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

Mixtral 8x22B (MoE) on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Mixtral 8x22B (MoE) at AWQ 4-bit needs 77.6 GB for its weights and 92.6 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
92.6 GB needed / 96 GB available
Decode speed
47.79
tok/s per stream
Aggregate
573.4
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7266
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 GB92.6 GB47.79573.410.27 s$0.7266✓
2× RTX192 GB92.6 GB81.2419505.15 s$0.4274✓
4× RTX384 GB92.6 GB162.577992.59 s$0.2137✓
8× RTX768 GB92.6 GB324.9311951.31 s$0.1069✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K19962.1 MB✓ fits
8K91.88 GB✓ fits
33K27.52 GB✓ fits
66K115.03 GB✓ fits
131K030.06 GB✗ does not fit
Other GPUs that run Mixtral 8x22B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×7799$0.0712
A100 80GB SXM42×2176$0.3064
H100 SXM5 80GB2×3645$0.3811
AMD MI300X 192GB1×1696$0.4095
L40S 48GB2×940.0$0.4137
AMD MI325X 288GB1×1920$0.5064
Frequently asked questions

Can Mixtral 8x22B (MoE) run on RTX PRO 6000 Blackwell 96GB?

Yes. Mixtral 8x22B (MoE) at AWQ 4-bit needs 92.6 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 8x22B (MoE) on RTX PRO 6000 Blackwell 96GB?

47.79 tokens/sec per stream and 573.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 10.27 s, and inter-token latency is 20.9 ms.

What does Mixtral 8x22B (MoE) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.7266 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 8x22B (MoE) need on RTX PRO 6000 Blackwell 96GB?

77.6 GB for the weights at AWQ 4-bit, plus 15.0 GB of KV cache for 8 concurrent requests at 8K context — 92.6 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 8x22B (MoE)?

It works. Compare $0.7266 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.0534, and the fastest is 4× Cerebras CS-3 (WSE-3) at 87040 tok/s.

Adjust the workload yourself

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

Open in calculator