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

Mixtral 8x7B (MoE) on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Mixtral 8x7B (MoE) at FP8 (E4M3) needs 46.7 GB for its weights and 55.3 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
55.3 GB needed / 96 GB available
Decode speed
135.4
tok/s per stream
Aggregate
1625
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2564
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 GB55.3 GB135.416253.42 s$0.2564✓
2× RTX192 GB55.3 GB230.355261.73 s$0.1508✓
4× RTX384 GB55.3 GB460.522104877.6 ms$0.0754✓
8× RTX768 GB55.3 GB921.088416453.8 ms$0.0377✓
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?
4K91549.8 MB✓ fits
8K451.07 GB✓ fits
33K114.29 GB✓ fits
66K58.59 GB✓ fits
131K217.18 GB✓ fits
Other GPUs that run Mixtral 8x7B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×5526$0.0503
AMD MI300X 192GB1×4807$0.1445
L40S 48GB2×2664$0.1460
AMD MI325X 288GB1×5442$0.1787
A100 80GB SXM41×1814$0.1838
Intel Gaudi 3 128GB1×2993$0.1856
Frequently asked questions

Can Mixtral 8x7B (MoE) run on RTX PRO 6000 Blackwell 96GB?

Yes. Mixtral 8x7B (MoE) at FP8 (E4M3) needs 55.3 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Mixtral 8x7B (MoE) on RTX PRO 6000 Blackwell 96GB?

135.4 tokens/sec per stream and 1625 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.42 s, and inter-token latency is 7.4 ms.

What does Mixtral 8x7B (MoE) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.2564 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 Mixtral 8x7B (MoE) need on RTX PRO 6000 Blackwell 96GB?

46.7 GB for the weights at FP8 (E4M3), plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 55.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX PRO 6000 Blackwell 96GB the right choice for Mixtral 8x7B (MoE)?

It works. Compare $0.2564 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0119, and the fastest is 2× Cerebras CS-3 (WSE-3) at 61674 tok/s.

Adjust the workload yourself

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

Open in calculator