NVIDIA96 GB VRAM1,792 GB/s$1.5/hr

Llama 3 8B on RTX PRO 6000 Blackwell 96GB: 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 PRO 6000 Blackwell 96GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
24.6 GB needed / 96 GB available
Decode speed
72.53
tok/s per stream
Aggregate
870.3
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4787
1× RTX PRO 6000 Blackwell 96GB
What changes as you add RTX PRO 6000 Blackwell 96GBs
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× RTX96 GB24.6 GB72.53870.32.15 s$0.4787✓
2× RTX192 GB24.6 GB123.329591.09 s$0.2816✓
4× RTX384 GB24.6 GB246.611837560.3 ms$0.1408✓
8× RTX768 GB24.6 GB493.247346295.2 ms$0.0704✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K148549.8 MB✓ fits
8K741.07 GB✓ fits
33K184.29 GB✓ fits
66K98.59 GB✓ fits
131K417.18 GB✓ fits
Other GPUs that run Llama 3 8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB1×870.3$0.1596
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
Frequently asked questions

Can Llama 3 8B run on RTX PRO 6000 Blackwell 96GB?

Yes. Llama 3 8B at FP16 needs 24.6 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Llama 3 8B on RTX PRO 6000 Blackwell 96GB?

72.53 tokens/sec per stream and 870.3 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.15 s, and inter-token latency is 13.8 ms.

What does Llama 3 8B on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.4787 per 1M output tokens at 1× RTX PRO 6000 Blackwell 96GB ($1.50/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 PRO 6000 Blackwell 96GB?

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 PRO 6000 Blackwell 96GB the right choice for Llama 3 8B?

It works. Compare $0.4787 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