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

Llama 3.3 70B on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Llama 3.3 70B at FP8 (E4M3) needs 70.6 GB for its weights and 92.1 GB in total at 8K context with 8 concurrent users. That means it needs 2× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
92.1 GB needed / 160 GB available
Decode speed
46.95
tok/s per stream
Aggregate
1127
tok/s @ 8 concurrent
Cost / 1M tokens
$0.5916
2× 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 GB92.1 GB0.0000.000——✗
2× A100160 GB92.1 GB46.9511273.76 s$0.5916✓
4× A100320 GB92.1 GB93.9145081.89 s$0.2958✓
8× A100640 GB92.1 GB187.818031962.2 ms$0.1479✓
Concurrency vs context length
On 2× A100 80GB SXM4 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?
4K661.34 GB✓ fits
8K332.68 GB✓ fits
33K810.74 GB✓ fits
66K421.47 GB✓ fits
131K242.95 GB✓ fits
Other GPUs that run Llama 3.3 70B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×4039$0.1376
H100 SXM5 80GB2×1888$0.7358
AMD MI300X 192GB1×878.3$0.7906
L40S 48GB2×486.8$0.7988
AMD MI325X 288GB1×994.3$0.9778
Intel Gaudi 3 128GB1×546.9$1.02
Frequently asked questions

Can Llama 3.3 70B run on A100 80GB SXM4?

Yes, but it needs 2× A100 80GB SXM4. At FP8 (E4M3) the total footprint is 92.1 GB (70.6 GB of weights plus 21.5 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is Llama 3.3 70B on A100 80GB SXM4?

46.95 tokens/sec per stream and 1127 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.76 s, and inter-token latency is 21.3 ms.

What does Llama 3.3 70B on A100 80GB SXM4 cost per million tokens?

$0.5916 per 1M output tokens at 2× 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 3.3 70B need on A100 80GB SXM4?

70.6 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 92.1 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Llama 3.3 70B?

It works. Compare $0.5916 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1031, and the fastest is 4× Cerebras CS-3 (WSE-3) at 45076 tok/s.

Adjust the workload yourself

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

Open in calculator