GLM-5.3 hardware requirements
Needs at least 4 GPUs at FP8 (Instinct MI350X OAM). For 1,000 tokens/s at 32k context you need 16 x H100 SXM5 80GB at $28.64/hr or 16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition at $8.00/hr.
Bare minimum
- Tokens/s
- 514
- Per hour
- $24.64
- Per million tokens
- $13.32
- Memory used
- 68%
- Tokens/s
- 384
- Per hour
- $8.00
- Per million tokens
- $5.79
- Memory used
- 51%
- Tokens/s
- 3.8k
- Per hour
- $8.00
- Per million tokens
- $0.59
- Memory used
- 58%
Configurations
Every datacenter GPU in the catalog, sized for these settings. Change precision, context and concurrency and the whole page follows. Each point is one replica: the smallest tensor-parallel group of that GPU that holds the model.
4 x GB300 NVL72 GPU 288GB: 5.1k tokens/s per replica at $34.48/hr.8 x GB200 NVL72 GPU 186GB: 9.6k tokens/s per replica at $84.00/hr.4 x Instinct MI350X OAM: 5.1k tokens/s per replica at $24.64/hr.4 x B300 SXM 262GB: 5.1k tokens/s per replica at $29.60/hr.8 x B200 SXM 180GB: 9.2k tokens/s per replica at $32.72/hr.4 x Instinct MI325X OAM: 3.8k tokens/s per replica at $15.20/hr.8 x Instinct MI300X 192GB: 6.4k tokens/s per replica at $19.12/hr.8 x H200 SXM 141GB: 5.8k tokens/s per replica at $23.92/hr.16 x H100 SXM5 80GB: 7.1k tokens/s per replica at $28.64/hr.8 x H200 NVL 141GB: 5.8k tokens/s per replica at $30.32/hr.16 x H100 NVL 94GB: 8.3k tokens/s per replica at $51.04/hr.16 x H100 PCIe 80GB: 4.2k tokens/s per replica at $40.00/hr.16 x RTX PRO 6000 Blackwell Workstation Edition: 3.8k tokens/s per replica at $28.80/hr.16 x RTX PRO 6000 Blackwell Server Edition: 3.4k tokens/s per replica at $9.44/hr.16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition: 3.8k tokens/s per replica at $8.00/hr.16 x A100 PCIe 80GB: 4.1k tokens/s per replica at $21.60/hr.16 x A100 SXM4 80GB: 4.3k tokens/s per replica at $22.40/hr.
Priced configurations only; 14 more fit without a live price and appear in the table. Marker size grows with the tensor-parallel width. Roofline estimates.
| GPU | TP | Tokens/s | |
|---|---|---|---|
| 4 | ~15k | Specs | |
| 4 | 14k | Specs | |
| 4 | 5.1k | Specs | |
| 4 | 5.1k | Rent | |
| 8 | 9.6k | Rent | |
| 4 | 5.1k | Rent | |
| 4 | 5.1k | Rent | |
| 8 | 9.2k | Rent | |
| 8 | 9.6k | Specs | |
| 4 | 3.8k | Rent | |
| 8 | 6.4k | Rent | |
| 16 | ~3.4k | Specs | |
| 8 | 5.8k | Rent | |
H100 SXM5 80GB 2 nodes | 16 | 7.1k | Rent |
| 8 | ~3.9k | Specs | |
| 8 | ~5.8k | Rent | |
H100 NVL 94GB 2 nodes | 16 | ~8.3k | Rent |
H100 PCIe 80GB 2 nodes | 16 | 4.2k | Rent |
H800 PCIe 80GB 2 nodes | 16 | 4.2k | Specs |
| 16 | 3.8k | Rent | |
| 16 | ~3.4k | Rent | |
| 16 | 3.8k | Rent | |
| 8 | ~3.9k | Specs | |
| 8 | ~3.9k | Specs | |
A800 PCIe 80GB 2 nodes | 16 | ~4.1k | Specs |
A100 PCIe 80GB 2 nodes | 16 | ~4.1k | Rent |
A100 SXM4 80GB 2 nodes | 16 | ~4.3k | Rent |
RTX PRO 5000 Blackwell 72GB 2 nodes | 16 | ~2.8k | Specs |
Instinct MI210 PCIe 2 nodes | 16 | ~3.5k | Specs |
| 8 | 4.8k | Specs | |
H20 96GB 2 nodes | 16 | 8.5k | Specs |
AMD vs NVIDIA
At FP8 the NVIDIA pick is 16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition at about 3.8k tokens/s for $8.00/hr; the AMD pick is 8 x Instinct MI300X 192GB at about 6.4k tokens/s for $19.12/hr. Per rental dollar NVIDIA delivers 1.43x the tokens of AMD here.
