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

Qwen3.8 27B (VLM) on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Qwen3.8 27B (VLM) at FP8 (E4M3) needs 27.8 GB for its weights and 33.3 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
33.3 GB needed / 80 GB available
Decode speed
70.19
tok/s per stream
Aggregate
842.3
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3957
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 GB33.3 GB70.19842.32.96 s$0.3957✓
2× A100160 GB33.3 GB119.328641.50 s$0.2328✓
4× A100320 GB33.3 GB238.711455763.6 ms$0.1164✓
8× A100640 GB33.3 GB477.345821396.8 ms$0.0582✓
Concurrency vs context length
On 1× A100 80GB SXM4 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?
4K1241.07 GB✓ fits
8K752.15 GB✓ fits
33K228.59 GB✓ fits
66K1117.18 GB✓ fits
131K534.36 GB✓ fits
Other GPUs that run Qwen3.8 27B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2566$0.1083
AMD MI300X 192GB1×2232$0.3111
A100 40GB SXM41×652.8$0.3830
AMD MI325X 288GB1×2527$0.3847
Intel Gaudi 3 128GB1×1390$0.3997
H100 SXM5 80GB1×1411$0.4922
Frequently asked questions

Can Qwen3.8 27B (VLM) run on A100 80GB SXM4?

Yes. Qwen3.8 27B (VLM) at FP8 (E4M3) needs 33.3 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Qwen3.8 27B (VLM) on A100 80GB SXM4?

70.19 tokens/sec per stream and 842.3 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.96 s, and inter-token latency is 14.2 ms.

What does Qwen3.8 27B (VLM) on A100 80GB SXM4 cost per million tokens?

$0.3957 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 Qwen3.8 27B (VLM) need on A100 80GB SXM4?

27.8 GB for the weights at FP8 (E4M3), plus 5.5 GB of KV cache for 8 concurrent requests at 8K context — 33.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Qwen3.8 27B (VLM)?

It works. Compare $0.3957 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0511, and the fastest is 1× Cerebras CS-3 (WSE-3) at 8423 tok/s.

Adjust the workload yourself

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

Open in calculator