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

Codestral 25.08 on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Codestral 25.08 at FP8 (E4M3) needs 22.0 GB for its weights and 37.0 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
37.0 GB needed / 288 GB available
Decode speed
354.5
tok/s per stream
Aggregate
4255
tok/s @ 8 concurrent
Cost / 1M tokens
$0.5223
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 GB37.0 GB354.54255513.4 ms$0.5223✓
2× B300576 GB37.0 GB602.714465272.0 ms$0.3072✓
4× B3001152 GB37.0 GB120557862151.0 ms$0.1536✓
8× B3002304 GB37.0 GB241123144790.5 ms$0.0768✓
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?
4K283962.1 MB✓ fits
8K1411.88 GB✓ fits
33K357.52 GB✓ fits
66K1715.03 GB✓ fits
131K830.06 GB✓ fits
Other GPUs that run Codestral 25.08
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×3240$0.0857
AMD MI300X 192GB1×2819$0.2464
A100 40GB SXM41×824.3$0.3033
AMD MI325X 288GB1×3191$0.3047
A100 80GB SXM41×1064$0.3134
Intel Gaudi 3 128GB1×1755$0.3166
Frequently asked questions

Can Codestral 25.08 run on B300 SXM 288GB?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on B300 SXM 288GB?

354.5 tokens/sec per stream and 4255 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 513.4 ms, and inter-token latency is 2.8 ms.

What does Codestral 25.08 on B300 SXM 288GB cost per million tokens?

$0.5223 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 Codestral 25.08 need on B300 SXM 288GB?

22.0 GB for the weights at FP8 (E4M3), plus 15.0 GB of KV cache for 8 concurrent requests at 8K context — 37.0 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Codestral 25.08?

It works. Compare $0.5223 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0404, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10636 tok/s.

Adjust the workload yourself

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

Open in calculator