NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Llama 3.1 405B on A100 80GB SXM4: 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× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 4×
256.6 GB needed / 320 GB available
Decode speed
17.46
tok/s per stream
Aggregate
838.2
tok/s @ 8 concurrent
Cost / 1M tokens
$1.59
4× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB256.6 GB0.0000.000——✗
2× A100160 GB256.6 GB0.0000.000——✗
4× A100320 GB256.6 GB17.46838.210.72 s$1.59✓
8× A100640 GB256.6 GB34.9233535.38 s$0.7954✓
Concurrency vs context length
On 4× A100 80GB SXM4 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
H100 SXM5 80GB4×1404$1.98
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 A100 80GB SXM4?

Yes, but it needs 4× A100 80GB SXM4. 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 A100 80GB SXM4?

17.46 tokens/sec per stream and 838.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 10.72 s, and inter-token latency is 57.3 ms.

What does Llama 3.1 405B on A100 80GB SXM4 cost per million tokens?

$1.59 per 1M output tokens at 4× A100 80GB SXM4 ($1.20/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 A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for Llama 3.1 405B?

It works. Compare $1.59 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