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

Qwen 3 235B-A22B (MoE) on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Qwen 3 235B-A22B (MoE) at AWQ 4-bit needs 129.3 GB for its weights and 141.9 GB in total at 8K context with 8 concurrent users. That means it needs 2× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
141.9 GB needed / 160 GB available
Decode speed
160.7
tok/s per stream
Aggregate
3857
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1728
2× 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 GB141.9 GB0.0000.000——✗
2× A100160 GB141.9 GB160.738571.19 s$0.1728✓
4× A100320 GB141.9 GB321.515430610.8 ms$0.0864✓
8× A100640 GB141.9 GB642.961719320.4 ms$0.0432✓
Concurrency vs context length
On 2× 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?
4K38807.5 MB✓ fits
8K191.58 GB✓ fits
33K46.31 GB✓ fits
66K212.62 GB✓ fits
131K125.23 GB✓ fits
Other GPUs that run Qwen 3 235B-A22B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB8×55300$0.0201
L40S 48GB4×6666$0.1167
H100 SXM5 80GB2×6461$0.2150
AMD MI300X 192GB1×3007$0.2310
H200 SXM5 141GB2×9258$0.2400
AMD MI325X 288GB1×3404$0.2856
Frequently asked questions

Can Qwen 3 235B-A22B (MoE) run on A100 80GB SXM4?

Yes, but it needs 2× A100 80GB SXM4. At AWQ 4-bit the total footprint is 141.9 GB (129.3 GB of weights plus 12.6 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is Qwen 3 235B-A22B (MoE) on A100 80GB SXM4?

160.7 tokens/sec per stream and 3857 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.19 s, and inter-token latency is 6.2 ms.

What does Qwen 3 235B-A22B (MoE) on A100 80GB SXM4 cost per million tokens?

$0.1728 per 1M output tokens at 2× 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 Qwen 3 235B-A22B (MoE) need on A100 80GB SXM4?

129.3 GB for the weights at AWQ 4-bit, plus 12.6 GB of KV cache for 8 concurrent requests at 8K context — 141.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Qwen 3 235B-A22B (MoE)?

It works. Compare $0.1728 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 5090 32GB at $0.0201, and the fastest is 4× Cerebras CS-3 (WSE-3) at 154298 tok/s.

Adjust the workload yourself

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

Open in calculator