✅Runs easilyfp16 · 4K ctx · batch 1
Run Llama 3.1 8B on H200
At fp16 precision, Llama 3.1 8B (8.0 B parameters) needs roughly 19.5 GB of VRAM. H200 has 141 GB, leaving 121.5 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 175 tokens/sec on a single card.
14%
VRAM
Runs easily
Estimated VRAM (fp16)
19.5 GB
of 141 GB on H200· 121.5 GB free
Tok/sec
175
TFTT
382ms
Power
267W
Rent on GPUniq marketplace
H200
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
16.00 GBKV Cache
0.50 GBActivations
2.00 GBFramework Overhead
1.04 GBQuantization 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) | 19.5 GB | 14% | ✅Runs easily |
| INT8 (8-bit) | 10.5 GB | 7% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 6.0 GB | 4% | ✅Runs easily |
Frequently asked
- Can H200 run Llama 3.1 8B?
- Yes. Llama 3.1 8B needs ≈ 19.5 GB VRAM at fp16 and H200 provides 141 GB. Expected throughput is 175 tokens/sec per GPU.
- How much VRAM does Llama 3.1 8B use?
- About 19.5 GB at fp16, 6.0 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 3.1 8B 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 3.1 8B
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