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

GLM-5.3 (MoE) on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

GLM-5.3 (MoE) at AWQ 4-bit needs 414.3 GB for its weights and 420.2 GB in total at 8K context with 8 concurrent users. That means it needs 8× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 8×
420.2 GB needed / 640 GB available
Decode speed
739.9
tok/s per stream
Aggregate
71027
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0375
8× 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 GB420.2 GB0.0000.000——✗
2× A100160 GB420.2 GB0.0000.000——✗
4× A100320 GB420.2 GB0.0000.000——✗
8× A100640 GB420.2 GB739.971027282.3 ms$0.0375✓
Concurrency vs context length
On 8× 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?
4K61315.70 GB✓ fits
8K30631.41 GB✓ fits
33K76125.63 GB✓ fits
66K38251.26 GB✓ fits
131K19502.51 GB✓ fits
Other GPUs that run GLM-5.3 (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
H100 SXM5 80GB8×118970$0.0467
B200 SXM 192GB4×71027$0.0939
H200 SXM5 141GB4×42616$0.1043
Frequently asked questions

Can GLM-5.3 (MoE) run on A100 80GB SXM4?

Yes, but it needs 8× A100 80GB SXM4. At AWQ 4-bit the total footprint is 420.2 GB (414.3 GB of weights plus 5.9 GB of KV cache at 8K × 8 concurrent users), against 640 GB of available VRAM across 8 GPUs.

How fast is GLM-5.3 (MoE) on A100 80GB SXM4?

739.9 tokens/sec per stream and 71027 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 282.3 ms, and inter-token latency is 1.4 ms.

What does GLM-5.3 (MoE) on A100 80GB SXM4 cost per million tokens?

$0.0375 per 1M output tokens at 8× 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 GLM-5.3 (MoE) need on A100 80GB SXM4?

414.3 GB for the weights at AWQ 4-bit, plus 5.9 GB of KV cache for 8 concurrent requests at 8K context — 420.2 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for GLM-5.3 (MoE)?

It works. Compare $0.0375 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× A100 80GB SXM4 at $0.0375, and the fastest is 8× H100 SXM5 80GB at 118970 tok/s.

Adjust the workload yourself

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

Open in calculator