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

Mixtral 8x7B (MoE) on H100 SXM5 80GB: 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 H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
55.3 GB needed / 80 GB available
Decode speed
253.2
tok/s per stream
Aggregate
3038
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2286
1× 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 GB55.3 GB253.23038460.9 ms$0.2286✓
2× H100160 GB55.3 GB430.410330245.8 ms$0.1344✓
4× H100320 GB55.3 GB860.941322137.9 ms$0.0672✓
8× H100640 GB55.3 GB172216528784.0 ms$0.0336✓
Concurrency vs context length
On 1× H100 SXM5 80GB 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?
4K62549.8 MB✓ fits
8K311.07 GB✓ fits
33K74.29 GB✓ fits
66K38.59 GB✓ fits
131K117.18 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 H100 SXM5 80GB?

Yes. Mixtral 8x7B (MoE) at FP8 (E4M3) needs 55.3 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Mixtral 8x7B (MoE) on H100 SXM5 80GB?

253.2 tokens/sec per stream and 3038 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 460.9 ms, and inter-token latency is 3.9 ms.

What does Mixtral 8x7B (MoE) on H100 SXM5 80GB cost per million tokens?

$0.2286 per 1M output tokens at 1× 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 8x7B (MoE) need on H100 SXM5 80GB?

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 H100 SXM5 80GB the right choice for Mixtral 8x7B (MoE)?

It works. Compare $0.2286 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