NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Codestral 25.08 on A100 80GB SXM4: 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 A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
37.0 GB needed / 80 GB available
Decode speed
88.64
tok/s per stream
Aggregate
1064
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3134
1× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB37.0 GB88.6410642.35 s$0.3134✓
2× A100160 GB37.0 GB150.736161.19 s$0.1843✓
4× A100320 GB37.0 GB301.414465611.0 ms$0.0922✓
8× A100640 GB37.0 GB602.757862320.5 ms$0.0461✓
Concurrency vs context length
On 1× A100 80GB SXM4 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?
4K61962.1 MB✓ fits
8K301.88 GB✓ fits
33K77.52 GB✓ fits
66K315.03 GB✓ fits
131K130.06 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
Intel Gaudi 3 128GB1×1755$0.3166
H100 SXM5 80GB1×1782$0.3898
Frequently asked questions

Can Codestral 25.08 run on A100 80GB SXM4?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on A100 80GB SXM4?

88.64 tokens/sec per stream and 1064 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.35 s, and inter-token latency is 11.3 ms.

What does Codestral 25.08 on A100 80GB SXM4 cost per million tokens?

$0.3134 per 1M output tokens at 1× A100 80GB SXM4 ($1.20/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 A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for Codestral 25.08?

It works. Compare $0.3134 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