NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Llama 4 Scout 17B-16E (MoE, VLM) on A100 80GB SXM4: 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 A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
72.8 GB needed / 80 GB available
Decode speed
122.4
tok/s per stream
Aggregate
1468
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2270
1× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB72.8 GB122.414681.82 s$0.2270✓
2× A100160 GB72.8 GB208.04992927.5 ms$0.1335✓
4× A100320 GB72.8 GB416.019968478.8 ms$0.0668✓
8× A100640 GB72.8 GB832.079872254.4 ms$0.0334✓
Concurrency vs context length
On 1× A100 80GB SXM4 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?
4K24824.6 MB✓ fits
8K121.61 GB✓ fits
33K36.44 GB✓ fits
66K112.88 GB✓ fits
131K025.77 GB✗ does not fit
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
Intel Gaudi 3 128GB1×2423$0.2293
H100 SXM5 80GB1×2459$0.2824
Frequently asked questions

Can Llama 4 Scout 17B-16E (MoE, VLM) run on A100 80GB SXM4?

Yes. Llama 4 Scout 17B-16E (MoE, VLM) at AWQ 4-bit needs 72.8 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Llama 4 Scout 17B-16E (MoE, VLM) on A100 80GB SXM4?

122.4 tokens/sec per stream and 1468 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.82 s, and inter-token latency is 8.2 ms.

What does Llama 4 Scout 17B-16E (MoE, VLM) on A100 80GB SXM4 cost per million tokens?

$0.2270 per 1M output tokens at 1× A100 80GB SXM4 ($1.20/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 A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for Llama 4 Scout 17B-16E (MoE, VLM)?

It works. Compare $0.2270 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