Intel128 GB VRAM3,300 GB/s$2/hr

Codestral 25.08 on Intel Gaudi 3 128GB: 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 Intel Gaudi 3 128GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
37.0 GB needed / 128 GB available
Decode speed
146.3
tok/s per stream
Aggregate
1755
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3166
1× Intel Gaudi 3 128GB
What changes as you add Intel Gaudi 3 128GBs
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× Intel128 GB37.0 GB146.31755429.7 ms$0.3166✓
2× Intel256 GB37.0 GB248.65967230.5 ms$0.1862✓
4× Intel512 GB37.0 GB497.323868130.2 ms$0.0931✓
8× Intel1024 GB37.0 GB994.59547280.1 ms$0.0466✓
Concurrency vs context length
On 1× Intel Gaudi 3 128GB 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?
4K112962.1 MB✓ fits
8K561.88 GB✓ fits
33K147.52 GB✓ fits
66K715.03 GB✓ fits
131K330.06 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
H100 SXM5 80GB1×1782$0.3898
Frequently asked questions

Can Codestral 25.08 run on Intel Gaudi 3 128GB?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and Intel Gaudi 3 128GB has 128 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on Intel Gaudi 3 128GB?

146.3 tokens/sec per stream and 1755 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 429.7 ms, and inter-token latency is 6.8 ms.

What does Codestral 25.08 on Intel Gaudi 3 128GB cost per million tokens?

$0.3166 per 1M output tokens at 1× Intel Gaudi 3 128GB ($2.00/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 Intel Gaudi 3 128GB?

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 Intel Gaudi 3 128GB the right choice for Codestral 25.08?

It works. Compare $0.3166 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