✅ RUNS NATIVE FP16 & ALL QUANTS
Qwen3-4B on NVIDIA RTX 3090 (24GB VRAM)
In 4-bit GGUF Q4_K_M, Qwen3-4B consumes 3.61 GB (2.42 GB weights + 0.7 GB KV cache at 8K context), achieving ~265 tokens/sec decode and ~4592 tokens/sec prefill.
🎛️ Precision VRAM, Context & Offload Tuner
📊 Live GPU Memory & Performance Telemetry
3.61 GB / 24 GB
🟦 Core + Emb Weights: 2.42 GB
🟪 KV Cache: 0.7 GB
🟨 Engine + CUDA Buffer: 0.49 GB
Required GPU VRAM3.61 GB
Required System RAM (CPU Offload)0.00 GB
Available Hardware VRAM24 GB (1x NVIDIA RTX 3090)
VRAM Headroom / Deficit20.39 GB
Est. Decode Speed (Generation)~265 tokens/sec
Est. Prefill Speed (Prompt Processing)~4592 tokens/sec
Est. Time-to-First-Token (4K Prompt)~892 ms
Transformer TopologyDense-GQA (36L, 8/32 KV/Q heads)
🚀 Ready-to-Copy Launch Command
ollama run qwen3:4b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen3-4B-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU & RENT-VS-BUY TCO CALCULATOR
Need More VRAM? Rent Pre-Configured Cloud GPUs Instantly (Qwen3-4B: 3.61 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 9.12 GB
Hardware Break-Even vs Buying: ~32 months (vs $750 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | Deploy on RunPod → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Rent Spot GPU → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | Claim $300 Credit → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | Get $200 Credit → |
All Quantization Formats Breakdown: Qwen3-4B × NVIDIA RTX 3090
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 3090 (24GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 7.49 GB | 0.7 GB | 8.68 GB | Yes (36%) | ~80 / 4592 tok/s | 1x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 3.88 GB | 0.7 GB | 5.07 GB | Yes (21%) | ~155.2 / 4592 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 3.98 GB | 0.7 GB | 5.17 GB | Yes (22%) | ~150.6 / 4592 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 3.07 GB | 0.7 GB | 4.26 GB | Yes (18%) | ~195.1 / 4592 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 2.74 GB | 0.7 GB | 3.93 GB | Yes (16%) | ~225 / 4592 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 2.42 GB | 0.7 GB | 3.61 GB | Yes (15%) | ~265 / 4592 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 2.1 GB | 0.7 GB | 3.29 GB | Yes (14%) | ~301.2 / 4592 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 1.93 GB | 0.7 GB | 3.12 GB | Yes (13%) | ~327.4 / 4592 tok/s | 1x |
Test This Model on Other GPUs
Qwen3-4B on NVIDIA RTX 5090 (32GB)Qwen3-4B on NVIDIA RTX 4090 (24GB)Qwen3-4B on NVIDIA RTX 5080 (16GB)Qwen3-4B on NVIDIA RTX 4080 Super (16GB)Qwen3-4B on NVIDIA RTX 4070 Ti Super (16GB)Qwen3-4B on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen3-4B on NVIDIA RTX 3060 12GB (12GB)Qwen3-4B on NVIDIA RTX 6000 Ada (48GB)Qwen3-4B on NVIDIA RTX A6000 (48GB)Qwen3-4B on NVIDIA L40S 48GB (48GB)
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