Qwen3 8B — Hardware Requirements & GPU Compatibility
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Qwen3 8B is a 8B-parameter open language model from litert-community in the Qwen 3 family. At Q4_K_M it needs about 5.28 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~124 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~27 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 8B
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
vllm serve litert-community/Qwen3-8B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~112 tok/s
- generation speed
- Cost per 1M output tokens
- $0.30
- per 1M output tokens
How Much VRAM Does Qwen3 8B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.7 GB | 9.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~175 tok/s | |
| 4.3 GB | 9.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~153 tok/s | |
| 5.3 GB | 10.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~124 tok/s | |
| 6.3 GB | 11.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~105 tok/s | |
| 7.3 GB | 12.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~90 tok/s | |
| 8.8 GB | 14.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~75 tok/s | |
| 17.6 GB | 23.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~37 tok/s |
Selected Q4_K_M
4-bit medium quantization7.9GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.2 GB free · ~124 tok/s
≥50 tok/s needs ≥406 GB/s · RTX 5090 ~221, RTX 3090 Ti ~124
Which Hardware Can Run Qwen3 8B?
Q4_K_M · 5.3 GBQwen3 8B (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Qwen3 8B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.1 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~94Simulate output on RTX 3080RTX 2080 Ti~76Simulate output on RTX 2080 TiRTX 3070 Ti~75Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~91Simulate output on RX 7900 XT32–40 GB
fits · 24.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~221Simulate output on RTX 5090M5 Max36 GB~61Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 8B
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| 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 |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
Specifications
- Publisher
- litert-community
- Family
- Qwen 3
- Parameters
- 8B
- Release Date
- 2026-04-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 8B need?
Qwen3 8B requires 5.3 GB of VRAM at Q4_K_M, or 17.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 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.
VRAM usage by quantization
What's the best quantization for Qwen3 8B?
For Qwen3 8B, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 8B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3 8B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 8B locally?
Yes — Qwen3 8B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 8B?
At Q4_K_M, Qwen3 8B can reach ~909 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~124 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 ÷ 5.28 × 0.65 = ~985 tok/s
Estimated speed at Q4_K_M (5.3 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?
At Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 16.00 GB. The smallest option (Q2_K) is 3.40 GB.
Which GPUs can run Qwen3 8B?
52 consumer GPUs can run Qwen3 8B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3 8B?
151 devices with unified memory can run Qwen3 8B at Q4_K_M (5.3 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.