Qwen3 8B Hardware Requirements
The 8GB-card staple: hybrid thinking/non-thinking modes and quality that made Llama-class small models look old. The Qwen3.5 small line (9B/4B/2B) is newer, but this remains the proven default.
VRAM needed (Q4, 8k context)
7.3 GB
Cheapest GPU that runs it: RTX 3060 (~$238 used)
Check Price on AmazonUpdated July 2026. Estimates — see methodology below.
VRAM by Quantization
Weights + KV cache at 8k context + 1.2GB system overhead. Q4_K_M is the community default — quality loss is negligible for most use.
| Quantization | Weights | KV cache (8k) | Total VRAM | Cheapest GPU that fits |
|---|---|---|---|---|
| Q4_K_M Recommended — near-lossless for most use, half the size of Q8 | 5.0 GB | 1.1 GB | 7.3 GB | RTX 3060 (~$238 used) |
| Q5_K_M Slightly higher quality than Q4 for ~18% more VRAM | 5.8 GB | 1.1 GB | 8.2 GB | RTX 3060 (~$238 used) |
| Q8_0 Effectively lossless — use if you have VRAM to spare | 8.7 GB | 1.1 GB | 11.0 GB | RTX 3060 (~$238 used) |
| FP16 Full precision — only for fine-tuning or maximum fidelity | 16.4 GB | 1.1 GB | 18.7 GB | RX 7900 XT (~$588 used) |
Longer context costs VRAM
KV cache grows linearly with context: 8k → 1.1 GB · 32k → 4.5 GB · 128k → 18.0 GB. If you plan to feed whole documents or codebases, size your GPU for the context you actually need, not just the weights.
Best GPUs for Qwen3 8B
The cheapest way to run Qwen3 8B well. Expect fast responses at ~36 tokens/sec.
The fastest single-GPU experience for Qwen3 8B. Expect instant-feeling responses at ~180 tokens/sec.
GPU Compatibility (Q4, 8k context)
Every GPU in our database, scored against Qwen3 8B. Speed is estimated decode rate — memory-bandwidth-bound, so VRAM and bandwidth matter more than shader count.
| GPU | VRAM | Verdict | Est. speed | Price | |
|---|---|---|---|---|---|
| RTX 3060 | 12 GB | Runs great | ~36 tok/sFast | ~$238 used | Check price |
| Arc B570 | 10 GB | Runs great | ~38 tok/sFast | from $225 | Check price |
| Arc B580 | 12 GB | Runs great | ~46 tok/sFast | from $250 | Check price |
| RX 6700 XT | 12 GB | Runs great | ~39 tok/sFast | ~$315 used | Check price |
| Arc A770 | 16 GB | Runs great | ~56 tok/sFast | from $300 | Check price |
| RX 7700 XT | 12 GB | Runs great | ~43 tok/sFast | ~$415 used | Check price |
| RX 6800 XT | 16 GB | Runs great | ~51 tok/sFast | ~$438 used | Check price |
| RTX 3080 | 10 GB | Runs great | ~76 tok/sInstant-feeling | ~$463 used | Check price |
| RX 7800 XT | 16 GB | Runs great | ~63 tok/sInstant-feeling | ~$488 used | Check price |
| RTX 4070 | 12 GB | Runs great | ~51 tok/sFast | ~$500 used | Check price |
| RTX 4070 SUPER | 12 GB | Runs great | ~51 tok/sFast | ~$563 used | Check price |
| RX 7900 XT | 20 GB | Runs great | ~80 tok/sInstant-feeling | ~$588 used | Check price |
| RTX 5060 Ti | 16 GB | Runs great | ~45 tok/sFast | from $550 | Check price |
| RX 9070 | 16 GB | Runs great | ~64 tok/sInstant-feeling | from $575 | Check price |
| RTX 5070 | 12 GB | Runs great | ~68 tok/sInstant-feeling | from $600 | Check price |
| RX 9070 XT | 16 GB | Runs great | ~64 tok/sInstant-feeling | from $600 | Check price |
| RTX 4070 Ti SUPER | 16 GB | Runs great | ~68 tok/sInstant-feeling | ~$750 used | Check price |
| RX 7900 XTX | 24 GB | Runs great | ~97 tok/sInstant-feeling | ~$838 used | Check price |
| RTX 4080 SUPER | 16 GB | Runs great | ~74 tok/sInstant-feeling | ~$900 used | Check price |
| RTX 5070 Ti | 16 GB | Runs great | ~90 tok/sInstant-feeling | from $900 | Check price |
| RTX 3090 | 24 GB | Runs great | ~94 tok/sInstant-feeling | ~$1,150 used | Check price |
| RTX 5080 | 16 GB | Runs great | ~97 tok/sInstant-feeling | from $1,250 | Check price |
| RTX 4090 | 24 GB | Runs great | ~101 tok/sInstant-feeling | ~$2,375 used | Check price |
| RTX 5090 | 32 GB | Runs great | ~180 tok/sInstant-feeling | from $2,800 | Check price |
| RTX 4060 | 8 GB | Tight fit | ~27 tok/sFast | ~$275 used | Check price |
| RX 7600 | 8 GB | Tight fit | ~29 tok/sFast | from $250 | Check price |
| RTX 4060 Ti | 8 GB | Tight fit | ~29 tok/sFast | ~$338 used | Check price |
| RTX 3070 | 8 GB | Tight fit | ~45 tok/sFast | ~$338 used | Check price |
| RTX 5060 | 8 GB | Tight fit | ~45 tok/sFast | from $325 | Check price |
Run it in one command
With Ollama installed, this pulls the default quant and starts chatting:
Frequently Asked Questions
How much VRAM do I need to run Qwen3 8B?+
At the recommended Q4_K_M quantization with 8k context, Qwen3 8B needs roughly 7.3GB of VRAM (5.0GB weights + KV cache + overhead). Q8 needs about 11.0GB and full FP16 about 18.7GB.
What is the cheapest GPU that runs Qwen3 8B?+
NVIDIA GeForce RTX 3060 (12GB, ~$238 used) is the cheapest current GPU in our database that runs Qwen3 8B fully in VRAM at an estimated ~36 tokens/sec.
Can I run Qwen3 8B on an RTX 3060?+
Yes — the RTX 3060 12GB runs Qwen3 8B at Q4 comfortably.
Can I run Qwen3 8B on a Mac?+
Yes, if the Mac has enough unified memory: budget roughly 7.3GB of RAM for the Q4 version (plus what macOS itself uses). Apple Silicon runs GGUF models well via Ollama or LM Studio.
Can I run Qwen3 8B on CPU only?+
Technically yes with enough system RAM, but a dense 8.2B model on CPU is slow — usually a few tokens/sec at best. Fine for testing, painful for daily use.
Is Qwen3 8B free for commercial use?+
Yes. Qwen3 8B is released under the Apache 2.0, which permits commercial use.
Related Models
How we calculate these numbers
VRAM = model weights (parameters × bits per weight ÷ 8) + KV cache (architecture-specific bytes per token × context length) + ~1.2GB runtime overhead. Speed estimates assume decode is memory-bandwidth-bound at ~50% utilization (lower for MoE models, which pay routing overhead), matching typical llama.cpp performance on consumer cards; real results vary with runtime, drivers, and settings. Quant sizes reflect GGUF K-quants, which keep some layers at higher precision. Figures are estimates for planning, not guarantees — when in doubt, buy more VRAM than you need today. Prices shown are launch MSRP; mid-2026 street prices often run well above MSRP due to the ongoing memory shortage, and used 24GB cards are holding their value unusually well.