Yi-1.5-34B-Chat on NVIDIA H100 80GB SXM (80GB VRAM)
In 4-bit Q4_K_M, Yi-1.5-34B-Chat uses only 24.02 GB of your 80 GB VRAM (30% utilization) at ~99.6 tokens/sec. In full 16-bit precision, it requires 73.4 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
24.02 GB / 80 GB🚀 Ready-to-Run Launch Command
ollama run yi:34b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf 01-ai/Yi-1.5-34B-Chat-GGUF -c 8192 -ngl 99 -fa
Need 24.02 GB+ VRAM to run Yi-1.5-34B-Chat?
Recommended cloud setup for 4-bit: L40S / RTX 6000 Ada (48GB) (or 73.4 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Yi-1.5-34B-Chat on NVIDIA H100 80GB SXM
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA H100 80GB SXM (80GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 70.86 GB | 1.88 GB | 73.4 GB | Tight (92%) | ~30.2 tok/s | 1x NVIDIA H100 80GB SXM |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 37.65 GB | 1.88 GB | 40.18 GB | Yes (50%) | ~56.8 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 37.65 GB | 1.88 GB | 40.18 GB | Yes (50%) | ~56.8 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q6_K (6-bit) | 6.56 bpw | 29.05 GB | 1.88 GB | 31.59 GB | Yes (39%) | ~73.6 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 25.2 GB | 1.88 GB | 27.74 GB | Yes (35%) | ~84.9 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 21.48 GB | 1.88 GB | 24.02 GB | Yes (30%) | ~99.6 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 18.82 GB | 1.88 GB | 21.36 GB | Yes (27%) | ~113.7 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 17.32 GB | 1.88 GB | 19.86 GB | Yes (25%) | ~123.5 tok/s | 1x NVIDIA H100 80GB SXM |
Frequently Asked Questions (Yi-1.5-34B-Chat + NVIDIA H100 80GB SXM)
Can NVIDIA H100 80GB SXM (80GB) run Yi-1.5-34B-Chat locally?
Yes — NVIDIA H100 80GB SXM (80GB) easily runs Yi-1.5-34B-Chat even in full FP16. In 4-bit Q4_K_M, Yi-1.5-34B-Chat uses only 24.02 GB of your 80 GB VRAM (30% utilization) at ~99.6 tokens/sec. In full 16-bit precision, it requires 73.4 GB.
How is KV Cache calculated for Yi-1.5-34B-Chat?
Yi-1.5-34B-Chat uses Dense-GQA with 60 transformer layers, 56 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.88 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.94 GB.