NVIDIA141 GB VRAM4,800 GB/s$4/hr

GLM-5.3 (MoE) on H200 SXM5 141GB: 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 4× H200 SXM5 141GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 4×
420.2 GB needed / 564 GB available
Decode speed
887.8
tok/s per stream
Aggregate
42616
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1043
4× H200 SXM5 141GB
What changes as you add H200 SXM5 141GBs
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× H200141 GB420.2 GB0.0000.000——✗
2× H200282 GB420.2 GB0.0000.000——✗
4× H200564 GB420.2 GB887.842616189.3 ms$0.1043✓
8× H2001128 GB420.2 GB1776170465109.7 ms$0.0521✓
Concurrency vs context length
On 4× H200 SXM5 141GB 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?
4K40615.70 GB✓ fits
8K20331.41 GB✓ fits
33K50125.63 GB✓ fits
66K25251.26 GB✓ fits
131K12502.51 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
A100 80GB SXM48×71027$0.0375
H100 SXM5 80GB8×118970$0.0467
B200 SXM 192GB4×71027$0.0939
Frequently asked questions

Can GLM-5.3 (MoE) run on H200 SXM5 141GB?

Yes, but it needs 4× H200 SXM5 141GB. 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 564 GB of available VRAM across 4 GPUs.

How fast is GLM-5.3 (MoE) on H200 SXM5 141GB?

887.8 tokens/sec per stream and 42616 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 189.3 ms, and inter-token latency is 1.1 ms.

What does GLM-5.3 (MoE) on H200 SXM5 141GB cost per million tokens?

$0.1043 per 1M output tokens at 4× H200 SXM5 141GB ($4.00/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 H200 SXM5 141GB?

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 H200 SXM5 141GB the right choice for GLM-5.3 (MoE)?

It works. Compare $0.1043 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