Yi 1.5 34B Chat GGUF — Hardware Requirements & GPU Compatibility
Chat
79 downloads
4 likesSpecifications
- Publisher
- second-state
- Family
- Yi 1.5
- Parameters
- 34B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Yi 1.5 34B Chat GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.3 GB | 15.8 GB | 14.45 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.7 GB | 16.2 GB | 14.88 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 17.4 GB | 17.9 GB | 16.57 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.8 GB | 18.3 GB | 17.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 21.2 GB | 21.7 GB | 20.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 25.0 GB | 25.5 GB | 24.23 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 28.9 GB | 29.4 GB | 28.05 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 34.8 GB | 35.3 GB | 34.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Yi 1.5 34B Chat GGUF?
Q4_K_M · 21.2 GBYi 1.5 34B Chat GGUF (Q4_K_M) requires 21.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 4K context window can add up to 0.5 GB, bringing total usage to 21.7 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Yi 1.5 34B Chat GGUF?
Q4_K_M · 21.2 GB21 devices with unified memory can run Yi 1.5 34B Chat GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Yi 1.5 34B Chat GGUF need?
Yi 1.5 34B Chat GGUF requires 21.2 GB of VRAM at Q4_K_M, or 34.8 GB at Q8_0. Full 4K context adds up to 0.5 GB (21.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 34B × 4.8 bits ÷ 8 = 20.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M21.2 GBQ4_K_M + full context21.7 GB- Can NVIDIA GeForce RTX 4090 run Yi 1.5 34B Chat GGUF?
Yes, at Q5_0 (22.1 GB) or lower. Higher quantizations like Q5_K_S (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Yi 1.5 34B Chat GGUF?
For Yi 1.5 34B Chat GGUF, Q4_K_M (21.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (22.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.3 GB.
VRAM requirement by quantization
Q2_K15.3 GB~75%Q4_017.8 GB~85%Q4_K_M ★21.2 GB~89%Q5_022.1 GB~90%Q5_K_S24.2 GB~92%Q8_034.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Yi 1.5 34B Chat GGUF on a Mac?
Yi 1.5 34B Chat GGUF requires at least 15.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Yi 1.5 34B Chat GGUF locally?
Yes — Yi 1.5 34B Chat GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 21.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Yi 1.5 34B Chat GGUF?
At Q4_K_M, Yi 1.5 34B Chat GGUF can reach ~138 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~31 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 21.2 × 0.55 = ~138 tok/s
Estimated speed at Q4_K_M (21.2 GB)
AMD Instinct MI300X~138 tok/sNVIDIA GeForce RTX 4090~31 tok/sNVIDIA H100 SXM~103 tok/sAMD Instinct MI250X~85 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Yi 1.5 34B Chat GGUF?
At Q4_K_M, the download is about 20.40 GB. The full-precision Q8_0 version is 34.00 GB. The smallest option (Q2_K) is 14.45 GB.
- Which GPUs can run Yi 1.5 34B Chat GGUF?
5 consumer GPUs can run Yi 1.5 34B Chat GGUF at Q4_K_M (21.2 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Yi 1.5 34B Chat GGUF?
21 devices with unified memory can run Yi 1.5 34B Chat GGUF at Q4_K_M (21.2 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.