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

Llama 3.3 70B on H200 SXM5 141GB: Does It Fit, How Fast, What It Costs

Llama 3.3 70B at FP8 (E4M3) needs 70.6 GB for its weights and 92.1 GB in total at 8K context with 8 concurrent users. That means it fits on a single H200 SXM5 141GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
92.1 GB needed / 141 GB available
Decode speed
66.29
tok/s per stream
Aggregate
795.5
tok/s @ 8 concurrent
Cost / 1M tokens
$1.40
1× 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 GB92.1 GB66.29795.52.38 s$1.40✓
2× H200282 GB92.1 GB112.727051.21 s$0.8216✓
4× H200564 GB92.1 GB225.410818618.6 ms$0.4108✓
8× H2001128 GB92.1 GB450.843273324.3 ms$0.2054✓
Concurrency vs context length
On 1× H200 SXM5 141GB 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?
4K521.34 GB✓ fits
8K262.68 GB✓ fits
33K610.74 GB✓ fits
66K321.47 GB✓ fits
131K142.95 GB✓ fits
Other GPUs that run Llama 3.3 70B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×4039$0.1376
A100 80GB SXM42×1127$0.5916
H100 SXM5 80GB2×1888$0.7358
AMD MI300X 192GB1×878.3$0.7906
L40S 48GB2×486.8$0.7988
AMD MI325X 288GB1×994.3$0.9778
Frequently asked questions

Can Llama 3.3 70B run on H200 SXM5 141GB?

Yes. Llama 3.3 70B at FP8 (E4M3) needs 92.1 GB including the KV cache, and H200 SXM5 141GB has 141 GB — so it fits on a single GPU.

How fast is Llama 3.3 70B on H200 SXM5 141GB?

66.29 tokens/sec per stream and 795.5 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.38 s, and inter-token latency is 15.1 ms.

What does Llama 3.3 70B on H200 SXM5 141GB cost per million tokens?

$1.40 per 1M output tokens at 1× 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 Llama 3.3 70B need on H200 SXM5 141GB?

70.6 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 92.1 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H200 SXM5 141GB the right choice for Llama 3.3 70B?

It works. Compare $1.40 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1031, and the fastest is 4× Cerebras CS-3 (WSE-3) at 45076 tok/s.

Adjust the workload yourself

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

Open in calculator