NVIDIA: 475 tokens/s per dollar, 791 tokens/s per kW (16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition).AMD: 333 tokens/s per dollar, 1.1k tokens/s per kW (8 x Instinct MI300X 192GB).Intel: no live price, 820 tokens/s per kW (8 x Data Center GPU Max 1550 128GB).Other: no live price, 385 tokens/s per kW (16 x BR100).
Fleet what-if
Compare whole fleets at these settings: a thousand of one part against a hundred of another. Each fleet splits into replicas of its tensor-parallel width; GPUs left over sit idle.
| Fleet | Replicas | Tokens/s |
|---|---|---|
| A 1,000 x H200 SXM 141GB | 125 x TP8 | 721k |
| B 104 x GB200 NVL72 GPU 186GB | 13 x TP8 | 125k |
1,000 x H200 SXM 141GB: 721k tokens/s, $2,990/hr, 700 kW.104 x GB200 NVL72 GPU 186GB: 125k tokens/s, $1,092/hr, no power figure.
H200 SXM 141GB: 721k tokens/s at 1000 GPUs.GB200 NVL72 GPU 186GB: 1.20M tokens/s at 1000 GPUs.
Size GLM-5.3 for a tokens per second target
The calculator starts from your traffic instead of a fleet: set a target rate and read the replica count for every GPU.
Frequently asked questions
How much GPU memory does GLM-5.3 need?
The weights take 753 GB at the native FP8 precision (1,507 GB at BF16, 753 GB at FP8, 424 GB at INT4). Each concurrent stream adds 88 KB of KV cache per token: 0.4 GB at 4,096 tokens and 2.9 GB at 32,768 tokens.
What is the bare minimum to run GLM-5.3?
4 x Instinct MI350X OAM at FP8 with 4,096 tokens of context and one stream. The cheapest live rental for that footprint is 16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition at $8.00 per hour.
How many H100s do you need to run GLM-5.3?
16 H100s at FP8 for 4,096 tokens of context and one stream. For 32,768 tokens and 32 concurrent streams the tensor-parallel group is 16 H100s, and 1,000 tokens per second takes 16 H100s across 1 replicas.
How much does GLM-5.3 cost per million tokens on an H100?
About $1.12 per million output tokens at FP8, 32,768 tokens of context and 32 streams, using the cheapest live on-demand price of $1.79 per GPU-hour. The cheapest part per token is 16 x RTX PRO 6000 Blackwell Max-Q Workstation Edition at $0.59.
What precision does GLM-5.3 ship in?
The published checkpoint is FP8. FP8 halves the BF16 footprint with the accuracy the lab validated; INT4 is a further community quantisation.
All figures are roofline estimates from the model's config.json and each GPU's published memory, bandwidth and tensor-core peak, with fixed efficiency factors. Prices are the cheapest live on-demand listing per GPU when the site has one. They are a planning floor; a tuned serving stack can do better. Same geometry as GLM-5.2, shipped in FP8. Active parameters computed from the expert geometry, net of the multi-token-prediction layer.