Cerebras44 GB VRAM20,000 GB/s

Codestral 25.08 on Cerebras CS-3 (WSE-3): 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 Cerebras CS-3 (WSE-3). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
37.0 GB needed / 44 GB available
Decode speed
886.4
tok/s per stream
Aggregate
10636
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Cerebras CS-3 (WSE-3) · quote required
What changes as you add Cerebras CS-3 (WSE-3)s
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× Cerebras44 GB37.0 GB886.410636607.8 ms—✓
2× Cerebras88 GB37.0 GB150736164319.0 ms—✓
4× Cerebras176 GB37.0 GB3014144655174.5 ms—✓
8× Cerebras352 GB37.0 GB6027578618102.3 ms—✓
Concurrency vs context length
On 1× Cerebras CS-3 (WSE-3) 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?
4K23962.1 MB✓ fits
8K111.88 GB✓ fits
33K27.52 GB✓ fits
66K115.03 GB✓ fits
131K030.06 GB✗ does not fit
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 Cerebras CS-3 (WSE-3)?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and Cerebras CS-3 (WSE-3) has 44 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on Cerebras CS-3 (WSE-3)?

886.4 tokens/sec per stream and 10636 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 607.8 ms, and inter-token latency is 1.1 ms.

What does Codestral 25.08 on Cerebras CS-3 (WSE-3) cost per million tokens?

— per 1M output tokens at 1× Cerebras CS-3 (WSE-3). 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 Cerebras CS-3 (WSE-3)?

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 Cerebras CS-3 (WSE-3) the right choice for Codestral 25.08?

It works. Compare — 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