Yi-1.5-34B-Chat on 2x NVIDIA RTX 3090 (48GB) (48GB VRAM)
Full FP16 (73.4 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 24.02 GB (21.48 GB weights + 1.88 GB KV cache at 8K context), delivering ~27.8 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
24.02 GB / 48 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:
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|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
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All Quantization Formats: Yi-1.5-34B-Chat on 2x NVIDIA RTX 3090 (48GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 2x NVIDIA RTX 3090 (48GB) (48GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 70.86 GB | 1.88 GB | 73.4 GB | No (Over by 25.4GB) | ~1.2 tok/s | 2x 2x NVIDIA RTX 3090 (48GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 37.65 GB | 1.88 GB | 40.18 GB | Yes (84%) | ~15.9 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 37.65 GB | 1.88 GB | 40.18 GB | Yes (84%) | ~15.9 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 29.05 GB | 1.88 GB | 31.59 GB | Yes (66%) | ~20.6 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 25.2 GB | 1.88 GB | 27.74 GB | Yes (58%) | ~23.7 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 21.48 GB | 1.88 GB | 24.02 GB | Yes (50%) | ~27.8 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 18.82 GB | 1.88 GB | 21.36 GB | Yes (45%) | ~31.8 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 17.32 GB | 1.88 GB | 19.86 GB | Yes (41%) | ~34.5 tok/s | 1x 2x NVIDIA RTX 3090 (48GB) |
Frequently Asked Questions (Yi-1.5-34B-Chat + 2x NVIDIA RTX 3090 (48GB))
Can 2x NVIDIA RTX 3090 (48GB) (48GB) run Yi-1.5-34B-Chat locally?
Yes — 2x NVIDIA RTX 3090 (48GB) (48GB) can run Yi-1.5-34B-Chat using FP8 (W8A8 / E4M3). Full FP16 (73.4 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 24.02 GB (21.48 GB weights + 1.88 GB KV cache at 8K context), delivering ~27.8 tokens/sec.
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.