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

GPT-NeoX 20B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

GPT-NeoX 20B at FP8 (E4M3) needs 20.0 GB for its weights and 90.9 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
90.9 GB needed / 288 GB available
Decode speed
390.0
tok/s per stream
Aggregate
4680
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4748
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 GB90.9 GB390.04680469.5 ms$0.4748✓
2× B300576 GB90.9 GB663.015912250.0 ms$0.2793✓
4× B3001152 GB90.9 GB132663648140.0 ms$0.1397✓
8× B3002304 GB90.9 GB265225459285.0 ms$0.0698✓
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?
4K604.43 GB✓ fits
8K308.86 GB✓ fits
33K735.43 GB✓ fits
66K370.87 GB✓ fits
131K1141.73 GB✓ fits
Other GPUs that run GPT-NeoX 20B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×14257$0.0390
A100 80GB SXM42×3978$0.1676
H100 SXM5 80GB2×6663$0.2084
AMD MI300X 192GB1×3101$0.2240
L40S 48GB2×1718$0.2263
AMD MI325X 288GB1×3510$0.2770
Frequently asked questions

Can GPT-NeoX 20B run on B300 SXM 288GB?

Yes. GPT-NeoX 20B at FP8 (E4M3) needs 90.9 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is GPT-NeoX 20B on B300 SXM 288GB?

390.0 tokens/sec per stream and 4680 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 469.5 ms, and inter-token latency is 2.6 ms.

What does GPT-NeoX 20B on B300 SXM 288GB cost per million tokens?

$0.4748 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 GPT-NeoX 20B need on B300 SXM 288GB?

20.0 GB for the weights at FP8 (E4M3), plus 70.9 GB of KV cache for 8 concurrent requests at 8K context — 90.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for GPT-NeoX 20B?

It works. Compare $0.4748 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.0292, and the fastest is 4× Cerebras CS-3 (WSE-3) at 159120 tok/s.

Adjust the workload yourself

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

Open in calculator