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

Llama 4 Scout 17B-16E (MoE, VLM) on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Llama 4 Scout 17B-16E (MoE, VLM) at AWQ 4-bit needs 60.0 GB for its weights and 72.8 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
72.8 GB needed / 96 GB available
Decode speed
109.6
tok/s per stream
Aggregate
1316
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3167
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 GB72.8 GB109.613164.50 s$0.3167✓
2× RTX192 GB72.8 GB186.444732.26 s$0.1863✓
4× RTX384 GB72.8 GB372.7178911.15 s$0.0932✓
8× RTX768 GB72.8 GB745.571565588.4 ms$0.0466✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K44824.6 MB✓ fits
8K221.61 GB✓ fits
33K56.44 GB✓ fits
66K212.88 GB✓ fits
131K125.77 GB✓ fits
Other GPUs that run Llama 4 Scout 17B-16E (MoE, VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×17891$0.0311
AMD MI300X 192GB1×3891$0.1785
L40S 48GB2×2157$0.1803
AMD MI325X 288GB1×4405$0.2207
A100 80GB SXM41×1468$0.2270
Intel Gaudi 3 128GB1×2423$0.2293
Frequently asked questions

Can Llama 4 Scout 17B-16E (MoE, VLM) run on RTX PRO 6000 Blackwell 96GB?

Yes. Llama 4 Scout 17B-16E (MoE, VLM) at AWQ 4-bit needs 72.8 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 4 Scout 17B-16E (MoE, VLM) on RTX PRO 6000 Blackwell 96GB?

109.6 tokens/sec per stream and 1316 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 4.50 s, and inter-token latency is 9.1 ms.

What does Llama 4 Scout 17B-16E (MoE, VLM) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.3167 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 4 Scout 17B-16E (MoE, VLM) need on RTX PRO 6000 Blackwell 96GB?

60.0 GB for the weights at AWQ 4-bit, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 72.8 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 4 Scout 17B-16E (MoE, VLM)?

It works. Compare $0.3167 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0146, and the fastest is 2× Cerebras CS-3 (WSE-3) at 49920 tok/s.

Adjust the workload yourself

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

Open in calculator