3880✓
4× B3001152 GB33.3 GB954.645821182.8 ms$0.1940✓
8× B3002304 GB33.3 GB1909183285106.4 ms$0.0970✓
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?
4K6201.07 GB✓ fits
8K3782.15 GB✓ fits
33K1138.59 GB✓ fits
66K5817.18 GB✓ fits
131K2934.36 GB✓ fits
Other GPUs that run Qwen3.8 27B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2566$0.1083
AMD MI300X 192GB1×2232$0.3111
A100 40GB SXM41×652.8$0.3830
AMD MI325X 288GB1×2527$0.3847
A100 80GB SXM41×842.3$0.3957
Intel Gaudi 3 128GB1×1390$0.3997
Frequently asked questions

Can Qwen3.8 27B (VLM) run on B300 SXM 288GB?

Yes. Qwen3.8 27B (VLM) at FP8 (E4M3) needs 33.3 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Qwen3.8 27B (VLM) on B300 SXM 288GB?

280.8 tokens/sec per stream and 3369 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 640.4 ms, and inter-token latency is 3.6 ms.

What does Qwen3.8 27B (VLM) on B300 SXM 288GB cost per million tokens?

$0.6596 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 Qwen3.8 27B (VLM) need on B300 SXM 288GB?

27.8 GB for the weights at FP8 (E4M3), plus 5.5 GB of KV cache for 8 concurrent requests at 8K context — 33.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Qwen3.8 27B (VLM)?

It works. Compare $0.6596 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0511, and the fastest is 1× Cerebras CS-3 (WSE-3) at 8423 tok/s.

Adjust the workload yourself

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

Open in calculator