Qwen1.5 110B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen1.5-110B-Chat is Alibaba Cloud's 111-billion-parameter dense chat model and the largest member of the Qwen1.5 series, which the team describes as a beta version of Qwen2 spanning nine sizes from 0.5B up to this 110B model plus a 14B mixture-of-experts variant. Unlike most other Qwen1.5 sizes, the 110B model uses grouped-query attention, and it was post-trained with supervised fine-tuning and direct preference optimization for improved chat alignment. At 111 billion parameters it needs a multi-GPU workstation to run even when quantized. Context length is 32,768 tokens, one of Qwen1.5's standout features since every size in the family shares a stable 32K window. It is released under the Tongyi Qianwen License Agreement, Alibaba's custom license permitting research and commercial use. It was published in April 2024, a beta step between the original Qwen and Qwen2.
1.9K downloads
130 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 111.2B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-04-25
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Qwen1.5 110B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 48.2 GB | 58.3 GB | 47.26 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 49.6 GB | 59.7 GB | 48.65 GB | 3-bit small quantization |
| Q3_K_Mest. | 3.90 | 55.2 GB | 65.3 GB | 54.21 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 67.7 GB | 77.8 GB | 66.73 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 80.2 GB | 90.3 GB | 79.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 92.7 GB | 102.8 GB | 91.75 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 112.2 GB | 122.3 GB | 111.21 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen1.5 110B Chat?
Q4_K_M · 67.7 GBQwen1.5 110B Chat (Q4_K_M) requires 67.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 89+ GB is recommended. Using the full 33K context window can add up to 10.1 GB, bringing total usage to 77.8 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen1.5 110B Chat?
Q4_K_M · 67.7 GB19 devices with unified memory can run Qwen1.5 110B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Qwen1.5 110B Chat
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen1.5 110B Chat need?
Qwen1.5 110B Chat requires 67.7 GB of VRAM at Q4_K_M, or 223.4 GB at BF16. Full 33K context adds up to 10.1 GB (77.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 111.2B × 4.8 bits ÷ 8 = 66.7 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M67.7 GBQ4_K_M + full context77.8 GB- Can NVIDIA GeForce RTX 5090 run Qwen1.5 110B Chat?
No — Qwen1.5 110B Chat requires at least 46.9 GB at IQ3_XS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen1.5 110B Chat?
For Qwen1.5 110B Chat, Q4_K_M (67.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (80.2 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 46.9 GB.
VRAM requirement by quantization
IQ3_XS46.9 GBQ3_K_S49.6 GBQ3_K_M55.2 GBQ4_K_M ★67.7 GBQ6_K92.7 GBBF16223.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen1.5 110B Chat on a Mac?
Qwen1.5 110B Chat requires at least 46.9 GB at IQ3_XS, 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 Qwen1.5 110B Chat locally?
Yes — Qwen1.5 110B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 67.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen1.5 110B Chat?
At Q4_K_M, Qwen1.5 110B Chat can reach ~71 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 67.7 × 0.65 = ~77 tok/s
Estimated speed at Q4_K_M (67.7 GB)
~77 tok/s~77 tok/s~71 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen1.5 110B Chat?
At Q4_K_M, the download is about 66.73 GB. The full-precision BF16 version is 222.42 GB. The smallest option (IQ3_XS) is 45.87 GB.
- Which GPUs can run Qwen1.5 110B Chat?
No single consumer GPU has enough VRAM to run Qwen1.5 110B Chat at Q4_K_M (67.7 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen1.5 110B Chat?
19 devices with unified memory can run Qwen1.5 110B Chat at Q4_K_M (67.7 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.