NVIDIA32 GB VRAM1,792 GB/s$0.5/hr

Llama 3 70B on RTX 5090 32GB: Does It Fit, How Fast, What It Costs

Llama 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 4× RTX 5090 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 4×
92.1 GB needed / 128 GB available
Decode speed
84.14
tok/s per stream
Aggregate
4039
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1376
4× RTX 5090 32GB

RTX 5090 32GB has no NVLink, so 4-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 RTX 5090 32GBs
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× RTX32 GB92.1 GB0.0000.000——✗
2× RTX64 GB92.1 GB0.0000.000——✗
4× RTX128 GB92.1 GB84.1440395.55 s$0.1376✓
8× RTX256 GB92.1 GB168.3161552.79 s$0.0688✓
Concurrency vs context length
On 4× RTX 5090 32GB 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?
4K421.34 GB✓ fits
8K212.68 GB✓ fits
33K510.74 GB✓ fits
66K221.47 GB✓ fits
131K142.95 GB✓ fits
Other GPUs that run Llama 3 70B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
A100 80GB SXM42×1127$0.5916
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 70B run on RTX 5090 32GB?

Yes, but it needs 4× RTX 5090 32GB. 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 128 GB of available VRAM across 4 GPUs.

How fast is Llama 3 70B on RTX 5090 32GB?

84.14 tokens/sec per stream and 4039 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 5.55 s, and inter-token latency is 11.9 ms.

What does Llama 3 70B on RTX 5090 32GB cost per million tokens?

$0.1376 per 1M output tokens at 4× RTX 5090 32GB ($0.5000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Llama 3 70B need on RTX 5090 32GB?

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 RTX 5090 32GB the right choice for Llama 3 70B?

It works. Compare $0.1376 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