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

Llama 3 8B on RTX 5090 32GB: Does It Fit, How Fast, What It Costs

Llama 3 8B at FP16 needs 16.1 GB for its weights and 24.6 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX 5090 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
24.6 GB needed / 32 GB available
Decode speed
72.53
tok/s per stream
Aggregate
870.3
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1596
1× RTX 5090 32GB
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 GB24.6 GB72.53870.32.55 s$0.1596✓
2× RTX64 GB24.6 GB123.329591.29 s$0.0939✓
4× RTX128 GB24.6 GB246.611837660.5 ms$0.0469✓
8× RTX256 GB24.6 GB493.247346345.3 ms$0.0235✓
Concurrency vs context length
On 1× RTX 5090 32GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K29549.8 MB✓ fits
8K141.07 GB✓ fits
33K34.29 GB✓ fits
66K18.59 GB✓ fits
131K017.18 GB✗ does not fit
Other GPUs that run Llama 3 8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
AMD MI300X 192GB1×2574$0.2698
V100 SXM2 32GB1×437.1$0.3177
A100 40GB SXM41×752.8$0.3321
AMD MI325X 288GB1×2914$0.3336
A100 80GB SXM41×971.4$0.3432
Intel Gaudi 3 128GB1×1603$0.3466
Frequently asked questions

Can Llama 3 8B run on RTX 5090 32GB?

Yes. Llama 3 8B at FP16 needs 24.6 GB including the KV cache, and RTX 5090 32GB has 32 GB — so it fits on a single GPU.

How fast is Llama 3 8B on RTX 5090 32GB?

72.53 tokens/sec per stream and 870.3 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.55 s, and inter-token latency is 13.8 ms.

What does Llama 3 8B on RTX 5090 32GB cost per million tokens?

$0.1596 per 1M output tokens at 1× 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 8B need on RTX 5090 32GB?

16.1 GB for the weights at FP16, plus 8.6 GB of KV cache for 8 concurrent requests at 8K context — 24.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 5090 32GB the right choice for Llama 3 8B?

It works. Compare $0.1596 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0443, and the fastest is 1× Cerebras CS-3 (WSE-3) at 9714 tok/s.

Adjust the workload yourself

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

Open in calculator