❌Won't fitfp16 · 4K ctx · batch 1
Does H200 run Llama 3.3 70B? No at fp16.
At fp16 precision, Llama 3.3 70B (70 B parameters) needs roughly 142.9 GB of VRAM. H200 only has 141 GB, short by 1.9 GB. INT4 quantization brings it down to 40.9 GB — that fits.
101%
VRAM
Won't fit
Estimated VRAM (fp16)
142.9 GB
of 141 GB on H200· 1.9 GB short
Tok/sec
29
TFTT
1692ms
Power
488W
Rent on GPUniq marketplace
H200
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Quantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on H200? |
|---|---|---|---|
| FP16 (full precision) | 142.9 GB | 101% | ❌Won't fit |
| INT8 (8-bit) | 74.9 GB | 53% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 40.9 GB | 29% | ✅Runs easily |
Frequently asked
- Can H200 run Llama 3.3 70B?
- Not at fp16 — Llama 3.3 70B needs about 142.9 GB while H200 has 141 GB. It fits at INT4 quantization (40.9 GB) with some accuracy trade-off.
- How much VRAM does Llama 3.3 70B use?
- About 142.9 GB at fp16, 40.9 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Llama 3.3 70B on H200?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For H200, enable Flash Attention 2.
- Where can I rent a H200?
- GPUniq aggregates live H200 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Llama 3.3 70B
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