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

Llama 3.1 405B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Llama 3.1 405B at AWQ 4-bit needs 222.8 GB for its weights and 256.6 GB in total at 8K context with 8 concurrent users. That means it needs 4× H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 4×
256.6 GB needed / 320 GB available
Decode speed
29.25
tok/s per stream
Aggregate
1404
tok/s @ 8 concurrent
Cost / 1M tokens
$1.98
4× 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 GB256.6 GB0.0000.000——✗
2× H100160 GB256.6 GB0.0000.000——✗
4× H100320 GB256.6 GB29.2514043.42 s$1.98✓
8× H100640 GB256.6 GB58.5056161.72 s$0.9893✓
Concurrency vs context length
On 4× 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?
4K462.11 GB✓ fits
8K234.23 GB✓ fits
33K516.91 GB✓ fits
66K233.82 GB✓ fits
131K167.65 GB✓ fits
Other GPUs that run Llama 3.1 405B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
L40S 48GB8×1448$1.07
A100 80GB SXM44×838.2$1.59
B200 SXM 192GB2×838.2$3.98
H200 SXM5 141GB2×502.9$4.42
AMD MI325X 288GB1×184.9$5.26
B300 SXM 288GB1×246.5$9.01
Frequently asked questions

Can Llama 3.1 405B run on H100 SXM5 80GB?

Yes, but it needs 4× H100 SXM5 80GB. At AWQ 4-bit the total footprint is 256.6 GB (222.8 GB of weights plus 33.8 GB of KV cache at 8K × 8 concurrent users), against 320 GB of available VRAM across 4 GPUs.

How fast is Llama 3.1 405B on H100 SXM5 80GB?

29.25 tokens/sec per stream and 1404 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.42 s, and inter-token latency is 34.2 ms.

What does Llama 3.1 405B on H100 SXM5 80GB cost per million tokens?

$1.98 per 1M output tokens at 4× 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 Llama 3.1 405B need on H100 SXM5 80GB?

222.8 GB for the weights at AWQ 4-bit, plus 33.8 GB of KV cache for 8 concurrent requests at 8K context — 256.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Llama 3.1 405B?

It works. Compare $1.98 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× A100 40GB SXM4 at $0.7697, and the fastest is 8× Cerebras CS-3 (WSE-3) at 33527 tok/s.

Adjust the workload yourself

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

Open in calculator