NVIDIA96 GB VRAM1,792 GB/s$1.5/hr

Codestral 25.08 on RTX PRO 6000 Blackwell 96GB: 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 RTX PRO 6000 Blackwell 96GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
37.0 GB needed / 96 GB available
Decode speed
79.42
tok/s per stream
Aggregate
953.0
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4372
1× RTX PRO 6000 Blackwell 96GB
What changes as you add RTX PRO 6000 Blackwell 96GBs
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× RTX96 GB37.0 GB79.42953.05.81 s$0.4372✓
2× RTX192 GB37.0 GB135.032402.92 s$0.2572✓
4× RTX384 GB37.0 GB270.0129611.48 s$0.1286✓
8× RTX768 GB37.0 GB540.051844752.7 ms$0.0643✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB 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?
4K78962.1 MB✓ fits
8K391.88 GB✓ fits
33K97.52 GB✓ fits
66K415.03 GB✓ fits
131K230.06 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 RTX PRO 6000 Blackwell 96GB?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on RTX PRO 6000 Blackwell 96GB?

79.42 tokens/sec per stream and 953.0 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 5.81 s, and inter-token latency is 12.6 ms.

What does Codestral 25.08 on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.4372 per 1M output tokens at 1× RTX PRO 6000 Blackwell 96GB ($1.50/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 RTX PRO 6000 Blackwell 96GB?

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 RTX PRO 6000 Blackwell 96GB the right choice for Codestral 25.08?

It works. Compare $0.4372 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