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

Gemma 2 27B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Gemma 2 27B at FP8 (E4M3) needs 27.0 GB for its weights and 51.7 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
51.7 GB needed / 288 GB available
Decode speed
288.9
tok/s per stream
Aggregate
3467
tok/s @ 8 concurrent
Cost / 1M tokens
$0.6410
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 GB51.7 GB288.93467623.3 ms$0.6410✓
2× B300576 GB51.7 GB491.111787326.9 ms$0.3771✓
4× B3001152 GB51.7 GB982.247147178.5 ms$0.1885✓
8× B3002304 GB51.7 GB1964188587104.2 ms$0.0943✓
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?
4K1691.54 GB✓ fits
8K843.09 GB✓ fits
33K2112.35 GB✓ fits
66K1024.70 GB✓ fits
131K549.39 GB✓ fits
Other GPUs that run Gemma 2 27B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2640$0.1052
AMD MI300X 192GB1×2297$0.3024
L40S 48GB2×1273$0.3055
AMD MI325X 288GB1×2600$0.3739
A100 80GB SXM41×866.7$0.3846
Intel Gaudi 3 128GB1×1430$0.3885
Frequently asked questions

Can Gemma 2 27B run on B300 SXM 288GB?

Yes. Gemma 2 27B at FP8 (E4M3) needs 51.7 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Gemma 2 27B on B300 SXM 288GB?

288.9 tokens/sec per stream and 3467 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 623.3 ms, and inter-token latency is 3.5 ms.

What does Gemma 2 27B on B300 SXM 288GB cost per million tokens?

$0.6410 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 Gemma 2 27B need on B300 SXM 288GB?

27.0 GB for the weights at FP8 (E4M3), plus 24.7 GB of KV cache for 8 concurrent requests at 8K context — 51.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Gemma 2 27B?

It works. Compare $0.6410 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0248, and the fastest is 2× Cerebras CS-3 (WSE-3) at 29467 tok/s.

Adjust the workload yourself

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

Open in calculator