Qwen3 8B Base — Hardware Requirements & GPU Compatibility
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Qwen3 8B Base is an 8.2-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 series. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a starting point for custom downstream applications. It was trained on a large multilingual corpus with improved data quality and training methodology compared to the Qwen 2.5 generation. The model runs efficiently on consumer GPUs with 8GB or more of VRAM and serves as the foundation for the Qwen3 8B instruction-tuned variant and community fine-tunes. It is a strong choice for practitioners building specialized models through further training. Released under the Apache 2.0 license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~119 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 8B Base
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/Qwen3-8B-Base-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.4 GB free · 16K context
Download: Qwen3-8B-Base.Q4_K_M.gguf5.03 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 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 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~107 tok/s
- generation speed
- Cost per 1M output tokens
- $0.32
- per 1M output tokens
How Much VRAM Does Qwen3 8B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.1 GB | 8.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~161 tok/s | |
| 4.2 GB | 8.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~156 tok/s | |
| 4.6 GB | 9.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~143 tok/s | |
| 4.7 GB | 9.2 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~139 tok/s | |
| 5.5 GB | 10.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~119 tok/s | |
| 6.4 GB | 11 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~102 tok/s | |
| 7.4 GB | 11.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~89 tok/s | |
| 8.8 GB | 13.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~75 tok/s |
Selected Q4_K_M
4-bit medium quantization7.6GBat 16K context
- Weights
- 4.9 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.4 GB free · ~119 tok/s
≥50 tok/s needs ≥424 GB/s · RTX 5090 ~211, RTX 3090 Ti ~119
Which Hardware Can Run Qwen3 8B Base?
Q4_K_M · 5.5 GBQwen3 8B Base (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 4.5 GB, bringing total usage to 10.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Qwen3 8B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.4 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~90Simulate output on RTX 3080RTX 2080 Ti~73Simulate output on RTX 2080 TiRTX 3070 Ti~72Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~87Simulate output on RX 7900 XT32–40 GB
fits · 24.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~211Simulate output on RTX 5090M5 Max36 GB~58Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 8B Base
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-28
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 8B Base need?
Qwen3 8B Base requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 33K context adds up to 4.5 GB (10.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 5.2 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen3 8B Base?
For Qwen3 8B Base, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 8B Base on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3 8B Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 8B Base locally?
Yes — Qwen3 8B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 8B Base?
At Q4_K_M, Qwen3 8B Base can reach ~870 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~119 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 ÷ 5.52 × 0.65 = ~942 tok/s
Estimated speed at Q4_K_M (5.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 8B Base?
At Q4_K_M, the download is about 4.91 GB. The full-precision BF16 version is 16.38 GB. The smallest option (IQ2_XXS) is 2.25 GB.
Which GPUs can run Qwen3 8B Base?
52 consumer GPUs can run Qwen3 8B Base at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3 8B Base?
151 devices with unified memory can run Qwen3 8B Base at Q4_K_M (5.5 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.