✅ RUNS NATIVE FP16 & ALL QUANTIZATIONS
Qwen3-8B on 4x NVIDIA A100 80GB (320GB) (320GB VRAM)
In 4-bit Q4_K_M, Qwen3-8B uses only 6.9 GB of your 320 GB VRAM (2% utilization) at ~254.3 tokens/sec. In full 16-bit precision, it requires 18.67 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
6.9 GB / 320 GB
🟦 Weights (5.12 GB)
🟪 KV Cache (1.13 GB)
🟨 Activation Buffer (0.66 GB)
Total Required VRAM6.9 GB
Available Hardware VRAM320 GB (4x NVIDIA A100 80GB (320GB))
VRAM Headroom / Overflow313.10 GB
Estimated Generation Speed~254.3 tokens/sec
Architecture & AttentionDense-GQA (36L, 8 KV heads)
🚀 Ready-to-Run Launch Command
ollama run qwen3:8b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen3-8B-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 6.9 GB+ VRAM to run Qwen3-8B?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 18.67 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Qwen3-8B on 4x NVIDIA A100 80GB (320GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 4x NVIDIA A100 80GB (320GB) (320GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 16.89 GB | 1.13 GB | 18.67 GB | Yes (6%) | ~77.1 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 8.97 GB | 1.13 GB | 10.76 GB | Yes (3%) | ~145.1 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 8.97 GB | 1.13 GB | 10.76 GB | Yes (3%) | ~145.1 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 6.93 GB | 1.13 GB | 8.71 GB | Yes (3%) | ~188 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 6.01 GB | 1.13 GB | 7.79 GB | Yes (2%) | ~216.8 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 5.12 GB | 1.13 GB | 6.9 GB | Yes (2%) | ~254.3 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 4.49 GB | 1.13 GB | 6.27 GB | Yes (2%) | ~280 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 4.13 GB | 1.13 GB | 5.91 GB | Yes (2%) | ~280 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
Frequently Asked Questions (Qwen3-8B + 4x NVIDIA A100 80GB (320GB))
Can 4x NVIDIA A100 80GB (320GB) (320GB) run Qwen3-8B locally?
Yes — 4x NVIDIA A100 80GB (320GB) (320GB) easily runs Qwen3-8B even in full FP16. In 4-bit Q4_K_M, Qwen3-8B uses only 6.9 GB of your 320 GB VRAM (2% utilization) at ~254.3 tokens/sec. In full 16-bit precision, it requires 18.67 GB.
How is KV Cache calculated for Qwen3-8B?
Qwen3-8B uses Dense-GQA with 36 transformer layers, 32 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.13 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.56 GB.
Compare Qwen3-8B on Other GPUs
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