Qwen1.5 32B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen1.5-32B-Chat is Alibaba's instruction-tuned, 32.5-billion-parameter chat model from the Qwen1.5 series, a beta release of the Qwen2 architecture that sits between the 14B and 72B dense models in the lineup. Qwen1.5 improved on the original Qwen with stable 32K context support across all model sizes, broader multilingual coverage, and no need for custom trust_remote_code, and this 32B checkpoint additionally uses grouped-query attention, unlike the smaller Qwen1.5 sizes, for faster inference. It was aligned on top of the pretrained base with supervised fine-tuning and direct preference optimization. At 32.5 billion parameters it needs a high-end consumer GPU, or a multi-GPU setup once quantized, to run comfortably. Context length is 32,768 tokens. It is released under Alibaba's Tongyi Qianwen license, a custom license that is free for most commercial and research use but requires a separate license from Alibaba once a deployment exceeds 100 million monthly active users. It was published in April 2024, part of Alibaba's second LLM generation, later followed by Qwen2 and Qwen2.5.
Runs well on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~32 tok/s · 8K)Macs from 24 GB (Q4_K_M · ~11 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 32B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Qwen1.5-32B-Chat-GGUF:Q4_K_MQ4_K_M · needs ~24.1 GB · 16K context
Download: Qwen1.5-32B-Chat.Q4_K_M.gguf19.70 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 5090 (32 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~57 tok/s
- generation speed
- Cost per 1M output tokens
- $2.41
- per 1M output tokens
How Much VRAM Does Qwen1.5 32B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.3 GB | 22.3 GB | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Fits: 32K context · ~46 tok/s | |
| 14.7 GB | 22.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~45 tok/s | |
| 15.1 GB | 23.1 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~44 tok/s | |
| 16.7 GB | 24.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~39 tok/s | |
| 20.3 GB | 28.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~32 tok/s | |
| 24 GB | 32.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~27 tok/s | |
| 27.7 GB | 35.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 31.4 GB | |
| 33.4 GB | 41.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 37.1 GB |
Selected Q4_K_M
4-bit medium quantization24.1GBat 16K context
- Weights
- 19.5 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · fits up to 8K context · ~32 tok/s
≥50 tok/s needs ≥1.56 TB/s · RTX 5090 ~57
Which Hardware Can Run Qwen1.5 32B Chat?
Q4_K_M · 20.3 GBQwen1.5 32B Chat (Q4_K_M) requires 20.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 28.4 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Qwen1.5 32B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit IQ3_M (19.2 GB)
1 GPU
RX 7900 XT~31Simulate output on RX 7900 XT32–40 GB
fits · 7.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~57Simulate output on RTX 5090M5 Max36 GB~16Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen1.5 32B Chat?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Apr 2024Independent
Independent30.7%
#82/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1204.1
#145/188 open
Alternatives to Qwen1.5 32B Chat
Qwen family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nanbeige4.3 33BNanbeige | — | 23.7 GB | ~32 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
| Agents A1InternScience | — | 21.5 GB | ~171 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 32.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-04-03
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen1.5 32B Chat need?
Qwen1.5 32B Chat requires 20.3 GB of VRAM at Q4_K_M, or 65.9 GB at BF16. Full 33K context adds up to 8.1 GB (28.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.5B × 4.8 bits ÷ 8 = 19.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen1.5 32B Chat?
Yes, at Q5_K_M (24 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen1.5 32B Chat?
For Qwen1.5 32B Chat, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (23.2 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 14.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 32B Chat on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Qwen1.5 32B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 32B Chat locally?
Yes — Qwen1.5 32B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 32B Chat?
At Q4_K_M, Qwen1.5 32B Chat can reach ~236 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. 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 ÷ 20.3 × 0.65 = ~256 tok/s
Estimated speed at Q4_K_M (20.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen1.5 32B Chat?
At Q4_K_M, the download is about 19.51 GB. The full-precision BF16 version is 65.02 GB. The smallest option (IQ3_XS) is 13.41 GB.
Which GPUs can run Qwen1.5 32B Chat?
7 consumer GPUs can run Qwen1.5 32B Chat at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Qwen1.5 32B Chat?
122 devices with unified memory can run Qwen1.5 32B Chat at Q4_K_M (20.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.