Qwen3 4B Instruct 2507 on H20 141GB HBM3e (141GB) — VRAM, KV Cache & Throughput
Qwen3 4B Instruct 2507 (4.02B total, 4.02B active, Dense GQA) uses 2.33 GiB of 4-bit weights (Q4_K_M), requiring 4.02 GiB total at 8K context and 7.39 GiB at 32K on H20 141GB HBM3e (~380 tok/s decode).
Fits with Safe Headroom
Measured GGUF shard size
HF config.json ↗
Required GPU Memory
4.02 GiB
4.32 GB (decimal)
Usable Hardware Memory
133.9 GiB
141 GB (physical)
Safe Headroom
+129.88 GiB
3% utilization
Est. Decode Throughput
~380 tok/s
4.02B active params
Est. Prefill Throughput
~19144 tok/s
4K Prompt TTFT: ~214 ms
Est. Cost / 1M Tokens
$0.04
Dense GQA · Apache-2.0
Recommended Hardware for Current Configuration
Best Single GPU
RTX 4060 Ti 16GB (16GB)
~$430
Best Value / Used GPU
RTX 3060 12GB (12GB)
~$280
Apple Unified Memory
Ryzen AI Max+ 395 128GB
~$1,999
Cloud From
Vast.ai · $0.06 / hr
1x RTX 3060 12GB
Production Launch Commands
llama.cpp (GGUF)
llama-server -hf unsloth/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M \ --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve Qwen/Qwen3-4B-Instruct-2507 \ --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~214ms · ~380 tok/s)
[Qwen3 4B Instruct 2507 · 4.02 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...
Cloud GPU Comparison & Rent-vs-Buy TCO
Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.
Purchase break-even
~18.5 months
Est. local power: $3.51 / mo
| Provider | Recommended Instance | Total VRAM | Hourly Rate | Monthly at Selected Hours | Billing Mode | Deploy |
|---|---|---|---|---|---|---|
| RunPod | 1x RTX 3090 (Community Cloud) | 24 GB | $0.22 / hr | $26.75 / mo | Per-second | Deploy GPU ↗ |
| Vast.ai | 1x RTX 3060 12GB (Verified Host) | 12 GB | $0.06 / hr | $7.42 / mo | Spot / On-demand | Deploy GPU ↗ |
| Vultr ($300 Free Credit*) | 1x L40S 48GB (Dedicated Instance · $300 Credit*) | 48 GB | $1.67 / hr | $203.19 / mo | Hourly | Deploy GPU ↗ |
| Vultr Cloud GPU | 1x L40S 48GB (Dedicated Instance) | 48 GB | $1.67 / hr | $203.19 / mo | Hourly | Deploy GPU ↗ |
| DigitalOcean | 1x RTX 6000 Ada (Dedicated Instance) | 48 GB | $1.57 / hr | $190.91 / mo | Per-second | Deploy GPU ↗ |
Rates reflect public listings on the verification date and fluctuate with regional inventory.
Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Qwen3 4B Instruct 2507
QLoRA (4b)
Fits with Safe Headroom
4.56 GiB
Model Weights: 2.33 GiB · Gradients + Optimizer: 1.2 GiB · Act: 1.03 GiB
Recommended GPU: RTX 4060 Ti 16GB (16GB)
LoRA (16b)
Fits with Safe Headroom
10.65 GiB
Model Weights: 7.49 GiB · Gradients + Optimizer: 1.8 GiB · Act: 1.36 GiB
Recommended GPU: RTX 4060 Ti 16GB (16GB)
Full BF16
Fits with Safe Headroom
33.11 GiB
Model Weights: 7.49 GiB · Gradients + Optimizer: 23.22 GiB · Act: 2.4 GiB
Recommended GPU: A40 48GB (48GB)
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
H20 141GB HBM3e (141GB physical, 133.9 GiB usable) runs 4-bit (Q4_K_M) Qwen3 4B Instruct 2507 at 8K context using 4.02 GiB with 129.88 GiB of safe headroom (~380 tok/s single-stream decode).
KV Cache & Runtime Tuning
Qwen3 4B Instruct 2507 uses Dense GQA (4.02B total, 4.02B active per token). Moving from 8K to 32K context expands FP16 KV cache from 1.13 GiB to 4.5 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 2.25 GiB at 32K (reducing total VRAM to 5.14 GiB).
VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)
Quantization Precision & Memory Matrix — Qwen3 4B Instruct 2507 @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (H20 141GB HBM3e) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) | 7.49 GiB | 1.13 GiB | 9.18 GiB (9.86 GB) | ~293.1 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Official checkpoint size |
| FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) | 3.99 GiB | 1.13 GiB | 5.68 GiB (6.1 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| GGUF Q6_K (6-bit — High Precision) (Q6_K) | 3.08 GiB | 1.13 GiB | 4.77 GiB (5.12 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) | 2.69 GiB | 1.13 GiB | 4.38 GiB (4.7 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) | 2.33 GiB | 1.13 GiB | 4.02 GiB (4.32 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) | 2.11 GiB | 1.13 GiB | 3.8 GiB (4.08 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) | 1.93 GiB | 1.13 GiB | 3.62 GiB (3.89 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
| GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) | 1.58 GiB | 1.13 GiB | 3.27 GiB (3.51 GB) | ~380 tok/s | Fits with Safe Headroom | RTX 4060 Ti 16GB (16GB) | Measured GGUF shard size |
Technical Specifications & FAQ
Does Qwen3 4B Instruct 2507 fit on H20 141GB HBM3e (141GB)?
In 4-bit (Q4_K_M) at 8,192 context, Qwen3 4B Instruct 2507 requires 4.02 GiB total GPU memory (2.33 GiB weights + 1.13 GiB KV cache + 0.56 GiB runtime buffers) against 133.9 GiB usable on H20 141GB HBM3e, delivering ~380 tok/s decode.
How much VRAM does Qwen3 4B Instruct 2507 need at 32K context?
At 32,768 context tokens, Qwen3 4B Instruct 2507 (Dense GQA) allocates 4.5 GiB for KV cache and state, bringing total 4-bit VRAM to 7.39 GiB (7.93 GB decimal).
Evaluate This Model on Other GPUs
RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)
Evaluate Other Models on This Hardware
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