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

Llama 2 7B on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Llama 2 7B at FP16 needs 13.5 GB for its weights and 47.8 GB in total at 8K context with 8 concurrent users. That means it fits on a single A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
47.8 GB needed / 80 GB available
Decode speed
96.44
tok/s per stream
Aggregate
1157
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2880
1× 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 GB47.8 GB96.441157748.2 ms$0.2880✓
2× A100160 GB47.8 GB163.93935390.0 ms$0.1694✓
4× A100320 GB47.8 GB327.915739210.0 ms$0.0847✓
8× A100640 GB47.8 GB655.862955120.0 ms$0.0424✓
Concurrency vs context length
On 1× A100 80GB SXM4 at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K302.15 GB✓ fits
8K154.29 GB✓ fits
33K317.18 GB✓ fits
66K134.36 GB✓ fits
131K068.72 GB✗ does not fit
Other GPUs that run Llama 2 7B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×3526$0.0788
AMD MI300X 192GB1×3067$0.2264
AMD MI325X 288GB1×3472$0.2800
Intel Gaudi 3 128GB1×1909$0.2909
H100 SXM5 80GB1×1938$0.3583
B200 SXM 192GB1×4629$0.3600
Frequently asked questions

Can Llama 2 7B run on A100 80GB SXM4?

Yes. Llama 2 7B at FP16 needs 47.8 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Llama 2 7B on A100 80GB SXM4?

96.44 tokens/sec per stream and 1157 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 748.2 ms, and inter-token latency is 10.4 ms.

What does Llama 2 7B on A100 80GB SXM4 cost per million tokens?

$0.2880 per 1M output tokens at 1× 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 2 7B need on A100 80GB SXM4?

13.5 GB for the weights at FP16, plus 34.4 GB of KV cache for 8 concurrent requests at 8K context — 47.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Llama 2 7B?

It works. Compare $0.2880 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0186, and the fastest is 2× Cerebras CS-3 (WSE-3) at 39347 tok/s.

Adjust the workload yourself

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

Open in calculator