NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

Mixtral 8x22B (MoE) on H100 SXM5 80GB: 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 needs 2× H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
92.6 GB needed / 160 GB available
Decode speed
151.9
tok/s per stream
Aggregate
3645
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3811
2× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
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× H10080 GB92.6 GB0.0000.000——✗
2× H100160 GB92.6 GB151.93645682.0 ms$0.3811✓
4× H100320 GB92.6 GB303.714579356.0 ms$0.1905✓
8× H100640 GB92.6 GB607.558317193.0 ms$0.0953✓
Concurrency vs context length
On 2× H100 SXM5 80GB 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?
4K87962.1 MB✓ fits
8K431.88 GB✓ fits
33K107.52 GB✓ fits
66K515.03 GB✓ fits
131K230.06 GB✓ fits
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
AMD MI300X 192GB1×1696$0.4095
L40S 48GB2×940.0$0.4137
AMD MI325X 288GB1×1920$0.5064
Intel Gaudi 3 128GB1×1056$0.5261
Frequently asked questions

Can Mixtral 8x22B (MoE) run on H100 SXM5 80GB?

Yes, but it needs 2× H100 SXM5 80GB. At AWQ 4-bit the total footprint is 92.6 GB (77.6 GB of weights plus 15.0 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is Mixtral 8x22B (MoE) on H100 SXM5 80GB?

151.9 tokens/sec per stream and 3645 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 682.0 ms, and inter-token latency is 6.6 ms.

What does Mixtral 8x22B (MoE) on H100 SXM5 80GB cost per million tokens?

$0.3811 per 1M output tokens at 2× H100 SXM5 80GB ($2.50/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 H100 SXM5 80GB?

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 H100 SXM5 80GB the right choice for Mixtral 8x22B (MoE)?

It works. Compare $0.3811 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