NVIDIA48 GB VRAM864 GB/s$0.7/hr

Llama 3.3 70B on L40S 48GB: 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× L40S 48GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
92.1 GB needed / 96 GB available
Decode speed
20.28
tok/s per stream
Aggregate
486.8
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7988
2× L40S 48GB

L40S 48GB has no NVLink, so 2-way tensor parallelism runs over PCIe. tokcalc applies a flat 0.85× multi-GPU efficiency factor for this, which is realistic for NVLink-equipped datacenter cards but optimistic for PCIe scale-out — treat the throughput and cost figures for this configuration as an upper bound, and measure before committing.

What changes as you add L40S 48GBs
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× L40S48 GB92.1 GB0.0000.000——✗
2× L40S96 GB92.1 GB20.28486.812.71 s$0.7988✓
4× L40S192 GB92.1 GB40.5719476.37 s$0.3994✓
8× L40S384 GB92.1 GB81.1477893.20 s$0.1997✓
Concurrency vs context length
On 2× L40S 48GB 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?
4K181.34 GB✓ fits
8K92.68 GB✓ fits
33K210.74 GB✓ fits
66K121.47 GB✓ fits
131K042.95 GB✗ does not fit
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
A100 80GB SXM42×1127$0.5916
H100 SXM5 80GB2×1888$0.7358
AMD MI300X 192GB1×878.3$0.7906
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 L40S 48GB?

Yes, but it needs 2× L40S 48GB. 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 96 GB of available VRAM across 2 GPUs.

How fast is Llama 3.3 70B on L40S 48GB?

20.28 tokens/sec per stream and 486.8 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 12.71 s, and inter-token latency is 49.3 ms.

What does Llama 3.3 70B on L40S 48GB cost per million tokens?

$0.7988 per 1M output tokens at 2× L40S 48GB ($0.7000/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 L40S 48GB?

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 L40S 48GB the right choice for Llama 3.3 70B?

It works. Compare $0.7988 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